feat: update datalist validate

Merge branch 'main' into feat/cli-skill-sync
Merge pull request #144 from modelstudioai/feat/add-version-tag
2026-09-14 19:49:23 +08:00 · 2026-08-11 13:33:31 +08:00 · 2026-08-11 10:47:45 +08:00 · 2026-08-07 18:02:08 +08:00 · 2026-08-07 17:42:31 +08:00 · 2026-08-07 17:26:21 +08:00
71 changed files with 2227 additions and 1503 deletions
@@ -0,0 +1,27 @@
+# Poke the FC publish-skills flow after skills/ changes land.
+# The FC side reconciles this repo's skills/ directory against OSS
+# (bailian-wiki/skills/) using the repo HEAD snapshot as the only
+# source of truth — the request itself carries no content. Both the
+# repo and branch params are validated against FC-side whitelists
+# (PUBLISH_REPOS / PUBLISH_BRANCHES).
+#
+# feat/cli-skill-sync is temporary for end-to-end testing; remove it
+# (here and from the FC PUBLISH_BRANCHES whitelist) once the sync
+# link is verified on main.
+name: Publish skills to OSS
+
+on:
+  push:
+    branches:
+      - main
+      - feat/cli-skill-sync
+    paths:
+      - "skills/**"
+
+jobs:
+  poke:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Trigger FC publish-skills
+        run: |
+          curl -sf -X POST "${{ vars.FC_TRIGGER_URL }}/publish-skills?repo=modelstudioai/cli&branch=${{ github.ref_name }}"
@@ -13,8 +13,9 @@

 ---

-_Chat with Qwen, generate images & videos, understand images, call agents,_
-_manage memory, search the web — all from your terminal._
+_Chat with Qwen, generate and edit images and videos, understand images, synthesize_
+_and recognize speech, call apps, manage memory, retrieve knowledge, search the web —_
+_every AI capability, one command away._

 _Built for AI Agents. Every command works as a structured tool call._

@@ -22,28 +23,16 @@ _Built for AI Agents. Every command works as a structured tool call._

 ## Features

-Equip your AI Agent out-of-the-box with these capabilities, composable across complex tasks:
+- **Model generation** — Full-modality generation across text, image, video, and speech, with editing and reference-based generation
+- **Asset understanding** — Parse and ask questions about images, documents, audio, and long videos
+- **App orchestration** — Call Managed Agents, agents, and workflows published on Aliyun Model Studio, wired to knowledge bases, memory, web search, and MCP tools
+- **Training & deployment** — Validate and upload datasets, fine-tune models, deploy dedicated models as endpoints
+- **Account operations** — Login, UI-based configuration, model marketplace, usage and quota, rate-limit increases, team seat management
+- **Plan onboarding** — Connect subscription plans such as Token Plan to the CLI and common coding agents in one step

- **Text chat** — Qwen3.8-max: major gains in agentic coding, frontend coding, and vibe coding
- **Multimodal (Omni)** — Full omni-modal support across text + image + audio + video
- **Image generation & editing** — Qwen-Image 3.0: pro text rendering, photorealism, strong semantic adherence, multi-image composition
- **Video generation & editing** — happyhorse-1.1 series: text-/image-/reference-to-video and natural-language video editing (up to 9-image reference)
- **Speech synthesis & recognition** — CosyVoice streaming TTS, voice cloning from 5–20s samples; FunAudio-ASR covers 30 languages including 7 Chinese dialects and 20+ Mandarin accents
- **Image & video understanding** — Qwen-VL: long-form video analysis, chart/document parsing, visual reasoning, multilingual OCR
- **Coding agent setup** — Configure Claude Code, Qwen Code, OpenCode, OpenClaw, Hermes Agent, or Codex to use DashScope with `bl config agent`
+> **Note:** App orchestration, training & deployment, account operations, and plan onboarding are currently available only to China site (aliyun.com) account holders and are not yet supported for international / global site accounts.

-> **Note:** The features below are currently available only to China site (aliyun.com) account holders and are not yet supported for international / global site accounts.
-
- **Knowledge base & memory** — Multimodal RAG retrieval and cross-session memory for personalized, coherent dialogue
- **App calls** — Invoke agents and workflows already published on Aliyun Model Studio
- **MCP integration** — Orchestrate Bailian MCP servers: list services, inspect tools, and invoke any tool directly from the terminal
- **Web search** — Real-time internet retrieval for up-to-date, accurate answers
- **Model recommendation** — Describe your scenario and get best-fit model suggestions; supports scoped search, model comparison, and alternative discovery
- **Fine-tuning & deployment** — Upload datasets, create text/audio/image fine-tune jobs (`finetune text|audio|image create`; text covers SFT/LoRA/DPO/CPT), probe job status non-blockingly (`finetune watch`), query per-model training capability (`finetune capability`), and deploy trained models as endpoints (`deploy text|audio|image create`)
- **Console capabilities** — Browse the model marketplace (`model list`) and Bailian apps (`app list`), review a unified usage view (`usage summary`), check free-tier quota (`usage free`), view model usage statistics (`usage stats`), manage workspaces (`workspace list`), and manage rate limits (`quota list/request/check/history`)
- **Local file auto-upload** — Every URL parameter accepts a local path; uploaded to free temp storage with 48-hour validity
-
-## Showcase: One-Sentence Cinematic Video
+## Showcase 1: A Cinematic Short Film from One Sentence

 <p align="center">
  <a href="https://cloud.video.taobao.com/vod/dS2F4huqbw5Nfe5L3wwb3grz2q2DNYD3retq8dU-iHo.mp4">
@@ -56,129 +45,77 @@ Equip your AI Agent out-of-the-box with these capabilities, composable across co
 A complete **2-minute, 16:9 cinematic short film** — produced end-to-end from a single natural-language sentence, with **zero manual editing**. This showcase demonstrates how an AI Agent can compose a multi-step creative pipeline by orchestrating three primitives:

 - **[Qwen Code](https://github.com/QwenLM/qwen-code)** — the agentic coding model that interprets the user's intent and drives the workflow
- **[Aliyun Model Studio CLI](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)** — invokes **HappyHorse 1.1**, Aliyun Model Studio's text-/image-/reference-to-video generation model
+- **[Aliyun Model Studio CLI](https://github.com/modelstudioai/cli/)** — invokes **HappyHorse 1.1**, Aliyun Model Studio's text-/image-/reference-to-video generation model
 - **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** — handles scene decomposition, storyboarding, shot continuity, and final stitching

 ### The single prompt

 > _"Generate a roughly 2-minute video in Japanese cinematic style — a sweet, innocent first-love story about a high-school girl. The plot should be heart-fluttering enough to make viewers want to fall in love. Aspect ratio: 16:9."_
->
-> _(Original: "帮我生成一段日系影视风格，高中女生的青涩初恋故事，剧情高甜，让人看了想谈恋爱，2分钟左右的视频，尺寸是16:9")_

-### How it works
+## Showcase 2: A Short-Film Director Managed Agent from One Sentence

-1. **Qwen Code** parses the request, plans the narrative beats, and decides which tools to call.
-2. The **spark-video Skill** breaks the story into shots, writes per-shot prompts, and enforces visual continuity (characters, lighting, palette, lens language).
-3. **`bl video generate`** dispatches each shot to **HappyHorse 1.1** in parallel.
-4. The skill stitches all clips back together into a single 16:9 / ~2-min deliverable.
+<p align="center">
+  <a href="https://cloud.video.taobao.com/vod/2v0GYLbJSQb2saj4iopTJDW3iRIHsintYlK-wTKbhqE.mp4">
+    <img src="https://img.alicdn.com/imgextra/i4/6000000001674/O1CN01xhzixhxltbH3LxWu_!!6000000001674-0-tbvideo.jpg" alt="Click to play the demo video" width="720" />
+  </a>
+</p>

-No timeline scrubbing. No frame-by-frame editing. Just one sentence → one video.
+<p align="center"><i>👆 Click the cover to play the full demo</i></p>
+
+One sentence builds a reusable cloud-side short-film director for storyboarding, storyboard image generation, and video creation:
+
+- **[Qwen Code](https://github.com/QwenLM/qwen-code)** — understands the requirement and generates the agent configuration
+- **[Aliyun Model Studio CLI](https://github.com/modelstudioai/cli/)** — validates the configuration, previews the changes, and completes the deployment
+- **[Managed Agent](https://bailian.console.aliyun.com/cn-beijing/?tab=managed-agents#/managed-agents/quick-start)** — runs the director role along with its skills and tools in the cloud
+
+### The single prompt
+
+> _"Build me a Managed Agent app that can produce short films — a director expert that generates videos and can also design the matching storyboards."_

 ## Installation

+**Agent install (recommended)**
+
+Send the following to your Agent — it will detect your environment, then install and verify the CLI for you:
+
+```text
+Please read https://bailian.aliyun.com/cli/install.md and install the Aliyun Model Studio CLI for me
+```
+
+**Manual install (npm)**
+
 ```bash
-# Recommended — no Node required
-curl -fsSL https://bailian.aliyun.com/cli/install.sh | bash
-
-# Windows (PowerShell)
-irm https://bailian.aliyun.com/cli/install.ps1 | iex
-
-# Node users / developers (Node.js >= 18.17)
 npm install -g bailian-cli
-
-# Agent skills
 npx skills add modelstudioai/cli --all -g
 ```

-> Binary install does not require Node.js. `npm install -g` remains fully supported.
+> Requires Node.js >= 18.17.

 ## Quick Start

-```bash
-# Authenticate, recommended
-bl auth login --console
+Once installed, just describe your task to your AI Agent — no need to assemble commands by hand.

-# Or authenticate with an API key
-bl auth login --api-key sk-xxxxx
-
-# Or use Token Plan (Base URL built in; the key is tested during login)
-bl auth login --config token-plan --api-key sk-sp-xxxxx
-
-# Configure a coding agent to use DashScope
-bl config agent --agent codex --base-url https://dashscope.aliyuncs.com/compatible-mode/v1 --api-key sk-xxxxx --model qwen3-coder-plus
-
-# Chat with Qwen
-bl text chat --message "What is DashScope?"
-
-# Multimodal chat (text + image + audio + video)
-bl omni --message "Describe this image" --image ./photo.jpg
-
-# Generate an image
-bl image generate --prompt "A cat in a spacesuit" --out-dir ./images/
-
-# Generate a video from local image
-bl video generate --image ./cat.png --prompt "Make the cat move" --download cat.mp4
-
-# Model recommendation — find the best model for your use case
-bl advisor recommend --message "I need a visual-understanding chatbot"
-
-# Compare specific models
-bl advisor recommend --message "qwen-max vs deepseek-v3 for code generation"
-
-# Browser login (required for console capability commands)
-bl auth login --console
-
-# Fine-tune & deploy — a one-shot train-to-serve workflow
-bl dataset upload --file ./train.jsonl                 # Upload a .jsonl dataset (validated first)
-bl finetune text create --model qwen3-8b --datasets ./train.jsonl --training-type sft-lora  # Local paths auto-upload
-bl finetune watch --job-id ft-xxx --output json       # Non-blocking probe (running/succeeded return 0; failed/canceled report an error)
-bl finetune capability --model qwen3-8b               # Which training types a model supports
-bl deploy text create --model qwen3-8b --name my-svc --plan mu  # Deploy the trained model as an endpoint
-
-# Browse models / apps / free-tier quota / usage statistics / workspaces
-bl model list                                        # Browse model families and pricing
-bl app list
-bl usage summary                                     # Unified view: free-tier quota + recent usage overview
-bl usage free                                         # Free-tier quota across models (add --model/--expiring/--sort)
-bl usage stats --workspace-id <id>                    # Model usage statistics (add --model for per-model)
-bl workspace list                                     # List all workspaces
-
-# Rate limit management (list / check / request / history)
-bl quota list                                         # View RPM/TPM limits (add --model to filter)
-bl quota check                                        # Current usage vs rate limits (add --model/--period)
-bl quota request --model qwen3.6-plus --tpm 6000000   # Request a temporary TPM increase
-bl quota history                                      # View quota-change history
-
-# Token Plan team management (requires AK/SK, see auth below)
-bl token-plan list-seats                                # View subscription seat details
-bl token-plan add-member --account-name dev --org-id org_xxx
-bl token-plan assign-seats --workspace-id ws_xxx --seat-type standard --account-id acc_xxx
-bl token-plan create-key --account-id acc_xxx --workspace-id ws_xxx
-```
+| Scenario                 | What to say to your Agent                                                         |
+| ------------------------ | --------------------------------------------------------------------------------- |
+| Managed Agent            | "Create a Managed Agent that can generate short-film storyboards and videos."     |
+| Image & video generation | "Generate an image of a cat in a spacesuit on Mars, then turn it into a video."   |
+| Usage & quota            | "Show my recent model usage, free-tier quota, and rate limits."                   |
+| Model selection          | "Recommend a model for image understanding and customer support."                 |
+| About Bailian CLI        | "Tell me what Bailian CLI can do for me, and suggest how to use it for my needs." |

 > More examples and scenarios: [Aliyun Model Studio CLI Site](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)

 ## Authentication

-### DashScope API Key
+### API Key

 Required for most commands. Get your key from the [DashScope Console](https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key).

 ```bash
-# Option 1: Environment variable
-export DASHSCOPE_API_KEY=sk-xxxxx
-
-# Option 2: Login command (persisted to ~/.bailian/config.json)
 bl auth login --api-key sk-xxxxx
-
-# Option 3: Per-command flag
-bl text chat --api-key sk-xxxxx --message "Hello"
 ```

-### Token Plan API Key
-
-Get or copy the API key from the [Token Plan subscription overview](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview).
-The CLI has the default Token Plan Base URL built in. Login tests the key first, then saves and activates the `token-plan` config only when validation succeeds.
+Get or copy your Token Plan API key from the [Token Plan subscription overview](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview).

 ```bash
 bl auth login --config token-plan --api-key sk-sp-xxxxx
@@ -186,26 +123,20 @@ bl auth login --config token-plan --api-key sk-sp-xxxxx

 ### Console Login (OAuth)

-Required for console capability commands (`model list`, `app list`, `usage summary/free/stats`, `workspace list`, `quota list/request/check/history`). Opens the Bailian console in your browser to sign in.
+Required for console capability commands (model list, app list, MCP list, workspace, usage queries, rate-limit increases, direct console calls). Opens the Bailian console in your browser to sign in.

 ```bash
 bl auth login --console
 ```

-### Alibaba Cloud OpenAPI AK/SK (Token Plan only)
+### Alibaba Cloud OpenAPI AK/SK

-Required for the `token-plan` command group. Get your AccessKey from [RAM Console](https://ram.console.aliyun.com/manage/ak).
+Token Plan seat and member management requires an Alibaba Cloud AccessKey. Get yours from the [RAM Console](https://ram.console.aliyun.com/manage/ak).

 > Recommended: create a RAM sub-account with minimum privileges instead of using the root account's AK/SK.

 ```bash
-# Option 1: Login command (persisted to ~/.bailian/config.json)
 bl auth login --open-api --access-key-id LTAI5t... --access-key-secret ...
-
-# Option 2: Environment variables
-export ALIBABA_CLOUD_ACCESS_KEY_ID=LTAI5t...
-export ALIBABA_CLOUD_ACCESS_KEY_SECRET=...
-export BAILIAN_WORKSPACE_ID=ws-...
 ```

 ## Configuration
@@ -214,18 +145,31 @@ export BAILIAN_WORKSPACE_ID=ws-...
 # View current config
 bl config show

-# Set defaults
-bl config set --key base_url --value https://dashscope-us.aliyuncs.com
-bl config set --key default_text_model --value qwen-turbo
-bl config set --key timeout --value 600
+# List all config profiles
+bl config list

-# Self-update to latest or a specific version
-bl update
-bl update --to 0.1.14
+# Switch config profile
+bl config use --name token-plan
 ```

 Config file location: `~/.bailian/config.json`

+## Update
+
+```bash
+bl update
+```
+
+Upgrades the CLI to the latest version and refreshes the installed Agent Skills. Release notes for every version live in [CHANGELOG.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.md).
+
+## Contributing
+
+Bug reports, feature requests, and PRs are welcome. See [CONTRIBUTING.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.md) for developer setup, repo layout, and the workflow for adding or changing commands.
+
+Scan the QR code to join the Aliyun Model Studio CLI DingTalk user group for usage help, troubleshooting, bug reports, and tips from other users.
+
+<img src="https://img.alicdn.com/imgextra/i3/O1CN015uuhYGb6j0L12xJZ_!!6000000006304-2-tps-516-485.png" alt="Aliyun Model Studio CLI DingTalk user group" width="240" />
+
 ## Links

 | Resource                     | URL                                                                                       |
@@ -237,11 +181,3 @@ Config file location: `~/.bailian/config.json`
 | Get API Key                  | https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key |
 | Get Token Plan API Key       | https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview         |
 | Get AccessKey                | https://ram.console.aliyun.com/manage/ak                                                  |
-
-## Changelog
-
-Release notes for every version live in [CHANGELOG.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.md).
-
-## Contributing
-
-Bug reports, feature requests, and PRs are welcome. See [CONTRIBUTING.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.md) for developer setup, repo layout, and the workflow for adding or changing commands.
@@ -22,28 +22,16 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_

 ## 功能特性

-让您的 AI Agent 开箱即具备以下能力，并可在复杂任务中自动组合调用：
+- **模型生成** — 文本、图像、视频、语音全模态生成，支持编辑与参考生成
+- **素材理解** — 图像、文档、音频、长视频的解析与问答
+- **应用编排** — 调用百炼已发布的 Managed Agent、智能体和工作流，接入知识库、记忆库、联网搜索与 MCP 工具
+- **模型训推** — 数据集校验上传、模型精调、专属模型部署上线
+- **账号运维** — 授权登录、界面化配置、模型市场、用量与额度、限流提额、团队席位管理
+- **套餐接入** — 支持 Token Plan 等订阅计划一键接到 CLI 和常见 Coding Agent

- **文本对话** — Qwen3.8-max：Agentic coding、前端编程、Vibe coding 等能力显著增强
- **全模态对话** — 文本 + 图像 + 音频 + 视频全模态支持
- **图像生成与编辑** — Qwen-Image 3.0：专业文字渲染、真实质感、强语义遵循、多图合成
- **视频生成与编辑** — happyhorse-1.1 系列，支持文生 / 图生 / 参考生（最多 9 张图参考）/ 自然语言视频编辑
- **语音合成与识别** — CosyVoice 实时流式合成，5-20s 样本即可克隆；FunAudio-ASR 覆盖 30 种语种，含汉语七大方言与 20+ 口音官话
- **图像与视频理解** — Qwen-VL：长视频解析、复杂图表与文档识别、视觉推理、多语种 OCR
- **Coding Agent 配置** — 使用 `bl config agent` 将 Claude Code、Qwen Code、OpenCode、OpenClaw、Hermes Agent 或 Codex 配置为使用 DashScope
+> **注意：** 应用编排、模型训推、账号运维和套餐接入目前仅支持中国站（aliyun.com）账号，暂不支持国际站 / 全球站账号。

-> **注意：** 以下功能目前仅对中国站（aliyun.com）账号开放，国际站 / 全球站账号暂不支持。
-
- **知识库与记忆库** — 多模态 RAG 检索 + 跨会话记忆，提供个性化连贯对话体验
- **应用调用** — 调用已发布在阿里云百炼平台上的智能体与工作流应用
- **MCP 集成** — 统一调度百炼 MCP 服务：列出服务、查看工具、直接在终端调用任意工具
- **联网搜索** — 实时互联网信息检索，提升回答准确性及时效性
- **模型推荐** — 描述你的场景，智能推荐最适合的模型；支持限定范围搜索、模型对比和替代发现
- **微调与部署** — 上传数据集、创建文本/音频/图像调优任务（`finetune text|audio|image create`；文本涵盖 SFT/LoRA/DPO/CPT）、非阻塞探测任务状态（`finetune watch`）、按模型查训练能力（`finetune capability`），并把训练好的模型部署为推理服务（`deploy text|audio|image create`）
- **控制台能力** — 浏览模型市场（`model list`）和百炼应用（`app list`），查看统一用量视图（`usage summary`），查询模型免费额度（`usage free`），查看模型用量统计（`usage stats`），管理业务空间（`workspace list`），管理限流与提额（`quota list/request/check/history`）
- **本地文件自动上传** — 所有 URL 参数同时支持本地路径，免费临时存储 48 小时
-
-## 示例:一句话生成一部电影短片
+## 示例 1：一句话生成一部电影短片

 <p align="center">
  <a href="https://cloud.video.taobao.com/vod/dS2F4huqbw5Nfe5L3wwb3grz2q2DNYD3retq8dU-iHo.mp4">
@@ -53,130 +41,80 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_

 <p align="center"><i>👆 点击封面播放完整 2 分钟演示</i></p>

-一部完整的 **2 分钟、16:9 电影感短片** —— 由一句自然语言端到端生成,**全程零手动剪辑**。这个示例展示了 AI Agent 如何把三个基础能力编排成一条多步创作流水线:
+一部完整的 **2 分钟、16:9 电影感短片** —— 由一句自然语言端到端生成，**全程零手动剪辑**。这个示例展示了 AI Agent 如何把三个基础能力编排成一条多步创作流水线：

- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— Agentic coding 模型,解析用户意图、驱动整个工作流
- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.1**,百炼的文生/图生/参考生视频模型
+- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— Agentic coding 模型，解析用户意图、驱动整个工作流
+- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.1**，百炼的文生/图生/参考生视频模型
 - **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** —— 负责场景拆分、分镜设计、镜头连贯性和最终拼接

 ### 唯一的提示词

-> _"帮我生成一段日系影视风格,高中女生的青涩初恋故事,剧情高甜,让人看了想谈恋爱,2 分钟左右的视频,尺寸是 16:9"_
+> _“帮我生成一段日系影视风格，高中女生的青涩初恋故事，剧情高甜，让人看了想谈恋爱，2 分钟左右的视频，尺寸是 16:9。”_

-### 工作流程
+## 示例 2：一句话构建短片导演 Managed Agent

-1. **Qwen Code** 解析需求、规划叙事节奏,决定要调用哪些工具。
-2. **spark-video Skill** 把故事拆成镜头、为每个镜头写提示词,并保证视觉连贯性(角色、光线、色调、镜头语言)。
-3. **`bl video generate`** 把每个镜头并行下发给 **HappyHorse 1.1**。
-4. Skill 把所有片段拼成最终的 16:9 / 约 2 分钟成片。
+<p align="center">
+  <a href="https://cloud.video.taobao.com/vod/2v0GYLbJSQb2saj4iopTJDW3iRIHsintYlK-wTKbhqE.mp4">
+    <img src="https://img.alicdn.com/imgextra/i4/6000000001674/O1CN01xhzixhxltbH3LxWu_!!6000000001674-0-tbvideo.jpg" alt="点击播放演示视频" width="720" />
+  </a>
+</p>

-没有时间线拖拽,没有逐帧剪辑。一句话 → 一部短片。
+<p align="center"><i>👆 点击封面播放完整演示</i></p>
+
+一句话构建一个可复用的云端短片导演，用于分镜设计、分镜图生成和视频创作：
+
+- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— 理解需求并生成 Agent 配置
+- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 校验配置、预览变更并完成部署
+- **[Managed Agent](https://bailian.console.aliyun.com/cn-beijing/?tab=managed-agents#/managed-agents/quick-start)** —— 在云端运行导演角色及其 Skill 和工具
+
+### 唯一的提示词
+
+> _“帮我构建一个 managedagent 应用，能够实现短片拍摄，导演专家生成视频，然后也能进行设计对应的分镜图。”_

 ## 安装

+**Agent 安装（推荐）**
+
+把下面这句话发给你的 Agent，它会自行判断环境并完成安装与校验：
+
+```text
+请阅读：https://bailian.aliyun.com/cli/install.md 并按照说明为我安装阿里云百炼 CLI
+```
+
+**手动安装（npm）**
+
 ```bash
-# 推荐 — 无需本机 Node.js
-curl -fsSL https://bailian.aliyun.com/cli/install.sh | bash
-
-# Windows（PowerShell）
-irm https://bailian.aliyun.com/cli/install.ps1 | iex
-
-# Node 用户 / 开发者（需要 Node.js >= 18.17）
 npm install -g bailian-cli
-
-# Agent skills
 npx skills add modelstudioai/cli --all -g
 ```

-> 二进制安装不依赖 Node.js。`npm install -g` 长期保留。
+> 需要预先安装 Node.js >= 18.17。

 ## 快速开始

-```bash
-# 认证（推荐浏览器登录）
-bl auth login --console
+安装完成后，直接在 AI Agent 中描述你的任务，无需手动拼接命令。

-# 或使用 API key 认证
-bl auth login --api-key sk-xxxxx
-
-# 或使用 Token Plan（已内置 Base URL，登录时自动测试 Key）
-bl auth login --config token-plan --api-key sk-sp-xxxxx
-
-# 配置 Coding Agent 使用 DashScope
-bl config agent --agent codex --base-url https://dashscope.aliyuncs.com/compatible-mode/v1 --api-key sk-xxxxx --model qwen3-coder-plus
-
-# 和通义千问对话
-bl text chat --message "你好，介绍一下阿里云百炼平台"
-
-# 多模态对话（文本 + 图片 + 音频 + 视频）
-bl omni --message "描述这张图片" --image ./photo.jpg
-
-# 生成图片
-bl image generate --prompt "一只穿太空服的猫在火星上" --out-dir ./images/
-
-# 图生视频（本地文件自动上传）
-bl video generate --image ./cat.png --prompt "让画面中的猫动起来" --download cat.mp4
-
-# 模型推荐 — 根据场景推荐最适合的模型
-bl advisor recommend --message "我要做一个能理解图片的客服机器人"
-
-# 对比特定模型
-bl advisor recommend --message "qwen-max 和 deepseek-v3 哪个更适合做代码生成"
-
-# 浏览器登录（控制台能力相关命令需要）
-bl auth login --console
-
-# 微调与部署 — 从训练到服务的一站式流程
-bl dataset upload --file ./train.jsonl                 # 上传 .jsonl 数据集（先校验）
-bl finetune text create --model qwen3-8b --datasets ./train.jsonl --training-type sft-lora  # 本地路径自动上传
-bl finetune watch --job-id ft-xxx --output json       # 非阻塞探测（运行中/成功返回 0；失败/取消报错）
-bl finetune capability --model qwen3-8b               # 查询模型支持哪些训练方式
-bl deploy text create --model qwen3-8b --name my-svc --plan mu  # 把训练好的模型部署为推理服务
-
-# 浏览模型 / 应用 / 免费额度 / 用量统计 / 业务空间
-bl model list                                        # 浏览模型系列与价格信息
-bl app list
-bl usage summary                                     # 统一视图：免费额度 + 近期用量概览
-bl usage free                                         # 各模型免费额度（可加 --model/--expiring/--sort）
-bl usage stats --workspace-id <id>                    # 模型用量统计（加 --model 查单模型）
-bl workspace list                                     # 列出所有业务空间
-
-# 限流管理与提额（list / check / request / history）
-bl quota list                                         # 查看 RPM/TPM 限额（加 --model 过滤）
-bl quota check                                        # 当前用量 vs 限流阈值（加 --model/--period）
-bl quota request --model qwen3.6-plus --tpm 6000000   # 申请临时 TPM 提额
-bl quota history                                      # 查看提额历史记录
-
-# Token Plan 团队版管理（需 AK/SK，见下方认证说明）
-bl token-plan list-seats                                # 查看订阅席位明细
-bl token-plan add-member --account-name dev --org-id org_xxx
-bl token-plan assign-seats --workspace-id ws_xxx --seat-type standard --account-id acc_xxx
-bl token-plan create-key --account-id acc_xxx --workspace-id ws_xxx
-```
+| 场景             | 可以这样对 Agent 说                                                     |
+| ---------------- | ----------------------------------------------------------------------- |
+| Managed Agent    | “帮我创建一个能够生成短片分镜和视频的 Managed Agent。”                  |
+| 图片和视频生成   | “生成一张穿着太空服的猫站在火星上的图片，再把它制作成一段视频。”        |
+| 用量与额度       | “查看最近的模型用量、免费额度和限流情况。”                              |
+| 模型选型         | “推荐一个适合图片理解和智能客服的模型。”                                |
+| 了解 Bailian CLI | “介绍一下 Bailian CLI 能帮我完成哪些任务，并根据我的需求推荐使用方式。” |

 > 更多案例与使用场景：[阿里云百炼 CLI 官方主页](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)

 ## 认证方式

-### DashScope API Key
+### API Key

 大部分命令均需要 API Key。前往 [DashScope 控制台](https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key) 获取。

 ```bash
-# 方式一：环境变量
-export DASHSCOPE_API_KEY=sk-xxxxx
-
-# 方式二：登录命令（持久化到 ~/.bailian/config.json）
 bl auth login --api-key sk-xxxxx
-
-# 方式三：命令行参数
-bl text chat --api-key sk-xxxxx --message "你好"
 ```

-### Token Plan API Key
-
-前往 [Token Plan 订阅详情](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview) 获取或复制 API Key。
-CLI 已内置 Token Plan 的默认 Base URL；登录命令会先测试 Key，通过后才保存并激活 `token-plan` 配置。
+Token Plan 的 API Key 前往 [Token Plan 订阅详情](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview) 获取或复制。

 ```bash
 bl auth login --config token-plan --api-key sk-sp-xxxxx
@@ -184,26 +122,20 @@ bl auth login --config token-plan --api-key sk-sp-xxxxx

 ### 控制台登录（OAuth）

-控制台能力命令（`model list`、`app list`、`usage summary/free/stats`、`workspace list`、`quota list/request/check/history`）需要使用此登录方式。打开浏览器跳转百炼控制台完成登录。
+控制台能力命令（模型列表、应用列表、MCP 列表、工作空间、用量查询、限流提额、控制台直调）需要使用此登录方式。打开浏览器跳转百炼控制台完成登录。

 ```bash
 bl auth login --console
 ```

-### 阿里云 OpenAPI AK/SK（仅 Token Plan）
+### 阿里云 OpenAPI AK/SK

-`token-plan` 命令组需要阿里云 AccessKey。前往 [RAM 控制台](https://ram.console.aliyun.com/manage/ak) 获取。
+Token Plan 的席位与成员管理需要阿里云 AccessKey。前往 [RAM 控制台](https://ram.console.aliyun.com/manage/ak) 获取。

 > 建议：创建 RAM 子账号并授予最小权限，避免使用主账号 AK/SK。

 ```bash
-# 方式一：登录命令（持久化到 ~/.bailian/config.json）
 bl auth login --open-api --access-key-id LTAI5t... --access-key-secret ...
-
-# 方式二：环境变量
-export ALIBABA_CLOUD_ACCESS_KEY_ID=LTAI5t...
-export ALIBABA_CLOUD_ACCESS_KEY_SECRET=...
-export BAILIAN_WORKSPACE_ID=ws-...
 ```

 ## 配置
@@ -212,20 +144,31 @@ export BAILIAN_WORKSPACE_ID=ws-...
 # 查看当前配置
 bl config show

-# 设置默认值
-bl config set --key base_url --value https://dashscope-us.aliyuncs.com
-bl config set --key default_text_model --value qwen-turbo
-bl config set --key timeout --value 600
+# 查看全部配置档
+bl config list

-# 自更新到最新版本
-bl update
-
-# 安装指定版本
-bl update --to 0.1.14
+# 切换配置档
+bl config use --name token-plan
 ```

 配置文件位置：`~/.bailian/config.json`

+## 更新
+
+```bash
+bl update
+```
+
+升级 CLI 至最新版本，并同步更新已安装的 Agent Skills。每个版本的变更详情记录在 [CHANGELOG.zh.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.zh.md)。
+
+## 参与贡献
+
+欢迎提 Issue、Feature Request 和 PR。开发环境搭建、仓库结构、新增/修改命令的工作流请见 [CONTRIBUTING.zh.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.zh.md)。
+
+欢迎扫码加入阿里云百炼 CLI 钉钉用户交流群，获取使用答疑、问题排查、Bug 反馈和使用经验交流支持。
+
+<img src="https://img.alicdn.com/imgextra/i3/O1CN015uuhYGb6j0L12xJZ_!!6000000006304-2-tps-516-485.png" alt="阿里云百炼 CLI 钉钉用户交流群" width="240" />
+
 ## 相关链接

 | 资源                    | 地址                                                                                      |
@@ -237,11 +180,3 @@ bl update --to 0.1.14
 | 获取 API Key            | https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key |
 | 获取 Token Plan API Key | https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview         |
 | 获取 AccessKey          | https://ram.console.aliyun.com/manage/ak                                                  |
-
-## 更新日志
-
-每个版本的变更详情记录在 [CHANGELOG.zh.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.zh.md)。
-
-## 参与贡献
-
-欢迎提 Issue、Feature Request 和 PR。开发环境搭建、仓库结构、新增/修改命令的工作流请见 [CONTRIBUTING.zh.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.zh.md)。
@@ -25,7 +25,7 @@ defineCommand({ auth }) → runtime/authStage → ctx.client → command.run(ctx
 当前 command 鉴权域(`AuthRequirement`):

 - `apiKey` — DashScope / OpenAI-compatible 模型域,用 API key 与 model base URL
- `console` — Bailian Console Gateway,用 console access token + region/site/switchAgent/workspace
+- `console` — Bailian Console Gateway,用 console access token + region/site/switchAgent;`workspace_id` 是独立的 Settings 作用域,不属于 credential
 - `openapi` — 阿里云 OpenAPI 签名域,用 AccessKey ID/Secret 调用 Token Plan 等 OpenAPI
 - `none` — 本地命令、登录/配置类命令、无需 credential 的命令

@@ -35,7 +35,7 @@ defineCommand({ auth }) → runtime/authStage → ctx.client → command.run(ctx

 - `bl auth login --api-key ...` 只更新 `api_key` / `base_url`
 - `bl auth login --console` 只更新 `access_token` 以及回调携带的 console 作用域字段
- `bl auth login --open-api ...` 只更新 `access_key_id` / `access_key_secret`
+- `bl auth login --open-api ...` 更新 `access_key_id` / `access_key_secret`,同时会调用 OpenAPI 生成 CLI `access_token` 并一并写入;即一次 `--open-api` 登录同时产生 `openapi` 与 `console` 域凭证
 - `bl auth logout --console` 只清 `access_token`
 - `bl auth logout --open-api` 只清 `access_key_id` / `access_key_secret` / `security_token`
 - `bl auth logout` 清 `api_key` + `base_url` + `access_token` + `access_key_*`
@@ -78,6 +78,9 @@ defineCommand({ auth }) → runtime/authStage → ctx.client → command.run(ctx
  - 如新增鉴权域,扩展 `AuthRequirement`
  - 更新 `credentialFlagDefs()` 暴露该域可见的 flag
  - 必要时新增 `*_AUTH_FLAGS`
+  - `workspace_id` 是作用域字段而非 credential,不要把它放进 `ConsoleCredential`;读取方式按命令 `auth` 域区分:
+    - `auth: "console"` 命令通过 `CONSOLE_AUTH_FLAGS` 自动获得 `--workspace-id`,由 `buildSettings()` 解析到 `settings.workspaceId`,命令统一从 `settings.workspaceId` 读取
+    - `auth: "apiKey"`/`"openapi"`/`"none"` 命令如需 `--workspace-id`,必须自声明 flag;因它不会进入 credential/global flags,命令从 `ctx.flags.workspaceId` 读取(可回退到 `settings.workspaceId`)
 - [ ] `packages/core/src/auth/types.ts`:
  - 新增 credential 类型 / source / scope 字段
 - [ ] `packages/core/src/auth/resolver.ts`:
@@ -131,6 +134,8 @@ defineCommand({ auth }) → runtime/authStage → ctx.client → command.run(ctx

 ## 完成后自查

+本仓库同时存在 `bl`(packages/cli) 与 `kscli`(packages/kscli) 两个入口,二者共享 core/runtime 鉴权链路,但暴露的命令不同。如果改动会影响两个入口共用的命令或错误提示,再分别验证它们各自实际暴露的路径;不要假设 `kscli` 也有 `bl auth *` 命令。
+
 ```sh
 # 各种凭证组合
 unset DASHSCOPE_API_KEY ALIBABA_CLOUD_ACCESS_KEY_ID ALIBABA_CLOUD_ACCESS_KEY_SECRET
@@ -150,9 +155,11 @@ Console 登录/网关相关改动:

 ```sh
 pnpm -F bailian-cli exec tsx src/main.ts auth login --console
-pnpm -F bailian-cli exec tsx src/main.ts usage stats --dry-run --output json
+pnpm -F bailian-cli exec tsx src/main.ts usage stats --dry-run --output json --workspace-id ws-xxx
 ```

+注意:`usage stats --dry-run` 仍会先校验 workspace,必须传入 `--workspace-id`(或 `BAILIAN_WORKSPACE_ID` / config `workspace_id`)。
+
 ## 常见漏点

 - ✗ 加了新 token 来源但忘了改 resolver 优先级,实际不生效
@@ -13,8 +13,9 @@

 ---

-_Chat with Qwen, generate images & videos, understand images, call agents,_
-_manage memory, search the web — all from your terminal._
+_Chat with Qwen, generate and edit images and videos, understand images, synthesize_
+_and recognize speech, call apps, manage memory, retrieve knowledge, search the web —_
+_every AI capability, one command away._

 _Built for AI Agents. Every command works as a structured tool call._

@@ -22,28 +23,16 @@ _Built for AI Agents. Every command works as a structured tool call._

 ## Features

-Equip your AI Agent out-of-the-box with these capabilities, composable across complex tasks:
+- **Model generation** — Full-modality generation across text, image, video, and speech, with editing and reference-based generation
+- **Asset understanding** — Parse and ask questions about images, documents, audio, and long videos
+- **App orchestration** — Call Managed Agents, agents, and workflows published on Aliyun Model Studio, wired to knowledge bases, memory, web search, and MCP tools
+- **Training & deployment** — Validate and upload datasets, fine-tune models, deploy dedicated models as endpoints
+- **Account operations** — Login, UI-based configuration, model marketplace, usage and quota, rate-limit increases, team seat management
+- **Plan onboarding** — Connect subscription plans such as Token Plan to the CLI and common coding agents in one step

- **Text chat** — Qwen3.8-max: major gains in agentic coding, frontend coding, and vibe coding
- **Multimodal (Omni)** — Full omni-modal support across text + image + audio + video
- **Image generation & editing** — Qwen-Image 3.0: pro text rendering, photorealism, strong semantic adherence, multi-image composition
- **Video generation & editing** — happyhorse-1.1 series: text-/image-/reference-to-video and natural-language video editing (up to 9-image reference)
- **Speech synthesis & recognition** — CosyVoice streaming TTS, voice cloning from 5–20s samples; FunAudio-ASR covers 30 languages including 7 Chinese dialects and 20+ Mandarin accents
- **Image & video understanding** — Qwen-VL: long-form video analysis, chart/document parsing, visual reasoning, multilingual OCR
- **Coding agent setup** — Configure Claude Code, Qwen Code, OpenCode, OpenClaw, Hermes Agent, or Codex to use DashScope with `bl config agent`
+> **Note:** App orchestration, training & deployment, account operations, and plan onboarding are currently available only to China site (aliyun.com) account holders and are not yet supported for international / global site accounts.

-> **Note:** The features below are currently available only to China site (aliyun.com) account holders and are not yet supported for international / global site accounts.
-
- **Knowledge base & memory** — Multimodal RAG retrieval and cross-session memory for personalized, coherent dialogue
- **App calls** — Invoke agents and workflows already published on Aliyun Model Studio
- **MCP integration** — Orchestrate Bailian MCP servers: list services, inspect tools, and invoke any tool directly from the terminal
- **Web search** — Real-time internet retrieval for up-to-date, accurate answers
- **Model recommendation** — Describe your scenario and get best-fit model suggestions; supports scoped search, model comparison, and alternative discovery
- **Fine-tuning & deployment** — Upload datasets, create text/audio/image fine-tune jobs (`finetune text|audio|image create`; text covers SFT/LoRA/DPO/CPT), probe job status non-blockingly (`finetune watch`), query per-model training capability (`finetune capability`), and deploy trained models as endpoints (`deploy text|audio|image create`)
- **Console capabilities** — Browse the model marketplace (`model list`) and Bailian apps (`app list`), review a unified usage view (`usage summary`), check free-tier quota (`usage free`), view model usage statistics (`usage stats`), manage workspaces (`workspace list`), and manage rate limits (`quota list/request/check/history`)
- **Local file auto-upload** — Every URL parameter accepts a local path; uploaded to free temp storage with 48-hour validity
-
-## Showcase: One-Sentence Cinematic Video
+## Showcase 1: A Cinematic Short Film from One Sentence

 <p align="center">
  <a href="https://cloud.video.taobao.com/vod/dS2F4huqbw5Nfe5L3wwb3grz2q2DNYD3retq8dU-iHo.mp4">
@@ -56,129 +45,77 @@ Equip your AI Agent out-of-the-box with these capabilities, composable across co
 A complete **2-minute, 16:9 cinematic short film** — produced end-to-end from a single natural-language sentence, with **zero manual editing**. This showcase demonstrates how an AI Agent can compose a multi-step creative pipeline by orchestrating three primitives:

 - **[Qwen Code](https://github.com/QwenLM/qwen-code)** — the agentic coding model that interprets the user's intent and drives the workflow
- **[Aliyun Model Studio CLI](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)** — invokes **HappyHorse 1.1**, Aliyun Model Studio's text-/image-/reference-to-video generation model
+- **[Aliyun Model Studio CLI](https://github.com/modelstudioai/cli/)** — invokes **HappyHorse 1.1**, Aliyun Model Studio's text-/image-/reference-to-video generation model
 - **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** — handles scene decomposition, storyboarding, shot continuity, and final stitching

 ### The single prompt

 > _"Generate a roughly 2-minute video in Japanese cinematic style — a sweet, innocent first-love story about a high-school girl. The plot should be heart-fluttering enough to make viewers want to fall in love. Aspect ratio: 16:9."_
->
-> _(Original: "帮我生成一段日系影视风格，高中女生的青涩初恋故事，剧情高甜，让人看了想谈恋爱，2分钟左右的视频，尺寸是16:9")_

-### How it works
+## Showcase 2: A Short-Film Director Managed Agent from One Sentence

-1. **Qwen Code** parses the request, plans the narrative beats, and decides which tools to call.
-2. The **spark-video Skill** breaks the story into shots, writes per-shot prompts, and enforces visual continuity (characters, lighting, palette, lens language).
-3. **`bl video generate`** dispatches each shot to **HappyHorse 1.1** in parallel.
-4. The skill stitches all clips back together into a single 16:9 / ~2-min deliverable.
+<p align="center">
+  <a href="https://cloud.video.taobao.com/vod/2v0GYLbJSQb2saj4iopTJDW3iRIHsintYlK-wTKbhqE.mp4">
+    <img src="https://img.alicdn.com/imgextra/i4/6000000001674/O1CN01xhzixhxltbH3LxWu_!!6000000001674-0-tbvideo.jpg" alt="Click to play the demo video" width="720" />
+  </a>
+</p>

-No timeline scrubbing. No frame-by-frame editing. Just one sentence → one video.
+<p align="center"><i>👆 Click the cover to play the full demo</i></p>
+
+One sentence builds a reusable cloud-side short-film director for storyboarding, storyboard image generation, and video creation:
+
+- **[Qwen Code](https://github.com/QwenLM/qwen-code)** — understands the requirement and generates the agent configuration
+- **[Aliyun Model Studio CLI](https://github.com/modelstudioai/cli/)** — validates the configuration, previews the changes, and completes the deployment
+- **[Managed Agent](https://bailian.console.aliyun.com/cn-beijing/?tab=managed-agents#/managed-agents/quick-start)** — runs the director role along with its skills and tools in the cloud
+
+### The single prompt
+
+> _"Build me a Managed Agent app that can produce short films — a director expert that generates videos and can also design the matching storyboards."_

 ## Installation

+**Agent install (recommended)**
+
+Send the following to your Agent — it will detect your environment, then install and verify the CLI for you:
+
+```text
+Please read https://bailian.aliyun.com/cli/install.md and install the Aliyun Model Studio CLI for me
+```
+
+**Manual install (npm)**
+
 ```bash
-# Recommended — no Node required
-curl -fsSL https://bailian.aliyun.com/cli/install.sh | bash
-
-# Windows (PowerShell)
-irm https://bailian.aliyun.com/cli/install.ps1 | iex
-
-# Node users / developers (Node.js >= 18.17)
 npm install -g bailian-cli
-
-# Agent skills
 npx skills add modelstudioai/cli --all -g
 ```

-> Binary install does not require Node.js. `npm install -g` remains fully supported.
+> Requires Node.js >= 18.17.

 ## Quick Start

-```bash
-# Authenticate, recommended
-bl auth login --console
+Once installed, just describe your task to your AI Agent — no need to assemble commands by hand.

-# Or authenticate with an API key
-bl auth login --api-key sk-xxxxx
-
-# Or use Token Plan (Base URL built in; the key is tested during login)
-bl auth login --config token-plan --api-key sk-sp-xxxxx
-
-# Configure a coding agent to use DashScope
-bl config agent --agent codex --base-url https://dashscope.aliyuncs.com/compatible-mode/v1 --api-key sk-xxxxx --model qwen3-coder-plus
-
-# Chat with Qwen
-bl text chat --message "What is DashScope?"
-
-# Multimodal chat (text + image + audio + video)
-bl omni --message "Describe this image" --image ./photo.jpg
-
-# Generate an image
-bl image generate --prompt "A cat in a spacesuit" --out-dir ./images/
-
-# Generate a video from local image
-bl video generate --image ./cat.png --prompt "Make the cat move" --download cat.mp4
-
-# Model recommendation — find the best model for your use case
-bl advisor recommend --message "I need a visual-understanding chatbot"
-
-# Compare specific models
-bl advisor recommend --message "qwen-max vs deepseek-v3 for code generation"
-
-# Browser login (required for console capability commands)
-bl auth login --console
-
-# Fine-tune & deploy — a one-shot train-to-serve workflow
-bl dataset upload --file ./train.jsonl                 # Upload a .jsonl dataset (validated first)
-bl finetune text create --model qwen3-8b --datasets ./train.jsonl --training-type sft-lora  # Local paths auto-upload
-bl finetune watch --job-id ft-xxx --output json       # Non-blocking probe (running/succeeded return 0; failed/canceled report an error)
-bl finetune capability --model qwen3-8b               # Which training types a model supports
-bl deploy text create --model qwen3-8b --name my-svc --plan mu  # Deploy the trained model as an endpoint
-
-# Browse models / apps / free-tier quota / usage statistics / workspaces
-bl model list                                        # Browse model families and pricing
-bl app list
-bl usage summary                                     # Unified view: free-tier quota + recent usage overview
-bl usage free                                         # Free-tier quota across models (add --model/--expiring/--sort)
-bl usage stats --workspace-id <id>                    # Model usage statistics (add --model for per-model)
-bl workspace list                                     # List all workspaces
-
-# Rate limit management (list / check / request / history)
-bl quota list                                         # View RPM/TPM limits (add --model to filter)
-bl quota check                                        # Current usage vs rate limits (add --model/--period)
-bl quota request --model qwen3.6-plus --tpm 6000000   # Request a temporary TPM increase
-bl quota history                                      # View quota-change history
-
-# Token Plan team management (requires AK/SK, see auth below)
-bl token-plan list-seats                                # View subscription seat details
-bl token-plan add-member --account-name dev --org-id org_xxx
-bl token-plan assign-seats --workspace-id ws_xxx --seat-type standard --account-id acc_xxx
-bl token-plan create-key --account-id acc_xxx --workspace-id ws_xxx
-```
+| Scenario                 | What to say to your Agent                                                         |
+| ------------------------ | --------------------------------------------------------------------------------- |
+| Managed Agent            | "Create a Managed Agent that can generate short-film storyboards and videos."     |
+| Image & video generation | "Generate an image of a cat in a spacesuit on Mars, then turn it into a video."   |
+| Usage & quota            | "Show my recent model usage, free-tier quota, and rate limits."                   |
+| Model selection          | "Recommend a model for image understanding and customer support."                 |
+| About Bailian CLI        | "Tell me what Bailian CLI can do for me, and suggest how to use it for my needs." |

 > More examples and scenarios: [Aliyun Model Studio CLI Site](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)

 ## Authentication

-### DashScope API Key
+### API Key

 Required for most commands. Get your key from the [DashScope Console](https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key).

 ```bash
-# Option 1: Environment variable
-export DASHSCOPE_API_KEY=sk-xxxxx
-
-# Option 2: Login command (persisted to ~/.bailian/config.json)
 bl auth login --api-key sk-xxxxx
-
-# Option 3: Per-command flag
-bl text chat --api-key sk-xxxxx --message "Hello"
 ```

-### Token Plan API Key
-
-Get or copy the API key from the [Token Plan subscription overview](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview).
-The CLI has the default Token Plan Base URL built in. Login tests the key first, then saves and activates the `token-plan` config only when validation succeeds.
+Get or copy your Token Plan API key from the [Token Plan subscription overview](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview).

 ```bash
 bl auth login --config token-plan --api-key sk-sp-xxxxx
@@ -186,26 +123,20 @@ bl auth login --config token-plan --api-key sk-sp-xxxxx

 ### Console Login (OAuth)

-Required for console capability commands (`model list`, `app list`, `usage summary/free/stats`, `workspace list`, `quota list/request/check/history`). Opens the Bailian console in your browser to sign in.
+Required for console capability commands (model list, app list, MCP list, workspace, usage queries, rate-limit increases, direct console calls). Opens the Bailian console in your browser to sign in.

 ```bash
 bl auth login --console
 ```

-### Alibaba Cloud OpenAPI AK/SK (Token Plan only)
+### Alibaba Cloud OpenAPI AK/SK

-Required for the `token-plan` command group. Get your AccessKey from [RAM Console](https://ram.console.aliyun.com/manage/ak).
+Token Plan seat and member management requires an Alibaba Cloud AccessKey. Get yours from the [RAM Console](https://ram.console.aliyun.com/manage/ak).

 > Recommended: create a RAM sub-account with minimum privileges instead of using the root account's AK/SK.

 ```bash
-# Option 1: Login command (persisted to ~/.bailian/config.json)
 bl auth login --open-api --access-key-id LTAI5t... --access-key-secret ...
-
-# Option 2: Environment variables
-export ALIBABA_CLOUD_ACCESS_KEY_ID=LTAI5t...
-export ALIBABA_CLOUD_ACCESS_KEY_SECRET=...
-export BAILIAN_WORKSPACE_ID=ws-...
 ```

 ## Configuration
@@ -214,18 +145,31 @@ export BAILIAN_WORKSPACE_ID=ws-...
 # View current config
 bl config show

-# Set defaults
-bl config set --key base_url --value https://dashscope-us.aliyuncs.com
-bl config set --key default_text_model --value qwen-turbo
-bl config set --key timeout --value 600
+# List all config profiles
+bl config list

-# Self-update to latest or a specific version
-bl update
-bl update --to 0.1.14
+# Switch config profile
+bl config use --name token-plan
 ```

 Config file location: `~/.bailian/config.json`

+## Update
+
+```bash
+bl update
+```
+
+Upgrades the CLI to the latest version and refreshes the installed Agent Skills. Release notes for every version live in [CHANGELOG.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.md).
+
+## Contributing
+
+Bug reports, feature requests, and PRs are welcome. See [CONTRIBUTING.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.md) for developer setup, repo layout, and the workflow for adding or changing commands.
+
+Scan the QR code to join the Aliyun Model Studio CLI DingTalk user group for usage help, troubleshooting, bug reports, and tips from other users.
+
+<img src="https://img.alicdn.com/imgextra/i3/O1CN015uuhYGb6j0L12xJZ_!!6000000006304-2-tps-516-485.png" alt="Aliyun Model Studio CLI DingTalk user group" width="240" />
+
 ## Links

 | Resource                     | URL                                                                                       |
@@ -237,11 +181,3 @@ Config file location: `~/.bailian/config.json`
 | Get API Key                  | https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key |
 | Get Token Plan API Key       | https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview         |
 | Get AccessKey                | https://ram.console.aliyun.com/manage/ak                                                  |
-
-## Changelog
-
-Release notes for every version live in [CHANGELOG.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.md).
-
-## Contributing
-
-Bug reports, feature requests, and PRs are welcome. See [CONTRIBUTING.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.md) for developer setup, repo layout, and the workflow for adding or changing commands.
@@ -22,28 +22,16 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_

 ## 功能特性

-让您的 AI Agent 开箱即具备以下能力，并可在复杂任务中自动组合调用：
+- **模型生成** — 文本、图像、视频、语音全模态生成，支持编辑与参考生成
+- **素材理解** — 图像、文档、音频、长视频的解析与问答
+- **应用编排** — 调用百炼已发布的 Managed Agent、智能体和工作流，接入知识库、记忆库、联网搜索与 MCP 工具
+- **模型训推** — 数据集校验上传、模型精调、专属模型部署上线
+- **账号运维** — 授权登录、界面化配置、模型市场、用量与额度、限流提额、团队席位管理
+- **套餐接入** — 支持 Token Plan 等订阅计划一键接到 CLI 和常见 Coding Agent

- **文本对话** — Qwen3.8-max：Agentic coding、前端编程、Vibe coding 等能力显著增强
- **全模态对话** — 文本 + 图像 + 音频 + 视频全模态支持
- **图像生成与编辑** — Qwen-Image 3.0：专业文字渲染、真实质感、强语义遵循、多图合成
- **视频生成与编辑** — happyhorse-1.1 系列，支持文生 / 图生 / 参考生（最多 9 张图参考）/ 自然语言视频编辑
- **语音合成与识别** — CosyVoice 实时流式合成，5-20s 样本即可克隆；FunAudio-ASR 覆盖 30 种语种，含汉语七大方言与 20+ 口音官话
- **图像与视频理解** — Qwen-VL：长视频解析、复杂图表与文档识别、视觉推理、多语种 OCR
- **Coding Agent 配置** — 使用 `bl config agent` 将 Claude Code、Qwen Code、OpenCode、OpenClaw、Hermes Agent 或 Codex 配置为使用 DashScope
+> **注意：** 应用编排、模型训推、账号运维和套餐接入目前仅支持中国站（aliyun.com）账号，暂不支持国际站 / 全球站账号。

-> **注意：** 以下功能目前仅对中国站（aliyun.com）账号开放，国际站 / 全球站账号暂不支持。
-
- **知识库与记忆库** — 多模态 RAG 检索 + 跨会话记忆，提供个性化连贯对话体验
- **应用调用** — 调用已发布在阿里云百炼平台上的智能体与工作流应用
- **MCP 集成** — 统一调度百炼 MCP 服务：列出服务、查看工具、直接在终端调用任意工具
- **联网搜索** — 实时互联网信息检索，提升回答准确性及时效性
- **模型推荐** — 描述你的场景，智能推荐最适合的模型；支持限定范围搜索、模型对比和替代发现
- **微调与部署** — 上传数据集、创建文本/音频/图像调优任务（`finetune text|audio|image create`；文本涵盖 SFT/LoRA/DPO/CPT）、非阻塞探测任务状态（`finetune watch`）、按模型查训练能力（`finetune capability`），并把训练好的模型部署为推理服务（`deploy text|audio|image create`）
- **控制台能力** — 浏览模型市场（`model list`）和百炼应用（`app list`），查看统一用量视图（`usage summary`），查询模型免费额度（`usage free`），查看模型用量统计（`usage stats`），管理业务空间（`workspace list`），管理限流与提额（`quota list/request/check/history`）
- **本地文件自动上传** — 所有 URL 参数同时支持本地路径，免费临时存储 48 小时
-
-## 示例:一句话生成一部电影短片
+## 示例 1：一句话生成一部电影短片

 <p align="center">
  <a href="https://cloud.video.taobao.com/vod/dS2F4huqbw5Nfe5L3wwb3grz2q2DNYD3retq8dU-iHo.mp4">
@@ -53,130 +41,80 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_

 <p align="center"><i>👆 点击封面播放完整 2 分钟演示</i></p>

-一部完整的 **2 分钟、16:9 电影感短片** —— 由一句自然语言端到端生成,**全程零手动剪辑**。这个示例展示了 AI Agent 如何把三个基础能力编排成一条多步创作流水线:
+一部完整的 **2 分钟、16:9 电影感短片** —— 由一句自然语言端到端生成，**全程零手动剪辑**。这个示例展示了 AI Agent 如何把三个基础能力编排成一条多步创作流水线：

- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— Agentic coding 模型,解析用户意图、驱动整个工作流
- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.1**,百炼的文生/图生/参考生视频模型
+- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— Agentic coding 模型，解析用户意图、驱动整个工作流
+- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.1**，百炼的文生/图生/参考生视频模型
 - **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** —— 负责场景拆分、分镜设计、镜头连贯性和最终拼接

 ### 唯一的提示词

-> _"帮我生成一段日系影视风格,高中女生的青涩初恋故事,剧情高甜,让人看了想谈恋爱,2 分钟左右的视频,尺寸是 16:9"_
+> _“帮我生成一段日系影视风格，高中女生的青涩初恋故事，剧情高甜，让人看了想谈恋爱，2 分钟左右的视频，尺寸是 16:9。”_

-### 工作流程
+## 示例 2：一句话构建短片导演 Managed Agent

-1. **Qwen Code** 解析需求、规划叙事节奏,决定要调用哪些工具。
-2. **spark-video Skill** 把故事拆成镜头、为每个镜头写提示词,并保证视觉连贯性(角色、光线、色调、镜头语言)。
-3. **`bl video generate`** 把每个镜头并行下发给 **HappyHorse 1.1**。
-4. Skill 把所有片段拼成最终的 16:9 / 约 2 分钟成片。
+<p align="center">
+  <a href="https://cloud.video.taobao.com/vod/2v0GYLbJSQb2saj4iopTJDW3iRIHsintYlK-wTKbhqE.mp4">
+    <img src="https://img.alicdn.com/imgextra/i4/6000000001674/O1CN01xhzixhxltbH3LxWu_!!6000000001674-0-tbvideo.jpg" alt="点击播放演示视频" width="720" />
+  </a>
+</p>

-没有时间线拖拽,没有逐帧剪辑。一句话 → 一部短片。
+<p align="center"><i>👆 点击封面播放完整演示</i></p>
+
+一句话构建一个可复用的云端短片导演，用于分镜设计、分镜图生成和视频创作：
+
+- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— 理解需求并生成 Agent 配置
+- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 校验配置、预览变更并完成部署
+- **[Managed Agent](https://bailian.console.aliyun.com/cn-beijing/?tab=managed-agents#/managed-agents/quick-start)** —— 在云端运行导演角色及其 Skill 和工具
+
+### 唯一的提示词
+
+> _“帮我构建一个 managedagent 应用，能够实现短片拍摄，导演专家生成视频，然后也能进行设计对应的分镜图。”_

 ## 安装

+**Agent 安装（推荐）**
+
+把下面这句话发给你的 Agent，它会自行判断环境并完成安装与校验：
+
+```text
+请阅读：https://bailian.aliyun.com/cli/install.md 并按照说明为我安装阿里云百炼 CLI
+```
+
+**手动安装（npm）**
+
 ```bash
-# 推荐 — 无需本机 Node.js
-curl -fsSL https://bailian.aliyun.com/cli/install.sh | bash
-
-# Windows（PowerShell）
-irm https://bailian.aliyun.com/cli/install.ps1 | iex
-
-# Node 用户 / 开发者（需要 Node.js >= 18.17）
 npm install -g bailian-cli
-
-# Agent skills
 npx skills add modelstudioai/cli --all -g
 ```

-> 二进制安装不依赖 Node.js。`npm install -g` 长期保留。
+> 需要预先安装 Node.js >= 18.17。

 ## 快速开始

-```bash
-# 认证（推荐浏览器登录）
-bl auth login --console
+安装完成后，直接在 AI Agent 中描述你的任务，无需手动拼接命令。

-# 或使用 API key 认证
-bl auth login --api-key sk-xxxxx
-
-# 或使用 Token Plan（已内置 Base URL，登录时自动测试 Key）
-bl auth login --config token-plan --api-key sk-sp-xxxxx
-
-# 配置 Coding Agent 使用 DashScope
-bl config agent --agent codex --base-url https://dashscope.aliyuncs.com/compatible-mode/v1 --api-key sk-xxxxx --model qwen3-coder-plus
-
-# 和通义千问对话
-bl text chat --message "你好，介绍一下阿里云百炼平台"
-
-# 多模态对话（文本 + 图片 + 音频 + 视频）
-bl omni --message "描述这张图片" --image ./photo.jpg
-
-# 生成图片
-bl image generate --prompt "一只穿太空服的猫在火星上" --out-dir ./images/
-
-# 图生视频（本地文件自动上传）
-bl video generate --image ./cat.png --prompt "让画面中的猫动起来" --download cat.mp4
-
-# 模型推荐 — 根据场景推荐最适合的模型
-bl advisor recommend --message "我要做一个能理解图片的客服机器人"
-
-# 对比特定模型
-bl advisor recommend --message "qwen-max 和 deepseek-v3 哪个更适合做代码生成"
-
-# 浏览器登录（控制台能力相关命令需要）
-bl auth login --console
-
-# 微调与部署 — 从训练到服务的一站式流程
-bl dataset upload --file ./train.jsonl                 # 上传 .jsonl 数据集（先校验）
-bl finetune text create --model qwen3-8b --datasets ./train.jsonl --training-type sft-lora  # 本地路径自动上传
-bl finetune watch --job-id ft-xxx --output json       # 非阻塞探测（运行中/成功返回 0；失败/取消报错）
-bl finetune capability --model qwen3-8b               # 查询模型支持哪些训练方式
-bl deploy text create --model qwen3-8b --name my-svc --plan mu  # 把训练好的模型部署为推理服务
-
-# 浏览模型 / 应用 / 免费额度 / 用量统计 / 业务空间
-bl model list                                        # 浏览模型系列与价格信息
-bl app list
-bl usage summary                                     # 统一视图：免费额度 + 近期用量概览
-bl usage free                                         # 各模型免费额度（可加 --model/--expiring/--sort）
-bl usage stats --workspace-id <id>                    # 模型用量统计（加 --model 查单模型）
-bl workspace list                                     # 列出所有业务空间
-
-# 限流管理与提额（list / check / request / history）
-bl quota list                                         # 查看 RPM/TPM 限额（加 --model 过滤）
-bl quota check                                        # 当前用量 vs 限流阈值（加 --model/--period）
-bl quota request --model qwen3.6-plus --tpm 6000000   # 申请临时 TPM 提额
-bl quota history                                      # 查看提额历史记录
-
-# Token Plan 团队版管理（需 AK/SK，见下方认证说明）
-bl token-plan list-seats                                # 查看订阅席位明细
-bl token-plan add-member --account-name dev --org-id org_xxx
-bl token-plan assign-seats --workspace-id ws_xxx --seat-type standard --account-id acc_xxx
-bl token-plan create-key --account-id acc_xxx --workspace-id ws_xxx
-```
+| 场景             | 可以这样对 Agent 说                                                     |
+| ---------------- | ----------------------------------------------------------------------- |
+| Managed Agent    | “帮我创建一个能够生成短片分镜和视频的 Managed Agent。”                  |
+| 图片和视频生成   | “生成一张穿着太空服的猫站在火星上的图片，再把它制作成一段视频。”        |
+| 用量与额度       | “查看最近的模型用量、免费额度和限流情况。”                              |
+| 模型选型         | “推荐一个适合图片理解和智能客服的模型。”                                |
+| 了解 Bailian CLI | “介绍一下 Bailian CLI 能帮我完成哪些任务，并根据我的需求推荐使用方式。” |

 > 更多案例与使用场景：[阿里云百炼 CLI 官方主页](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)

 ## 认证方式

-### DashScope API Key
+### API Key

 大部分命令均需要 API Key。前往 [DashScope 控制台](https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key) 获取。

 ```bash
-# 方式一：环境变量
-export DASHSCOPE_API_KEY=sk-xxxxx
-
-# 方式二：登录命令（持久化到 ~/.bailian/config.json）
 bl auth login --api-key sk-xxxxx
-
-# 方式三：命令行参数
-bl text chat --api-key sk-xxxxx --message "你好"
 ```

-### Token Plan API Key
-
-前往 [Token Plan 订阅详情](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview) 获取或复制 API Key。
-CLI 已内置 Token Plan 的默认 Base URL；登录命令会先测试 Key，通过后才保存并激活 `token-plan` 配置。
+Token Plan 的 API Key 前往 [Token Plan 订阅详情](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview) 获取或复制。

 ```bash
 bl auth login --config token-plan --api-key sk-sp-xxxxx
@@ -184,26 +122,20 @@ bl auth login --config token-plan --api-key sk-sp-xxxxx

 ### 控制台登录（OAuth）

-控制台能力命令（`model list`、`app list`、`usage summary/free/stats`、`workspace list`、`quota list/request/check/history`）需要使用此登录方式。打开浏览器跳转百炼控制台完成登录。
+控制台能力命令（模型列表、应用列表、MCP 列表、工作空间、用量查询、限流提额、控制台直调）需要使用此登录方式。打开浏览器跳转百炼控制台完成登录。

 ```bash
 bl auth login --console
 ```

-### 阿里云 OpenAPI AK/SK（仅 Token Plan）
+### 阿里云 OpenAPI AK/SK

-`token-plan` 命令组需要阿里云 AccessKey。前往 [RAM 控制台](https://ram.console.aliyun.com/manage/ak) 获取。
+Token Plan 的席位与成员管理需要阿里云 AccessKey。前往 [RAM 控制台](https://ram.console.aliyun.com/manage/ak) 获取。

 > 建议：创建 RAM 子账号并授予最小权限，避免使用主账号 AK/SK。

 ```bash
-# 方式一：登录命令（持久化到 ~/.bailian/config.json）
 bl auth login --open-api --access-key-id LTAI5t... --access-key-secret ...
-
-# 方式二：环境变量
-export ALIBABA_CLOUD_ACCESS_KEY_ID=LTAI5t...
-export ALIBABA_CLOUD_ACCESS_KEY_SECRET=...
-export BAILIAN_WORKSPACE_ID=ws-...
 ```

 ## 配置
@@ -212,20 +144,31 @@ export BAILIAN_WORKSPACE_ID=ws-...
 # 查看当前配置
 bl config show

-# 设置默认值
-bl config set --key base_url --value https://dashscope-us.aliyuncs.com
-bl config set --key default_text_model --value qwen-turbo
-bl config set --key timeout --value 600
+# 查看全部配置档
+bl config list

-# 自更新到最新版本
-bl update
-
-# 安装指定版本
-bl update --to 0.1.14
+# 切换配置档
+bl config use --name token-plan
 ```

 配置文件位置：`~/.bailian/config.json`

+## 更新
+
+```bash
+bl update
+```
+
+升级 CLI 至最新版本，并同步更新已安装的 Agent Skills。每个版本的变更详情记录在 [CHANGELOG.zh.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.zh.md)。
+
+## 参与贡献
+
+欢迎提 Issue、Feature Request 和 PR。开发环境搭建、仓库结构、新增/修改命令的工作流请见 [CONTRIBUTING.zh.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.zh.md)。
+
+欢迎扫码加入阿里云百炼 CLI 钉钉用户交流群，获取使用答疑、问题排查、Bug 反馈和使用经验交流支持。
+
+<img src="https://img.alicdn.com/imgextra/i3/O1CN015uuhYGb6j0L12xJZ_!!6000000006304-2-tps-516-485.png" alt="阿里云百炼 CLI 钉钉用户交流群" width="240" />
+
 ## 相关链接

 | 资源                    | 地址                                                                                      |
@@ -237,11 +180,3 @@ bl update --to 0.1.14
 | 获取 API Key            | https://bailian.console.aliyun.com/cn-beijing/?source_channel=key_github&tab=app#/api-key |
 | 获取 Token Plan API Key | https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview         |
 | 获取 AccessKey          | https://ram.console.aliyun.com/manage/ak                                                  |
-
-## 更新日志
-
-每个版本的变更详情记录在 [CHANGELOG.zh.md](https://github.com/modelstudioai/cli/blob/main/CHANGELOG.zh.md)。
-
-## 参与贡献
-
-欢迎提 Issue、Feature Request 和 PR。开发环境搭建、仓库结构、新增/修改命令的工作流请见 [CONTRIBUTING.zh.md](https://github.com/modelstudioai/cli/blob/main/CONTRIBUTING.zh.md)。
@@ -1,6 +1,6 @@
 {
  "name": "bailian-cli",
-  "version": "1.14.1",
+  "version": "1.14.2",
  "description": "CLI for Aliyun Model Studio (DashScope) AI Platform.",
  "keywords": [
    "agent",
@@ -62,6 +62,7 @@ import {
  finetuneTextCreate,
  finetuneAudioCreate,
  finetuneImageCreate,
+  finetuneVideoCreate,
  finetuneList,
  finetuneGet,
  finetuneCancel,
@@ -71,6 +72,7 @@ import {
  finetuneExport,
  finetuneWatch,
  finetuneCapability,
+  finetunePrice,
  deployTextCreate,
  deployAudioCreate,
  deployImageCreate,
@@ -80,6 +82,8 @@ import {
  deployScale,
  deployUpdate,
  deployDelete,
+  deployPause,
+  deployResume,
  tokenPlanListSeats,
  tokenPlanCreateKey,
  tokenPlanAssignSeats,
@@ -93,6 +97,7 @@ import {
  skillUpdate,
  skillRemove,
  skillList,
+  skillInit,
  managedAgentInit,
  managedAgentValidate,
  managedAgentPlan,
@@ -180,6 +185,7 @@ export const commands: Record<string, AnyCommand> = {
  "finetune text create": finetuneTextCreate,
  "finetune audio create": finetuneAudioCreate,
  "finetune image create": finetuneImageCreate,
+  "finetune video create": finetuneVideoCreate,
  "finetune list": finetuneList,
  "finetune get": finetuneGet,
  "finetune cancel": finetuneCancel,
@@ -189,6 +195,7 @@ export const commands: Record<string, AnyCommand> = {
  "finetune export": finetuneExport,
  "finetune watch": finetuneWatch,
  "finetune capability": finetuneCapability,
+  "finetune price": finetunePrice,
  "deploy text create": deployTextCreate,
  "deploy audio create": deployAudioCreate,
  "deploy image create": deployImageCreate,
@@ -198,6 +205,8 @@ export const commands: Record<string, AnyCommand> = {
  "deploy scale": deployScale,
  "deploy update": deployUpdate,
  "deploy delete": deployDelete,
+  "deploy pause": deployPause,
+  "deploy resume": deployResume,
  "token-plan list-seats": tokenPlanListSeats,
  "token-plan create-key": tokenPlanCreateKey,
  "token-plan assign-seats": tokenPlanAssignSeats,
@@ -211,6 +220,7 @@ export const commands: Record<string, AnyCommand> = {
  "skill update": skillUpdate,
  "skill remove": skillRemove,
  "skill list": skillList,
+  "skill init": skillInit,
  "managed-agent init": managedAgentInit,
  "managed-agent validate": managedAgentValidate,
  "managed-agent plan": managedAgentPlan,
@@ -1,6 +1,6 @@
 {
  "name": "bailian-cli-commands",
-  "version": "1.14.1",
+  "version": "1.14.2",
  "description": "Command library for bailian-cli products (knowledge, memory, media, …). See https://www.npmjs.com/package/bailian-cli for usage.",
  "homepage": "https://bailian.console.aliyun.com/cli",
  "bugs": {
@@ -1,5 +1,5 @@
-import { defineCommand, detectOutputFormat, deleteDataset, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, deleteDataset, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const DELETE_FLAGS = {
  fileId: {
@@ -19,20 +19,18 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const fileId = flags.fileId;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "dataset.delete", file_id: fileId }, format);
+      emitResult({ action: "dataset.delete", file_id: fileId }, "json");
      return;
    }

    const response = await deleteDataset(ctx.client, fileId);

-    if (settings.quiet || format === "text") {
-      emitBare(`Deleted ${fileId}.`);
-      emitRequestId(response.request_id, settings.quiet);
+    if (settings.quiet) {
+      emitBare(fileId);
    } else {
-      emitResult(response, format);
+      emitResult(response, "json");
    }
  },
 });
@@ -1,5 +1,5 @@
-import { defineCommand, detectOutputFormat, getDataset, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, getDataset, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const GET_FLAGS = {
  fileId: {
@@ -19,10 +19,9 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const fileId = flags.fileId;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "dataset.get", file_id: fileId }, format);
+      emitResult({ action: "dataset.get", file_id: fileId }, "json");
      return;
    }

@@ -45,19 +44,10 @@ export default defineCommand({
      description: file.description ?? "",
    };

-    if (format === "json") {
-      emitResult({ ...item, request_id: response.request_id }, format);
-      return;
+    if (settings.quiet) {
+      emitBare(item.file_id);
+    } else {
+      emitResult({ ...item, request_id: response.request_id }, "json");
    }
-
-    // text / quiet
-    emitBare(`file_id:      ${item.file_id}`);
-    emitBare(`name:         ${item.name}`);
-    emitBare(`size:         ${item.size}`);
-    if (item.md5) emitBare(`md5:          ${item.md5}`);
-    if (item.purpose) emitBare(`purpose:      ${item.purpose}`);
-    if (item.created_at) emitBare(`created_at:   ${item.created_at}`);
-    if (item.description) emitBare(`description:  ${item.description}`);
-    emitRequestId(response.request_id, settings.quiet);
  },
 });
@@ -1,5 +1,5 @@
-import { defineCommand, detectOutputFormat, listDatasets, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId, formatTable } from "bailian-cli-runtime";
+import { defineCommand, listDatasets, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const LIST_FLAGS = {
  page: { type: "number", valueHint: "<n>", description: "Page number (default: 1)" },
@@ -23,7 +23,6 @@ export default defineCommand({
  exampleArgs: ["", "--purpose fine-tune", "--purpose evaluation --page-size 20", "--output json"],
  async run(ctx) {
    const { settings, flags } = ctx;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
      emitResult(
@@ -33,7 +32,7 @@ export default defineCommand({
          page_size: flags.pageSize,
          purpose: flags.purpose,
        },
-        format,
+        "json",
      );
      return;
    }
@@ -46,7 +45,6 @@ export default defineCommand({
    const files = response.data?.files ?? [];
    const total = response.data?.total;

-    // Normalize to consistent structure for both text/json output.
    const items = files.map((item) => ({
      file_id: item.file_id ?? "",
      name: item.name ?? "",
@@ -54,20 +52,10 @@ export default defineCommand({
      purpose: item.purpose ?? "",
    }));

-    if (format === "json") {
-      emitResult({ items, total, request_id: response.request_id }, format);
-      return;
+    if (settings.quiet) {
+      for (const item of items) emitBare(item.file_id);
+    } else {
+      emitResult({ items, total, request_id: response.request_id }, "json");
    }
-
-    // text / quiet
-    if (items.length === 0) {
-      emitBare("No dataset files found.");
-      return;
-    }
-    const headers = ["FILE_ID", "NAME", "SIZE", "PURPOSE"];
-    const rows = items.map((i) => [i.file_id, i.name, i.size, i.purpose]);
-    for (const line of formatTable(headers, rows)) emitBare(line);
-    if (total !== undefined) emitBare(`\nTotal: ${total}`);
-    emitRequestId(response.request_id, settings.quiet);
  },
 });
@@ -1,6 +1,5 @@
 import {
  defineCommand,
-  detectOutputFormat,
  uploadDataset,
  validateDataset,
  parseDatasetSchemaFlag,
@@ -11,7 +10,7 @@ import {
  ExitCode,
  type FlagsDef,
 } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const UPLOAD_FLAGS = {
  file: {
@@ -29,7 +28,7 @@ const UPLOAD_FLAGS = {
    type: "string",
    valueHint: "<s>",
    description:
-      'Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), or "image" (image generation). Default auto-detects per record.',
+      'Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), "image" (image generation), or "video" (video generation). Default auto-detects per record.',
  },
  noValidate: {
    type: "switch",
@@ -45,7 +44,7 @@ export default defineCommand({
  description: "Upload a dataset file (.jsonl or .zip) to Bailian",
  auth: "apiKey",
  usageArgs:
-    "--file <path> [--purpose <name>] [--schema <chatml|dpo|cpt|tts|image>] [--no-validate] [--full-validate]",
+    "--file <path> [--purpose <name>] [--schema <chatml|dpo|cpt|tts|image|video>] [--no-validate] [--full-validate]",
  flags: UPLOAD_FLAGS,
  exampleArgs: [
    "--file train.jsonl",
@@ -73,16 +72,8 @@ export default defineCommand({
    const filePath = flags.file;
    const purpose = flags.purpose || "fine-tune";
    const schema = parseDatasetSchemaFlag(flags.schema);
-    if (schema === "video") {
-      throw new BailianError(
-        `--schema video is not supported.`,
-        ExitCode.USAGE,
-        `Supported schemas: chatml, dpo, cpt, tts, image.`,
-      );
-    }
-    const format = detectOutputFormat(settings.output);
-    // Image schema allows larger ZIPs (1 GB vs 300 MB for text).
-    const isMediaSchema = schema === "image";
+    // Image and video schemas allow larger ZIPs (1 GB vs 300 MB for text).
+    const isMediaSchema = schema === "image" || schema === "video";

    if (!flags.noValidate) {
      const maxBytes = isMediaSchema ? MAX_MEDIA_ZIP_BYTES : MAX_DATASET_BYTES;
@@ -129,7 +120,7 @@ export default defineCommand({
          validate: !flags.noValidate,
          schema: schema ?? "auto",
        },
-        format,
+        "json",
      );
      return;
    }
@@ -142,11 +133,8 @@ export default defineCommand({

    if (settings.quiet) {
      emitBare(file.file_id);
-    } else if (format === "text") {
-      emitBare(`Uploaded ${file.name} → file_id=${file.file_id}`);
-      emitRequestId(request_id, settings.quiet);
    } else {
-      emitResult({ ...file, request_id }, format);
+      emitResult({ ...file, request_id }, "json");
    }
  },
 });
@@ -1,26 +1,13 @@
 import {
  defineCommand,
-  detectOutputFormat,
  validateDataset,
  parseDatasetSchemaFlag,
-  formatIssue,
  BailianError,
  ExitCode,
-  type ValidationResult,
  type FlagsDef,
 } from "bailian-cli-core";
 import { emitResult, emitBare } from "bailian-cli-runtime";

-function formatStats(result: ValidationResult): string[] {
-  const out: string[] = [];
-  if (result.stats.totalRecords !== undefined) out.push(`records: ${result.stats.totalRecords}`);
-  if (result.stats.sampledRecords !== undefined)
-    out.push(`sampled: ${result.stats.sampledRecords}`);
-  if (result.stats.bytes !== undefined) out.push(`bytes: ${result.stats.bytes}`);
-  if (result.stats.durationMs !== undefined) out.push(`took: ${result.stats.durationMs}ms`);
-  return out;
-}
-
 const VALIDATE_FLAGS = {
  file: {
    type: "string",
@@ -36,7 +23,7 @@ const VALIDATE_FLAGS = {
    type: "string",
    valueHint: "<s>",
    description:
-      'Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), or "image" (image generation). Default auto-detects per record.',
+      'Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), "image" (image generation), or "video" (video generation). Default auto-detects per record.',
  },
 } satisfies FlagsDef;

@@ -44,13 +31,14 @@ export default defineCommand({
  description: "Locally validate a dataset file (.jsonl or .zip) without uploading",
  // 纯本地校验，不触网、不需 API key（与 `pipeline validate` 一致）。
  auth: "none",
-  usageArgs: "--file <path> [--full-validate] [--schema <chatml|dpo|cpt|tts|image>]",
+  usageArgs: "--file <path> [--full-validate] [--schema <chatml|dpo|cpt|tts|image|video>]",
  flags: VALIDATE_FLAGS,
  exampleArgs: [
    "--file train.jsonl",
    "--file dpo.jsonl --schema dpo",
    "--file cpt.jsonl --schema cpt",
    "--file audio.zip --schema tts",
+    "--file wan-i2v-training-dataset.zip --schema video",
    "--file eval.jsonl --full-validate",
    "--file train.jsonl --output json",
  ],
@@ -60,27 +48,20 @@ export default defineCommand({
    "Schemas: chatml = {messages:[...]} (SFT); dpo = {messages:[...], chosen,",
    'rejected}; cpt = {text:"..."} (continual pre-training, raw text);',
    'tts = {wav_fn:"train/xxx.wav", text:"..."} (audio fine-tuning);',
-    'image = {img_path:"..."} (image generation). With no --schema, a record',
-    "carrying wav_fn is validated as TTS, img_path as image, chosen/rejected",
-    "as DPO, text (no messages) as CPT, otherwise ChatML. Pass --schema to",
-    "require a specific shape on every record. ZIP archives (.zip) are",
-    "validated structurally (data.jsonl present, media references resolve) in",
-    "addition to per-record content checks. Use --full-validate to JSON.parse",
-    "every line.",
+    'image = {img_path:"..."} (image generation);',
+    'video = {first_frame_path:"...", video_path:"..."} (video generation,',
+    "i2v first-frame or kf2v first+last-frame with last_frame_path). With no",
+    "--schema, a record carrying wav_fn is validated as TTS, img_path as image,",
+    "first_frame_path/video_path as video, chosen/rejected as DPO, text (no",
+    "messages) as CPT, otherwise ChatML. Pass --schema to require a specific",
+    "shape on every record. ZIP archives (.zip) are validated structurally",
+    "(data.jsonl present, media references resolve) in addition to per-record",
+    "content checks. Use --full-validate to JSON.parse every line.",
  ],
  async run(ctx) {
    const { settings, flags } = ctx;
    const filePath = flags.file;
    const schema = parseDatasetSchemaFlag(flags.schema);
-    if (schema === "video") {
-      throw new BailianError(
-        `--schema video is not supported.`,
-        ExitCode.USAGE,
-        `Supported schemas: chatml, dpo, cpt, tts, image.`,
-      );
-    }
-    const format = detectOutputFormat(settings.output);
-
    if (settings.dryRun) {
      emitResult(
        {
@@ -89,38 +70,17 @@ export default defineCommand({
          full: flags.fullValidate,
          schema: schema ?? "auto",
        },
-        format,
+        "json",
      );
      return;
    }

    const result = await validateDataset(filePath, { fullValidate: flags.fullValidate, schema });

-    if (format === "json") {
-      // For json output we always emit the structured result, exit code conveys validity.
-      emitResult(result, format);
-    } else if (settings.quiet) {
+    if (settings.quiet) {
      emitBare(result.valid ? "ok" : "fail");
    } else {
-      const status = result.valid ? "PASSED" : "FAILED";
-      emitBare(`Dataset validation ${status} for ${result.filePath}`);
-      const stats = formatStats(result);
-      if (stats.length) emitBare(`  ${stats.join(" · ")}`);
-
-      if (result.errors.length) {
-        emitBare(`Errors (${result.errors.length}):`);
-        for (const error of result.errors.slice(0, 20)) emitBare(formatIssue(error));
-        if (result.errors.length > 20) {
-          emitBare(`  … and ${result.errors.length - 20} more.`);
-        }
-      }
-      if (result.warnings.length) {
-        emitBare(`Warnings (${result.warnings.length}):`);
-        for (const warning of result.warnings.slice(0, 10)) emitBare(formatIssue(warning));
-        if (result.warnings.length > 10) {
-          emitBare(`  … and ${result.warnings.length - 10} more.`);
-        }
-      }
+      emitResult(result, "json");
    }

    if (!result.valid) {
@@ -1,6 +1,5 @@
 import {
  defineCommand,
-  detectOutputFormat,
  createDeployment,
  pickPlanStrategy,
  STRATEGIES,
@@ -11,16 +10,16 @@ import {
  type CommandContext,
  type FlagsDef,
 } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const CREATE_FLAGS = {
-  model: {
+  modelName: {
    type: "string",
-    valueHint: "<name>",
-    description: "Model name (catalog model or fine-tuned output) (required)",
+    valueHint: "<model_name>",
+    description: "Model to deploy — fine-tuned output name or catalog model (required)",
    required: true,
  },
-  name: {
+  displayName: {
    type: "string",
    valueHint: "<display_name>",
    description: "Console display name for the deployment (required)",
@@ -64,7 +63,7 @@ const CREATE_FLAGS = {
 } satisfies FlagsDef;

 const CREATE_USAGE =
-  "--model <model_name> --name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]";
+  "--model-name <model_name> --display-name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]";

 const CREATE_NOTES = [
  "Plan defaults to `lora` (Token-billed) for text/image and `mu` (model-unit-",
@@ -78,14 +77,11 @@ const CREATE_NOTES = [
  "Use `bl deploy models --source base` to inspect available templates.",
  "After creation, status starts at PENDING and transitions to RUNNING.",
  "Invoke the deployed model with: bl text chat --model <deployed_model>",
-  "WARNING: --model is overloaded across commands and refers to DIFFERENT",
-  "values. `bl deploy <modality> create --model` takes the exported model_name",
-  "(e.g. `qwen3-8b-ft-...`), but the create response also returns a",
-  "`deployed_model` field (the deployment instance id, e.g.",
-  "`qwen3-8b-5ecb5f068d79`). The inference call `bl text chat --model` must use",
-  "the `deployed_model` from the create response — NOT the `model_name` you",
-  "passed to `deploy <modality> create`. Do not reuse the value across the two",
-  "commands.",
+  "NOTE: --model-name is the model being deployed (e.g. `qwen3-8b-ft-...`).",
+  "The create response also returns a `deployed_model` field — the deployment",
+  "instance id (e.g. `qwen3-8b-5ecb5f068d79`). Use that id for inference",
+  "(`bl text chat --model <deployed_model>`) and lifecycle commands",
+  "(`deploy get/scale/pause/resume/delete --deployed-model <id>`).",
 ];

 /**
@@ -119,10 +115,9 @@ async function runCreate(
  ctx: CommandContext<typeof CREATE_FLAGS>,
 ): Promise<void> {
  const { identity, settings, flags } = ctx;
-  const model = flags.model as string;
-  const name = flags.name as string;
+  const model = flags.modelName as string;
+  const name = flags.displayName as string;
  const plan = (flags.plan as string | undefined) || defaultDeployPlan(modality);
-  const format = detectOutputFormat(settings.output);

  // Plan-specific behaviour is owned by core `plans.ts`. The strategy resolves
  // the plan-specific body fragment (mu may auto-pick a template from the
@@ -146,7 +141,7 @@ async function runCreate(
  };

  if (settings.dryRun) {
-    emitResult({ action: "deploy.create", body }, format);
+    emitResult({ action: "deploy.create", body }, "json");
    return;
  }

@@ -155,17 +150,8 @@ async function runCreate(

  if (settings.quiet) {
    emitBare(deployment?.deployed_model ?? "");
-  } else if (format === "text") {
-    emitBare(`Created deployment.`);
-    if (deployment?.deployed_model) emitBare(`  deployed_model:  ${deployment.deployed_model}`);
-    if (deployment?.status) emitBare(`  status:          ${deployment.status}`);
-    if (deployment?.plan) emitBare(`  plan:            ${deployment.plan}`);
-    emitBare(
-      `\nNext: track readiness with: ${identity.binName} deploy get --deployed-model ${deployment?.deployed_model ?? "<id>"}`,
-    );
-    emitRequestId(response.request_id, settings.quiet);
  } else {
-    emitResult(response, format);
+    emitResult(response, "json");
  }
 }

@@ -176,10 +162,10 @@ export const deployTextCreate = defineCommand({
  usageArgs: CREATE_USAGE,
  flags: CREATE_FLAGS,
  exampleArgs: [
-    "--model my-qwen-sft --name my-sft-test",
-    "--model qwen3.6-flash-2026-04-16 --name my-flash --plan ptu --input-tpm 10000 --output-tpm 1000",
-    "--model qwen3-8b --name my-qwen3-mu --plan mu",
-    "--model qwen3-8b --name my-qwen3 --plan mu --deploy-spec MU1 --capacity 2",
+    "--model-name my-qwen-sft --display-name my-sft-test",
+    "--model-name qwen3.6-flash-2026-04-16 --display-name my-flash --plan ptu --input-tpm 10000 --output-tpm 1000",
+    "--model-name qwen3-8b --display-name my-qwen3-mu --plan mu",
+    "--model-name qwen3-8b --display-name my-qwen3 --plan mu --deploy-spec MU1 --capacity 2",
  ],
  notes: CREATE_NOTES,
  validate: (flags) => validateCreate("text", flags),
@@ -193,9 +179,9 @@ export const deployAudioCreate = defineCommand({
  usageArgs: CREATE_USAGE,
  flags: CREATE_FLAGS,
  exampleArgs: [
-    "--model my-cosyvoice-ft --name my-tts",
-    "--model my-cosyvoice-ft --name my-tts --deploy-spec dps-xxxx --capacity 1",
-    "--model my-cosyvoice-ft --name my-tts --dry-run",
+    "--model-name my-cosyvoice-ft --display-name my-tts",
+    "--model-name my-cosyvoice-ft --display-name my-tts --deploy-spec dps-xxxx --capacity 1",
+    "--model-name my-cosyvoice-ft --display-name my-tts --dry-run",
  ],
  notes: CREATE_NOTES,
  validate: (flags) => validateCreate("audio", flags),
@@ -209,9 +195,9 @@ export const deployImageCreate = defineCommand({
  usageArgs: CREATE_USAGE,
  flags: CREATE_FLAGS,
  exampleArgs: [
-    "--model my-wan-ft --name my-wan",
-    "--model my-wan-ft --name my-wan-mu --plan mu",
-    "--model my-wan-ft --name my-wan --dry-run",
+    "--model-name my-wan-ft --display-name my-wan",
+    "--model-name my-wan-ft --display-name my-wan-mu --plan mu",
+    "--model-name my-wan-ft --display-name my-wan --dry-run",
  ],
  notes: CREATE_NOTES,
  validate: (flags) => validateCreate("image", flags),
@@ -1,13 +1,12 @@
 import {
  defineCommand,
-  detectOutputFormat,
  deleteDeployment,
  getDeployment,
  BailianError,
  ExitCode,
  type FlagsDef,
 } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const DELETE_FLAGS = {
  deployedModel: {
@@ -38,10 +37,9 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const deployedModel = flags.deployedModel;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "deploy.delete", deployed_model: deployedModel }, format);
+      emitResult({ action: "deploy.delete", deployed_model: deployedModel }, "json");
      return;
    }

@@ -55,7 +53,8 @@ export default defineCommand({
        if (status && status !== "STOPPED" && status !== "FAILED") {
          throw new BailianError(
            `Deployment ${deployedModel} is ${status}. Only STOPPED / FAILED deployments can be deleted. ` +
-              `Stop it first via the platform console, or pass --skip-precheck to attempt deletion anyway.`,
+              `Run \`bl deploy pause --deployed-model ${deployedModel}\` to pause it first, ` +
+              `or pass --skip-precheck to attempt deletion anyway.`,
            ExitCode.USAGE,
          );
        }
@@ -69,11 +68,8 @@ export default defineCommand({

    if (settings.quiet) {
      emitBare(deployedModel);
-    } else if (format === "text") {
-      emitBare(`Deleted ${deployedModel}.`);
-      emitRequestId(response.request_id, settings.quiet);
    } else {
-      emitResult(response, format);
+      emitResult(response, "json");
    }
  },
 });
@@ -1,5 +1,5 @@
-import { defineCommand, detectOutputFormat, getDeployment, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, getDeployment, type FlagsDef } from "bailian-cli-core";
+import { emitResult } from "bailian-cli-runtime";

 const GET_FLAGS = {
  deployedModel: {
@@ -22,10 +22,9 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const deployedModel = flags.deployedModel;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "deploy.get", deployed_model: deployedModel }, format);
+      emitResult({ action: "deploy.get", deployed_model: deployedModel }, "json");
      return;
    }

@@ -33,7 +32,7 @@ export default defineCommand({
    const deployment = response.output ?? response.data;

    if (!deployment) {
-      emitBare(`No data returned for ${deployedModel}`);
+      emitResult({ deployed_model: deployedModel, request_id: response.request_id }, "json");
      return;
    }

@@ -57,18 +56,6 @@ export default defineCommand({
    if (deployment.gmt_create) item.created_at = deployment.gmt_create;
    if (deployment.gmt_modified) item.updated_at = deployment.gmt_modified;

-    if (format === "json") {
-      emitResult({ ...item, request_id: response.request_id }, format);
-      return;
-    }
-
-    // text / quiet — fixed-width label column for alignment
-    const label = (key: string) => `${key}:`.padEnd(18);
-    for (const [key, value] of Object.entries(item)) {
-      if (value === "" || value === undefined) continue;
-      const display = typeof value === "string" ? value : JSON.stringify(value);
-      emitBare(`${label(key)}${display}`);
-    }
-    emitRequestId(response.request_id, settings.quiet);
+    emitResult({ ...item, request_id: response.request_id }, "json");
  },
 });
@@ -1,10 +1,5 @@
-import {
-  defineCommand,
-  detectOutputFormat,
-  listDeployments,
-  type FlagsDef,
-} from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId, formatTable } from "bailian-cli-runtime";
+import { defineCommand, listDeployments, type FlagsDef } from "bailian-cli-core";
+import { emitResult } from "bailian-cli-runtime";

 const LIST_FLAGS = {
  page: { type: "number", valueHint: "<n>", description: "Page number (default: 1)" },
@@ -28,13 +23,12 @@ export default defineCommand({
  exampleArgs: ["", "--status RUNNING", "--page-size 20 --output json"],
  async run(ctx) {
    const { settings, flags } = ctx;
-    const format = detectOutputFormat(settings.output);
    const status = flags.status || undefined;

    if (settings.dryRun) {
      emitResult(
        { action: "deploy.list", page: flags.page, page_size: flags.pageSize, status },
-        format,
+        "json",
      );
      return;
    }
@@ -57,27 +51,6 @@ export default defineCommand({
      created_at: item.gmt_create ?? "",
    }));

-    if (format === "json") {
-      emitResult({ items, total, request_id: response.request_id }, format);
-      return;
-    }
-
-    // text / quiet
-    if (items.length === 0) {
-      emitBare("No deployments found.");
-      return;
-    }
-    const headers = ["DEPLOYED_MODEL", "MODEL_NAME", "STATUS", "PLAN", "CAPACITY", "CREATED_AT"];
-    const rows = items.map((item) => [
-      item.deployed_model,
-      item.model_name,
-      item.status,
-      item.plan,
-      item.capacity,
-      item.created_at,
-    ]);
-    for (const line of formatTable(headers, rows)) emitBare(line);
-    if (total !== undefined) emitBare(`\nTotal: ${total}`);
-    emitRequestId(response.request_id, settings.quiet);
+    emitResult({ items, total, request_id: response.request_id }, "json");
  },
 });
@@ -1,10 +1,5 @@
-import {
-  defineCommand,
-  detectOutputFormat,
-  listDeployableModels,
-  type FlagsDef,
-} from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId, formatTable } from "bailian-cli-runtime";
+import { defineCommand, listDeployableModels, type FlagsDef } from "bailian-cli-core";
+import { emitResult } from "bailian-cli-runtime";

 const MODELS_FLAGS = {
  page: { type: "number", valueHint: "<n>", description: "Page number (default: 1)" },
@@ -39,7 +34,6 @@ export default defineCommand({
  ],
  async run(ctx) {
    const { settings, flags } = ctx;
-    const format = detectOutputFormat(settings.output);
    // Default version to v1.0 — without it, the API returns the legacy catalog
    // (only old fine-tune outputs). Pass --catalog-version "" to opt out.
    const version = flags.catalogVersion === "" ? undefined : (flags.catalogVersion ?? "v1.0");
@@ -54,7 +48,7 @@ export default defineCommand({
          version,
          model_source: modelSource,
        },
-        format,
+        "json",
      );
      return;
    }
@@ -72,102 +66,55 @@ export default defineCommand({
    // Two response shapes:
    //   - custom (fine-tuned): top-level supported_plans: string[]
    //   - base (catalog):      plans: [{plan, templates?, cu_specs?}]
-    // For json: surface the deployment-relevant fields preserved as a tree, so
+    // Surface the deployment-relevant fields preserved as a tree, so
    // downstream tooling can drive `bl deploy <modality> create --deploy-spec <…>`
-    // without a second round-trip. For text: keep the compact one-line summary.
-    if (format === "json") {
-      const items = models.map((model) => {
-        const out: Record<string, unknown> = {
-          model_name: model.model_name ?? "",
-        };
-        if (model.base_model) out.base_model = model.base_model;
-        if (model.model_source) out.model_source = model.model_source;
-        if (model.supported_plans && model.supported_plans.length > 0) {
-          out.supported_plans = model.supported_plans;
-        }
-        if (model.plans && model.plans.length > 0) {
-          out.plans = model.plans.map((plan) => {
-            const planEntry: Record<string, unknown> = { plan: plan.plan ?? "" };
-            if (plan.cu_specs && plan.cu_specs.length > 0) {
-              planEntry.cu_specs = plan.cu_specs;
-            }
-            if (plan.templates && plan.templates.length > 0) {
-              // Pull the top 6 fields most useful for `bl deploy <modality> create`.
-              // Drop noisy/redundant: template_source, template_type,
-              // template_version, deploy_spec (typically == template_id).
-              planEntry.templates = plan.templates.map((template) => {
-                const tpl: Record<string, unknown> = {};
-                if (template.template_id) tpl.template_id = template.template_id;
-                if (template.template_name) tpl.template_name = template.template_name;
-                if (template.charge_type) tpl.charge_type = template.charge_type;
-                // Flatten roles.unified for the common COUPLED case.
-                const unified = template.roles?.unified;
-                if (unified?.model_unit_spec) tpl.model_unit_spec = unified.model_unit_spec;
-                if (unified?.capacity_unit_per_instance !== undefined)
-                  tpl.capacity_unit_per_instance = unified.capacity_unit_per_instance;
-                // Preserve split-role configs (SEPERATED) as-is so callers
-                // can still drive prefill/decode sizing.
-                if (template.roles?.prefill || template.roles?.decode) {
-                  tpl.roles = {
-                    prefill: template.roles?.prefill,
-                    decode: template.roles?.decode,
-                  };
-                }
-                if (template.template_desc) tpl.template_desc = template.template_desc;
-                return tpl;
-              });
-            }
-            return planEntry;
-          });
-        }
-        return out;
-      });
-      emitResult({ items, total, request_id: response.request_id }, format);
-      return;
-    }
-
-    // text / quiet — keep the compact single-line summary table.
-    const textItems = models.map((model) => {
-      let plansSummary = "";
-      if (model.supported_plans && model.supported_plans.length > 0) {
-        plansSummary = model.supported_plans.join(",");
-      } else if (model.plans && model.plans.length > 0) {
-        plansSummary = model.plans
-          .map((plan) => {
-            const planName = plan.plan ?? "?";
-            if (plan.templates && plan.templates.length > 0) {
-              return `${planName}(${plan.templates.length}t)`;
-            }
-            if (plan.cu_specs && plan.cu_specs.length > 0) {
-              return `${planName}(${plan.cu_specs.join("/")})`;
-            }
-            return planName;
-          })
-          .join(",");
-      } else {
-        plansSummary = "-";
-      }
-      return {
+    // without a second round-trip.
+    const items = models.map((model) => {
+      const out: Record<string, unknown> = {
        model_name: model.model_name ?? "",
-        base_model: model.base_model ?? "",
-        source: model.model_source ?? "",
-        plans: plansSummary,
      };
+      if (model.base_model) out.base_model = model.base_model;
+      if (model.model_source) out.model_source = model.model_source;
+      if (model.supported_plans && model.supported_plans.length > 0) {
+        out.supported_plans = model.supported_plans;
+      }
+      if (model.plans && model.plans.length > 0) {
+        out.plans = model.plans.map((plan) => {
+          const planEntry: Record<string, unknown> = { plan: plan.plan ?? "" };
+          if (plan.cu_specs && plan.cu_specs.length > 0) {
+            planEntry.cu_specs = plan.cu_specs;
+          }
+          if (plan.templates && plan.templates.length > 0) {
+            // Pull the top 6 fields most useful for `bl deploy <modality> create`.
+            // Drop noisy/redundant: template_source, template_type,
+            // template_version, deploy_spec (typically == template_id).
+            planEntry.templates = plan.templates.map((template) => {
+              const tpl: Record<string, unknown> = {};
+              if (template.template_id) tpl.template_id = template.template_id;
+              if (template.template_name) tpl.template_name = template.template_name;
+              if (template.charge_type) tpl.charge_type = template.charge_type;
+              // Flatten roles.unified for the common COUPLED case.
+              const unified = template.roles?.unified;
+              if (unified?.model_unit_spec) tpl.model_unit_spec = unified.model_unit_spec;
+              if (unified?.capacity_unit_per_instance !== undefined)
+                tpl.capacity_unit_per_instance = unified.capacity_unit_per_instance;
+              // Preserve split-role configs (SEPERATED) as-is so callers
+              // can still drive prefill/decode sizing.
+              if (template.roles?.prefill || template.roles?.decode) {
+                tpl.roles = {
+                  prefill: template.roles?.prefill,
+                  decode: template.roles?.decode,
+                };
+              }
+              if (template.template_desc) tpl.template_desc = template.template_desc;
+              return tpl;
+            });
+          }
+          return planEntry;
+        });
+      }
+      return out;
    });
-
-    if (textItems.length === 0) {
-      emitBare("No deployable models found.");
-      return;
-    }
-    const headers = ["MODEL_NAME", "BASE_MODEL", "SOURCE", "PLANS"];
-    const rows = textItems.map((item) => [
-      item.model_name,
-      item.base_model,
-      item.source,
-      item.plans,
-    ]);
-    for (const line of formatTable(headers, rows)) emitBare(line);
-    if (total !== undefined) emitBare(`\nTotal: ${total}`);
-    emitRequestId(response.request_id, settings.quiet);
+    emitResult({ items, total, request_id: response.request_id }, "json");
  },
 });
@@ -0,0 +1,85 @@
+import {
+  defineCommand,
+  stopModelService,
+  listIndependentDeployedModels,
+  findDeploymentEntry,
+  BailianError,
+  ExitCode,
+  type FlagsDef,
+} from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";
+
+const PAUSE_FLAGS = {
+  deployedModel: {
+    type: "string",
+    valueHint: "<id>",
+    description: "Deployed model identifier (required)",
+    required: true,
+  },
+  skipPrecheck: {
+    type: "switch",
+    description: "Skip the local RUNNING/PENDING status precheck",
+  },
+} satisfies FlagsDef;
+
+/**
+ * `bl deploy pause` — pause a running deployment.
+ *
+ * Takes the model service offline so it no longer serves inference requests.
+ * For mu/ptu plans, billing stops while paused.
+ * Precheck: status must be RUNNING or PENDING.
+ */
+export default defineCommand({
+  description: "Pause a running model deployment (stops billing for mu/ptu)",
+  auth: "console",
+  usageArgs: "--deployed-model <id> [--skip-precheck]",
+  flags: PAUSE_FLAGS,
+  exampleArgs: [
+    "--deployed-model dep-...",
+    "--deployed-model dep-... --skip-precheck",
+    "--deployed-model dep-... --dry-run",
+  ],
+  notes: [
+    "While paused, billing ceases for mu/ptu plans. Use `deploy resume` to bring it back online or `deploy delete` to remove.",
+    "Precheck verifies status is RUNNING/PENDING before issuing the pause; pass --skip-precheck to bypass.",
+  ],
+  async run(ctx) {
+    const { settings, flags } = ctx;
+    const deployedModel = flags.deployedModel;
+
+    if (settings.dryRun) {
+      emitResult({ action: "deploy.pause", deployed_model: deployedModel }, "json");
+      return;
+    }
+
+    // Precheck: verify the deployment is in a pausable state.
+    if (!flags.skipPrecheck) {
+      try {
+        const entries = await listIndependentDeployedModels(ctx.client);
+        const entry = findDeploymentEntry(entries, deployedModel);
+        if (entry) {
+          const status = (entry.status ?? "").toUpperCase();
+          if (status && status !== "RUNNING" && status !== "PENDING") {
+            throw new BailianError(
+              `Deployment ${deployedModel} is ${status}. Only RUNNING / PENDING deployments can be paused. ` +
+                `Pass --skip-precheck to attempt the pause anyway.`,
+              ExitCode.USAGE,
+            );
+          }
+        }
+        // If entry not found in list, proceed — the server will surface the real error.
+      } catch (error) {
+        if (error instanceof BailianError) throw error;
+        // If the list call itself failed, proceed and let the API call surface the error.
+      }
+    }
+
+    const response = await stopModelService(ctx.client, deployedModel);
+
+    if (settings.quiet) {
+      emitBare(deployedModel);
+    } else {
+      emitResult({ deployed_model: deployedModel, action: "pause", ...response }, "json");
+    }
+  },
+});
@@ -0,0 +1,84 @@
+import {
+  defineCommand,
+  startModelService,
+  listIndependentDeployedModels,
+  findDeploymentEntry,
+  BailianError,
+  ExitCode,
+  type FlagsDef,
+} from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";
+
+const RESUME_FLAGS = {
+  deployedModel: {
+    type: "string",
+    valueHint: "<id>",
+    description: "Deployed model identifier (required)",
+    required: true,
+  },
+  skipPrecheck: {
+    type: "switch",
+    description: "Skip the local STOPPED status precheck",
+  },
+} satisfies FlagsDef;
+
+/**
+ * `bl deploy resume` — resume a paused deployment.
+ *
+ * Brings the model service back online so it can serve inference requests.
+ * Precheck: status must be STOPPED.
+ */
+export default defineCommand({
+  description: "Resume a paused model deployment (brings service back online)",
+  auth: "console",
+  usageArgs: "--deployed-model <id> [--skip-precheck]",
+  flags: RESUME_FLAGS,
+  exampleArgs: [
+    "--deployed-model dep-...",
+    "--deployed-model dep-... --skip-precheck",
+    "--deployed-model dep-... --dry-run",
+  ],
+  notes: [
+    "Precheck verifies status is STOPPED before issuing the resume; pass --skip-precheck to bypass.",
+    "For mu/ptu plans, billing resumes once the service is back online.",
+  ],
+  async run(ctx) {
+    const { settings, flags } = ctx;
+    const deployedModel = flags.deployedModel;
+
+    if (settings.dryRun) {
+      emitResult({ action: "deploy.resume", deployed_model: deployedModel }, "json");
+      return;
+    }
+
+    // Precheck: verify the deployment is in a resumable state.
+    if (!flags.skipPrecheck) {
+      try {
+        const entries = await listIndependentDeployedModels(ctx.client);
+        const entry = findDeploymentEntry(entries, deployedModel);
+        if (entry) {
+          const status = (entry.status ?? "").toUpperCase();
+          if (status && status !== "STOPPED") {
+            throw new BailianError(
+              `Deployment ${deployedModel} is ${status}. Only STOPPED deployments can be resumed. ` +
+                `Pass --skip-precheck to attempt the resume anyway.`,
+              ExitCode.USAGE,
+            );
+          }
+        }
+        // If entry not found in list, proceed — the server will surface the real error.
+      } catch (error) {
+        if (error instanceof BailianError) throw error;
+        // If the list call itself failed, proceed and let the API call surface the error.
+      }
+    }
+
+    const response = await startModelService(ctx.client, deployedModel);
+
+    if (settings.quiet) {
+      emitBare(deployedModel);
+    } else {
+      emitResult({ deployed_model: deployedModel, action: "resume", ...response }, "json");
+    }
+  },
+});
@@ -1,10 +1,5 @@
-import {
-  defineCommand,
-  detectOutputFormat,
-  scaleDeployment,
-  type FlagsDef,
-} from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, scaleDeployment, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const SCALE_FLAGS = {
  deployedModel: {
@@ -52,7 +47,6 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const deployedModel = flags.deployedModel;
-    const format = detectOutputFormat(settings.output);

    const body: Record<string, unknown> = {};
    if (flags.capacity !== undefined) body.capacity = flags.capacity;
@@ -60,21 +54,16 @@ export default defineCommand({
    if (flags.outputTpm !== undefined) body.output_tpm = flags.outputTpm;

    if (settings.dryRun) {
-      emitResult({ action: "deploy.scale", deployed_model: deployedModel, body }, format);
+      emitResult({ action: "deploy.scale", deployed_model: deployedModel, body }, "json");
      return;
    }

    const response = await scaleDeployment(ctx.client, deployedModel, body);
-    const deployment = response.output ?? response.data;

    if (settings.quiet) {
      emitBare(deployedModel);
-    } else if (format === "text") {
-      const cap = deployment?.capacity !== undefined ? ` (capacity=${deployment.capacity})` : "";
-      emitBare(`Scaled ${deployedModel}${cap}.`);
-      emitRequestId(response.request_id, settings.quiet);
    } else {
-      emitResult(response, format);
+      emitResult(response, "json");
    }
  },
 });
@@ -1,10 +1,5 @@
-import {
-  defineCommand,
-  detectOutputFormat,
-  updateDeployment,
-  type FlagsDef,
-} from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, updateDeployment, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const UPDATE_FLAGS = {
  deployedModel: {
@@ -48,31 +43,22 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const deployedModel = flags.deployedModel;
-    const format = detectOutputFormat(settings.output);

    const body: Record<string, unknown> = {};
    if (flags.rpmLimit !== undefined) body.rpm_limit = flags.rpmLimit;
    if (flags.tpmLimit !== undefined) body.tpm_limit = flags.tpmLimit;

    if (settings.dryRun) {
-      emitResult({ action: "deploy.update", deployed_model: deployedModel, body }, format);
+      emitResult({ action: "deploy.update", deployed_model: deployedModel, body }, "json");
      return;
    }

    const response = await updateDeployment(ctx.client, deployedModel, body);
-    const deployment = response.output ?? response.data;

    if (settings.quiet) {
      emitBare(deployedModel);
-    } else if (format === "text") {
-      const parts: string[] = [];
-      if (deployment?.rpm_limit !== undefined) parts.push(`rpm_limit=${deployment.rpm_limit}`);
-      if (deployment?.tpm_limit !== undefined) parts.push(`tpm_limit=${deployment.tpm_limit}`);
-      const summary = parts.length ? ` (${parts.join(", ")})` : "";
-      emitBare(`Updated ${deployedModel}${summary}.`);
-      emitRequestId(response.request_id, settings.quiet);
    } else {
-      emitResult(response, format);
+      emitResult(response, "json");
    }
  },
 });
@@ -1,5 +1,5 @@
-import { defineCommand, detectOutputFormat, cancelFineTune, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, cancelFineTune, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const CANCEL_FLAGS = {
  jobId: {
@@ -23,24 +23,18 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const jobId = flags.jobId;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "finetune.cancel", job_id: jobId }, format);
+      emitResult({ action: "finetune.cancel", job_id: jobId }, "json");
      return;
    }

    const response = await cancelFineTune(ctx.client, jobId);
-    const job = response.output ?? response.data;

    if (settings.quiet) {
      emitBare(jobId);
-    } else if (format === "text") {
-      const status = job?.status ? ` (status=${job.status})` : "";
-      emitBare(`Cancelled ${jobId}${status}.`);
-      emitRequestId(response.request_id, settings.quiet);
    } else {
-      emitResult(response, format);
+      emitResult(response, "json");
    }
  },
 });
@@ -1,6 +1,5 @@
 import {
  defineCommand,
-  detectOutputFormat,
  fetchModelList,
  fetchModelCapability,
  listSupportedTrainingTypes,
@@ -43,19 +42,8 @@ async function fetchAllFoundationModels(settings: Settings): Promise<ModelCapabi
  return all as ModelCapability[];
 }

-const VARIANT_LABEL: Record<string, string> = {
-  full: "full-parameter",
-  lora: "LoRA",
-};
-
-function describeTrainingType(value: string): string {
-  if (!isTrainingTypeCli(value)) return value;
-  const { method, variant } = trainingTypeMethodVariant(value);
-  return `${VARIANT_LABEL[variant] ?? variant} ${method.toUpperCase()}`;
-}
-
 const CAPABILITY_FLAGS = {
-  model: {
+  baseModel: {
    type: "string",
    valueHint: "<m>",
    description: "List training types supported by this base model.",
@@ -71,31 +59,31 @@ export default defineCommand({
  description:
    "Query fine-tune training capability — by model (which training types it supports) or by training type (which models support it)",
  auth: "none",
-  usageArgs: "--model <m> | --training-type <t>",
+  usageArgs: "--base-model <m> | --training-type <t>",
  flags: CAPABILITY_FLAGS,
  exampleArgs: [
-    "--model qwen3-8b",
+    "--base-model qwen3-8b",
    "--training-type sft-lora",
    "--training-type cpt --output json",
    "--training-type sft --quiet",
  ],
  notes: [
-    "Exactly one of --model / --training-type is required.",
+    "Exactly one of --base-model / --training-type is required.",
    "Training-type values use the `<method>` / `<method>-lora` convention:",
    "sft | sft-lora | dpo | dpo-lora | cpt. (cpt has no -lora variant server-side.)",
    "Queries listFoundationModels, a public API — no console login needed.",
  ],
  validate: (f) => {
-    if (f.model && f.trainingType)
-      return "--model and --training-type are mutually exclusive; pass one.";
-    if (!f.model && !f.trainingType) return "one of --model / --training-type is required.";
+    if (f.baseModel && f.trainingType)
+      return "--base-model and --training-type are mutually exclusive; pass one.";
+    if (!f.baseModel && !f.trainingType)
+      return "one of --base-model / --training-type is required.";
    return undefined;
  },
  async run(ctx) {
    const { settings, flags } = ctx;
-    const model = flags.model || undefined;
+    const model = flags.baseModel || undefined;
    const trainingType = flags.trainingType || undefined;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
      emitResult(
@@ -104,7 +92,7 @@ export default defineCommand({
          model,
          training_type: trainingType,
        },
-        format,
+        "json",
      );
      return;
    }
@@ -113,7 +101,7 @@ export default defineCommand({
    if (model) {
      const capability = await fetchModelCapability(settings, model);
      if (!capability) {
-        emitBare(`No foundation model found matching "${model}".`);
+        emitResult({ model, error: `No foundation model found matching "${model}".` }, "json");
        return;
      }
      const supported = listSupportedTrainingTypes(capability);
@@ -121,23 +109,15 @@ export default defineCommand({
        for (const value of supported) emitBare(value);
        return;
      }
-      if (format !== "text") {
-        emitResult(
-          {
-            model: capability.model ?? model,
-            supported,
-            supports: capability.supports,
-            trainingTypes: capability.trainingTypes,
-          },
-          format,
-        );
-        return;
-      }
-      emitBare(`${capability.model ?? model}`);
-      emitBare(supported.length ? "Supported training types:" : "No supported training types.");
-      for (const value of supported) {
-        emitBare(`  ${value.padEnd(10)} ${describeTrainingType(value)}`);
-      }
+      emitResult(
+        {
+          model: capability.model ?? model,
+          supported,
+          supports: capability.supports,
+          trainingTypes: capability.trainingTypes,
+        },
+        "json",
+      );
      return;
    }

@@ -162,20 +142,15 @@ export default defineCommand({
      for (const entry of matched) emitBare(entry.model);
      return;
    }
-    if (format !== "text") {
-      emitResult(
-        {
-          training_type: trainingType,
-          method,
-          variant,
-          count: matched.length,
-          models: matched,
-        },
-        format,
-      );
-      return;
-    }
-    emitBare(`Models supporting ${trainingType} (${method} / ${variant}): ${matched.length}`);
-    for (const entry of matched) emitBare(`  ${entry.model}`);
+    emitResult(
+      {
+        training_type: trainingType,
+        method,
+        variant,
+        count: matched.length,
+        models: matched,
+      },
+      "json",
+    );
  },
 });
@@ -1,10 +1,5 @@
-import {
-  defineCommand,
-  detectOutputFormat,
-  listCheckpoints,
-  type FlagsDef,
-} from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId, formatTable } from "bailian-cli-runtime";
+import { defineCommand, listCheckpoints, type FlagsDef } from "bailian-cli-core";
+import { emitResult } from "bailian-cli-runtime";

 const CHECKPOINTS_FLAGS = {
  jobId: {
@@ -15,6 +10,8 @@ const CHECKPOINTS_FLAGS = {
  },
 } satisfies FlagsDef;

+const EXPIRY_WARN_THRESHOLD_MS = 72 * 60 * 60 * 1000; // 72 hours
+
 export default defineCommand({
  description: "List checkpoints produced by a fine-tune job",
  auth: "apiKey",
@@ -22,16 +19,15 @@ export default defineCommand({
  flags: CHECKPOINTS_FLAGS,
  exampleArgs: ["--job-id ft-xxx", "--job-id ft-xxx --output json"],
  notes: [
-    "Use the returned `checkpoint` value with `finetune export` to publish",
-    "a deployable model.",
+    "`model_name` (shown for SUCCEEDED checkpoints) is the direct input for `deploy create --model-name`.",
+    "Checkpoints expire ~15 days after creation; `expire_time` shows the deadline. Export or deploy before expiry.",
  ],
  async run(ctx) {
    const { settings, flags } = ctx;
    const jobId = flags.jobId;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "finetune.checkpoints", job_id: jobId }, format);
+      emitResult({ action: "finetune.checkpoints", job_id: jobId }, "json");
      return;
    }

@@ -44,22 +40,26 @@ export default defineCommand({
      checkpoint: item.checkpoint ?? item.checkpoint_id ?? "",
      step: item.step !== undefined ? String(item.step) : "",
      status: item.status ?? "",
+      model_name: item.model_name ?? "",
+      expire_time: item.expire_time ?? "",
    }));

-    if (format === "json") {
-      emitResult({ items, total, request_id: response.request_id }, format);
-      return;
-    }
+    emitResult({ items, total, request_id: response.request_id }, "json");

-    // text / quiet
-    if (items.length === 0) {
-      emitBare("No checkpoints found.");
-      return;
+    // Near-expiry warning: check if any non-expired checkpoint is within 72h of expiry.
+    const now = Date.now();
+    const expiringSoon = items.filter((item) => {
+      if (!item.expire_time) return false;
+      const deadline = new Date(item.expire_time).getTime();
+      if (Number.isNaN(deadline)) return false;
+      const remaining = deadline - now;
+      return remaining > 0 && remaining < EXPIRY_WARN_THRESHOLD_MS;
+    });
+    if (expiringSoon.length > 0) {
+      process.stderr.write(
+        `\n[warning] ${expiringSoon.length} checkpoint(s) will expire within 72 hours. ` +
+          "Export or deploy before expiry to avoid losing the model artifact.\n",
+      );
    }
-    const headers = ["CHECKPOINT", "STEP", "STATUS"];
-    const rows = items.map((i) => [i.checkpoint, i.step, i.status]);
-    for (const line of formatTable(headers, rows)) emitBare(line);
-    emitBare(`\nTotal: ${total}`);
-    emitRequestId(response.request_id, settings.quiet);
  },
 });
@@ -1,6 +1,5 @@
 import {
  defineCommand,
-  detectOutputFormat,
  createFineTune,
  getDataset,
  uploadDataset,
@@ -27,7 +26,7 @@ import {
 } from "bailian-cli-core";
 import { existsSync, statSync } from "fs";
 import { basename } from "path";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 /**
 * A `--datasets` / `--validations` token is treated as a local file to upload
@@ -208,7 +207,7 @@ async function uploadResolvedLocal(
 }

 /** The modality a `finetune <modality> create` subcommand is bound to. */
-type CommandModality = "text" | "audio" | "image";
+type CommandModality = "text" | "audio" | "image" | "video";

 /**
 * Flags shared by every `finetune <modality> create` subcommand: what to train
@@ -216,10 +215,10 @@ type CommandModality = "text" | "audio" | "image";
 * output. Every modality's model consumes these.
 */
 const COMMON_FLAGS = {
-  model: {
+  baseModel: {
    type: "string",
    valueHint: "<model>",
-    description: "Base model to fine-tune",
+    description: "Base model to fine-tune (e.g. qwen3-8b; not the output model name)",
    required: true,
  },
  datasets: {
@@ -317,13 +316,41 @@ const IMAGE_FLAGS = {
 } satisfies FlagsDef;

 const TEXT_USAGE =
-  "--model <model> --datasets <id|path,...> [--validations <id|path,...>] [--model-name <name>] [--suffix <text>] [--n-epochs <n>] [--batch-size <n>] [--learning-rate <str>] [--max-length <n>] [--training-type <sft|sft-lora|dpo|dpo-lora|cpt>]";
+  "--base-model <model> --datasets <id|path,...> [--validations <id|path,...>] [--model-name <name>] [--suffix <text>] [--n-epochs <n>] [--batch-size <n>] [--learning-rate <str>] [--max-length <n>] [--training-type <sft|sft-lora|dpo|dpo-lora|cpt>]";

 const AUDIO_USAGE =
-  "--model <model> --datasets <id|path> [--validations <id|path>] [--model-name <name>] [--suffix <text>]";
+  "--base-model <model> --datasets <id|path> [--validations <id|path>] [--model-name <name>] [--suffix <text>]";

 const IMAGE_USAGE =
-  "--model <model> --datasets <id|path> [--validations <id|path>] [--model-name <name>] [--suffix <text>] [--generation-type <t2i|i2i>] [--learning-rate <str>]";
+  "--base-model <model> --datasets <id|path> [--validations <id|path>] [--model-name <name>] [--suffix <text>] [--generation-type <t2i|i2i>] [--learning-rate <str>]";
+
+/**
+ * Video (Wan i2v/kf2v) flags: exposes the three hyper-parameters that the
+ * video API supports and users may want to override. Defaults are model-specific
+ * (resolved by the sft-lora profile: wan2.7 → batch_size 1 / max_pixels 102400,
+ * wan2.5 → 4 / 36864, wan2.2 → 4 / 262144).
+ */
+const VIDEO_FLAGS = {
+  ...COMMON_FLAGS,
+  nEpochs: {
+    type: "number",
+    valueHint: "<n>",
+    description: "Training epochs (default: 50)",
+  },
+  batchSize: {
+    type: "number",
+    valueHint: "<n>",
+    description: "Batch size (default: model-specific, 1 for wan2.7, 4 for wan2.5/2.2)",
+  },
+  learningRate: {
+    type: "string",
+    valueHint: "<str>",
+    description: 'Learning rate as a string to preserve precision (default: "2e-5")',
+  },
+} satisfies FlagsDef;
+
+const VIDEO_USAGE =
+  "--base-model <model> --datasets <id|path> [--validations <id|path>] [--model-name <name>] [--suffix <text>] [--n-epochs <n>] [--batch-size <n>] [--learning-rate <str>]";

 const COMMON_NOTES = [
  "Creating a job uploads any local datasets and consumes training quota.",
@@ -383,7 +410,7 @@ async function runCreate<F extends FlagsDef>(
 ): Promise<void> {
  const { identity, settings } = ctx;
  const flags = ctx.flags as Record<string, unknown>;
-  const model = flags.model as string;
+  const model = flags.baseModel as string;
  const datasetsRaw = flags.datasets as string;

  // CosyVoice audio fine-tuning accepts exactly one training file
@@ -606,8 +633,6 @@ async function runCreate<F extends FlagsDef>(
  if (modelName) body.model_name = modelName;
  if (suffix) body.finetuned_output_suffix = suffix;

-  const format = detectOutputFormat(settings.output);
-
  if (settings.dryRun) {
    const pending = [
      ...training.localPaths.map((path) => ({ field: "datasets", path })),
@@ -617,7 +642,7 @@ async function runCreate<F extends FlagsDef>(
      pending.length > 0
        ? { action: "finetune.create", body, pending_uploads: pending }
        : { action: "finetune.create", body },
-      format,
+      "json",
    );
    return;
  }
@@ -627,16 +652,8 @@ async function runCreate<F extends FlagsDef>(

  if (settings.quiet) {
    if (job?.job_id) emitBare(job.job_id);
-  } else if (format === "text") {
-    if (job?.job_id) {
-      emitBare(`Created fine-tune job: ${job.job_id}`);
-      if (job.status) emitBare(`Status: ${job.status}`);
-      emitRequestId(response.request_id, settings.quiet);
-    } else {
-      emitResult(response, format);
-    }
  } else {
-    emitResult(response, format);
+    emitResult(response, "json");
  }
 }

@@ -647,14 +664,14 @@ export const finetuneTextCreate = defineCommand({
  usageArgs: TEXT_USAGE,
  flags: TEXT_FLAGS,
  exampleArgs: [
-    "--model qwen3-8b --datasets file-xxx",
-    "--model qwen3-8b --datasets ./train.jsonl",
-    "--model qwen3-8b --datasets ./train.jsonl --validations ./eval.jsonl",
-    "--model qwen3-8b --datasets file-aaa,./extra.jsonl",
-    "--model qwen3-8b --datasets ./train.jsonl --training-type sft",
-    '--model qwen3-8b --datasets file-xxx --learning-rate "1.6e-5" --n-epochs 4',
-    "--model qwen3-8b --datasets file-xxx --output json",
-    "--model qwen3-8b --datasets file-xxx --dry-run",
+    "--base-model qwen3-8b --datasets file-xxx",
+    "--base-model qwen3-8b --datasets ./train.jsonl",
+    "--base-model qwen3-8b --datasets ./train.jsonl --validations ./eval.jsonl",
+    "--base-model qwen3-8b --datasets file-aaa,./extra.jsonl",
+    "--base-model qwen3-8b --datasets ./train.jsonl --training-type sft",
+    '--base-model qwen3-8b --datasets file-xxx --learning-rate "1.6e-5" --n-epochs 4',
+    "--base-model qwen3-8b --datasets file-xxx --output json",
+    "--base-model qwen3-8b --datasets file-xxx --dry-run",
  ],
  notes: TEXT_NOTES,
  run: (ctx) => runCreate("text", ctx),
@@ -667,11 +684,11 @@ export const finetuneAudioCreate = defineCommand({
  usageArgs: AUDIO_USAGE,
  flags: AUDIO_FLAGS,
  exampleArgs: [
-    "--model cosyvoice-v3-flash --datasets ./audio.zip",
-    "--model cosyvoice-v3-flash --datasets file-xxx",
-    "--model cosyvoice-v3-flash --datasets ./audio.zip --model-name my-tts",
-    "--model cosyvoice-v3-flash --datasets file-xxx --output json",
-    "--model cosyvoice-v3-flash --datasets ./audio.zip --dry-run",
+    "--base-model cosyvoice-v3-flash --datasets ./audio.zip",
+    "--base-model cosyvoice-v3-flash --datasets file-xxx",
+    "--base-model cosyvoice-v3-flash --datasets ./audio.zip --model-name my-tts",
+    "--base-model cosyvoice-v3-flash --datasets file-xxx --output json",
+    "--base-model cosyvoice-v3-flash --datasets ./audio.zip --dry-run",
  ],
  notes: AUDIO_NOTES,
  run: (ctx) => runCreate("audio", ctx),
@@ -684,13 +701,38 @@ export const finetuneImageCreate = defineCommand({
  usageArgs: IMAGE_USAGE,
  flags: IMAGE_FLAGS,
  exampleArgs: [
-    "--model wan2.7-image-pro --datasets ./images.zip",
-    "--model wan2.7-image-pro --datasets file-xxx",
-    "--model wan2.7-image-pro --datasets file-xxx --generation-type i2i",
-    "--model wan2.7-image-pro --datasets ./images.zip --model-name my-wan",
-    "--model wan2.7-image-pro --datasets file-xxx --output json",
-    "--model wan2.7-image-pro --datasets ./images.zip --dry-run",
+    "--base-model wan2.7-image-pro --datasets ./images.zip",
+    "--base-model wan2.7-image-pro --datasets file-xxx",
+    "--base-model wan2.7-image-pro --datasets file-xxx --generation-type i2i",
+    "--base-model wan2.7-image-pro --datasets ./images.zip --model-name my-wan",
+    "--base-model wan2.7-image-pro --datasets file-xxx --output json",
+    "--base-model wan2.7-image-pro --datasets ./images.zip --dry-run",
  ],
  notes: IMAGE_NOTES,
  run: (ctx) => runCreate("image", ctx),
 });
+
+const VIDEO_NOTES = [
+  ...COMMON_NOTES,
+  "Video generation training (Wan i2v/kf2v) runs efficient_sft with model-",
+  "specific defaults: wan2.7 (batch_size=1, max_pixels=102400), wan2.5/2.2",
+  "(batch_size=4, max_pixels per model). Override with --batch-size/--n-epochs.",
+  "Datasets are .zip archives with data.jsonl + frame images + videos.",
+  "Recommended: ≥10 training samples, 20-100 for stable results.",
+];
+
+/** `bl finetune video create` — fine-tune a video generation model. Datasets are `.zip`. */
+export const finetuneVideoCreate = defineCommand({
+  description: "Create a video generation model fine-tune job (Wan i2v/kf2v, efficient_sft)",
+  auth: "apiKey",
+  usageArgs: VIDEO_USAGE,
+  flags: VIDEO_FLAGS,
+  exampleArgs: [
+    "--base-model wan2.7-i2v --datasets file-xxx",
+    "--base-model wan2.7-i2v --datasets ./i2v-data.zip",
+    "--base-model wan2.2-kf2v-flash --datasets file-xxx --n-epochs 100",
+    "--base-model wan2.7-i2v --datasets file-xxx --dry-run",
+  ],
+  notes: VIDEO_NOTES,
+  run: (ctx) => runCreate("video", ctx),
+});
@@ -1,5 +1,5 @@
-import { defineCommand, detectOutputFormat, deleteFineTune, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, deleteFineTune, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const DELETE_FLAGS = {
  jobId: {
@@ -23,10 +23,9 @@ export default defineCommand({
  async run(ctx) {
    const { settings, flags } = ctx;
    const jobId = flags.jobId;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "finetune.delete", job_id: jobId }, format);
+      emitResult({ action: "finetune.delete", job_id: jobId }, "json");
      return;
    }

@@ -34,11 +33,8 @@ export default defineCommand({

    if (settings.quiet) {
      emitBare(jobId);
-    } else if (format === "text") {
-      emitBare(`Deleted ${jobId}.`);
-      emitRequestId(response.request_id, settings.quiet);
    } else {
-      emitResult(response, format);
+      emitResult(response, "json");
    }
  },
 });
@@ -1,10 +1,5 @@
-import {
-  defineCommand,
-  detectOutputFormat,
-  exportCheckpoint,
-  type FlagsDef,
-} from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, exportCheckpoint, type FlagsDef } from "bailian-cli-core";
+import { emitResult, emitBare } from "bailian-cli-runtime";

 const EXPORT_FLAGS = {
  jobId: {
@@ -39,11 +34,10 @@ export default defineCommand({
    "explicit export is the canonical path for non-best checkpoints.",
  ],
  async run(ctx) {
-    const { identity, settings, flags } = ctx;
+    const { settings, flags } = ctx;
    const jobId = flags.jobId;
    const checkpoint = flags.checkpoint;
    const modelName = flags.modelName;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
      emitResult(
@@ -53,7 +47,7 @@ export default defineCommand({
          checkpoint,
          model_name: modelName,
        },
-        format,
+        "json",
      );
      return;
    }
@@ -64,14 +58,8 @@ export default defineCommand({

    if (settings.quiet) {
      emitBare(exported);
-    } else if (format === "text") {
-      emitBare(`Exported ${jobId} / ${checkpoint} → model_name=${exported}`);
-      emitBare(
-        `Next: ${identity.binName} deploy text create --model ${exported} --name <display-name>`,
-      );
-      emitRequestId(response.request_id, settings.quiet);
    } else {
-      emitResult(response, format);
+      emitResult(response, "json");
    }
  },
 });
@@ -0,0 +1,105 @@
+/**
+ * Best-effort actual training fee calculation using the model catalog's
+ * "ft" (fine-tune) price entry. Pure API-key domain — no console auth needed.
+ *
+ * The model catalog (`listFoundationModels` via public gateway) returns a
+ * `prices[]` array **only when `queryPrice: true` is passed** (the same flag
+ * `fetchModelDetail` uses). Combined with the job's `output.usage` (actual
+ * consumed tokens, present on SUCCEEDED / CANCELED), this gives the exact
+ * training cost without any console-domain login.
+ */
+import {
+  callConsoleGateway,
+  effectiveConsoleGatewayConfig,
+  unwrapResponse,
+  MODEL_LIST_API,
+  type Settings,
+  type ModelPriceInfo,
+} from "bailian-cli-core";
+
+export interface ActualFee {
+  cost: number;
+  unitPrice: number;
+  priceUnit: string;
+}
+
+/**
+ * Fetch the model's training price from the public catalog gateway.
+ * Uses the same anonymous gateway path as `fetchModelCapability` (no console
+ * token required), but adds `queryPrice: true` to include the prices array.
+ */
+async function fetchTrainingPrice(
+  settings: Settings,
+  model: string,
+): Promise<ModelPriceInfo | null> {
+  const eff = effectiveConsoleGatewayConfig(settings);
+  const result = await callConsoleGateway(
+    { region: eff.consoleRegion, site: eff.consoleSite, switchAgent: eff.consoleSwitchAgent },
+    settings.timeout,
+    {
+      api: MODEL_LIST_API,
+      data: {
+        input: {
+          pageNo: 1,
+          pageSize: 10,
+          group: true,
+          model,
+          queryPrice: true,
+          querySampleCode: false,
+          queryGroupByModel: true,
+          queryQuota: false,
+          queryQpmInfo: false,
+          queryApplyStatus: false,
+          queryPermissions: false,
+          queryActivationStatus: false,
+        },
+      },
+    },
+  );
+  const responseData = unwrapResponse(result as Record<string, unknown>);
+  const list = (responseData.list as Record<string, unknown>[]) ?? [];
+  // The response is grouped; find the exact model in items.
+  for (const group of list) {
+    const items = (group.items as Record<string, unknown>[]) ?? [];
+    for (const item of items) {
+      if (item.model === model) {
+        const prices = (item.prices as ModelPriceInfo[]) ?? [];
+        return prices.find((entry) => entry.type === "ft") ?? null;
+      }
+    }
+    // Flat response fallback (no items nesting).
+    if (group.model === model) {
+      const prices = (group.prices as ModelPriceInfo[]) ?? [];
+      return prices.find((entry) => entry.type === "ft") ?? null;
+    }
+  }
+  return null;
+}
+
+/**
+ * Compute the actual training fee from the model catalog's "ft" price entry.
+ * Returns null when the price is unavailable (network error, model not in
+ * catalog, or no "ft" entry). Never throws.
+ *
+ * Only uses the public model catalog (model metadata) — does NOT call
+ * console-domain pricing APIs (modelCenter.getModelPrice). Models whose
+ * catalog entry lacks a "ft" price (e.g. CosyVoice) will simply omit the
+ * training_cost field until the platform adds it to the catalog.
+ */
+export async function computeActualFee(
+  settings: Settings,
+  model: string,
+  usageTokens: number,
+): Promise<ActualFee | null> {
+  try {
+    const ftEntry = await fetchTrainingPrice(settings, model);
+    const unitPrice = Number(ftEntry?.price);
+    if (!Number.isFinite(unitPrice) || unitPrice <= 0) return null;
+    const priceUnit = ftEntry?.priceUnit ?? "每百万tokens";
+    // Catalog price is yuan per million tokens.
+    const cost = (usageTokens / 1_000_000) * unitPrice;
+    return { cost: Number(cost.toFixed(4)), unitPrice, priceUnit };
+  } catch {
+    return null;
+  }
+}
@@ -1,5 +1,6 @@
-import { defineCommand, detectOutputFormat, getFineTune, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { defineCommand, getFineTune, type FlagsDef } from "bailian-cli-core";
+import { emitResult } from "bailian-cli-runtime";
+import { computeActualFee } from "./fee.ts";

 const GET_FLAGS = {
  jobId: {
@@ -17,12 +18,11 @@ export default defineCommand({
  flags: GET_FLAGS,
  exampleArgs: ["--job-id ft-xxx", "--job-id ft-xxx --output json"],
  async run(ctx) {
-    const { identity, settings, flags } = ctx;
+    const { settings, flags } = ctx;
    const jobId = flags.jobId;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
-      emitResult({ action: "finetune.get", job_id: jobId }, format);
+      emitResult({ action: "finetune.get", job_id: jobId }, "json");
      return;
    }

@@ -30,18 +30,24 @@ export default defineCommand({
    const job = response.output ?? response.data;

    if (!job) {
-      emitBare(`No data returned for ${jobId}`);
+      emitResult({ job_id: jobId, error: "No data returned" }, "json");
      return;
    }

-    const hp = job.hyper_parameters;
+    const hyperParameters = job.hyper_parameters;
    const hyperParts: string[] = [];
-    if (hp?.n_epochs !== undefined) hyperParts.push(`n_epochs=${hp.n_epochs}`);
-    if (hp?.batch_size !== undefined) hyperParts.push(`batch_size=${hp.batch_size}`);
-    if (hp?.learning_rate !== undefined) hyperParts.push(`learning_rate=${hp.learning_rate}`);
-    if (hp?.max_length !== undefined) hyperParts.push(`max_length=${hp.max_length}`);
+    if (hyperParameters?.n_epochs !== undefined)
+      hyperParts.push(`n_epochs=${hyperParameters.n_epochs}`);
+    if (hyperParameters?.batch_size !== undefined)
+      hyperParts.push(`batch_size=${hyperParameters.batch_size}`);
+    if (hyperParameters?.learning_rate !== undefined)
+      hyperParts.push(`learning_rate=${hyperParameters.learning_rate}`);
+    if (hyperParameters?.max_length !== undefined)
+      hyperParts.push(`max_length=${hyperParameters.max_length}`);

-    const item = {
+    const usageTokens = typeof job.usage === "number" ? job.usage : undefined;
+
+    const item: Record<string, unknown> = {
      job_id: job.job_id ?? jobId,
      base_model: job.model ?? "",
      status: job.status ?? "",
@@ -53,29 +59,20 @@ export default defineCommand({
      model_name: job.model_name ?? "",
      created_at: job.create_time ?? job.gmt_create ?? "",
      updated_at: job.end_time ?? job.gmt_modified ?? "",
+      usage_tokens: usageTokens ?? "",
+      charge_type: typeof job.charge_type === "string" ? job.charge_type : "",
    };

-    if (format === "json") {
-      emitResult({ ...item, request_id: response.request_id }, format);
-      return;
+    // Actual fee: only when the platform reports a concrete token count
+    // (SUCCEEDED / CANCELED). Best-effort — silently omitted on lookup failure.
+    if (usageTokens !== undefined && usageTokens > 0 && job.model) {
+      const fee = await computeActualFee(settings, job.model, usageTokens);
+      if (fee) {
+        item.training_cost = fee.cost;
+        item.cost_basis = `${fee.unitPrice} 元/${fee.priceUnit}`;
+      }
    }

-    // text / quiet
-    emitBare(`job_id:           ${item.job_id}`);
-    if (item.base_model) emitBare(`base_model:       ${item.base_model}`);
-    if (item.status) emitBare(`status:           ${item.status}`);
-    if (item.training_type) emitBare(`training_type:    ${item.training_type}`);
-    if (item.training_files.length) emitBare(`training_files:   ${item.training_files.join(", ")}`);
-    if (item.validation_files.length)
-      emitBare(`validation_files: ${item.validation_files.join(", ")}`);
-    if (item.hyper_params) emitBare(`hyper_params:     ${item.hyper_params}`);
-    if (item.output_model)
-      emitBare(
-        `output_model:     ${item.output_model}  (→ ${identity.binName} deploy text create --model)`,
-      );
-    if (item.model_name) emitBare(`model_name:       ${item.model_name}`);
-    if (item.created_at) emitBare(`created_at:       ${item.created_at}`);
-    if (item.updated_at) emitBare(`updated_at:       ${item.updated_at}`);
-    emitRequestId(response.request_id, settings.quiet);
+    emitResult({ ...item, request_id: response.request_id }, "json");
  },
 });
@@ -1,5 +1,5 @@
-import { defineCommand, detectOutputFormat, listFineTunes, type FlagsDef } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId, formatTable } from "bailian-cli-runtime";
+import { defineCommand, listFineTunes, type FlagsDef } from "bailian-cli-core";
+import { emitResult } from "bailian-cli-runtime";

 const LIST_FLAGS = {
  page: { type: "number", valueHint: "<n>", description: "Page number (default: 1)" },
@@ -13,71 +13,48 @@ const LIST_FLAGS = {
    valueHint: "<s>",
    description: "Filter by status (PENDING / RUNNING / SUCCEEDED / FAILED / CANCELED)",
  },
+  baseModel: {
+    type: "string",
+    valueHint: "<model>",
+    description: "Filter by base model ID (server-side)",
+  },
 } satisfies FlagsDef;

 export default defineCommand({
  description: "List fine-tune jobs",
  auth: "apiKey",
-  usageArgs: "[--page <n>] [--page-size <n>] [--status <s>]",
+  usageArgs: "[--page <n>] [--page-size <n>] [--status <s>] [--base-model <model>]",
  flags: LIST_FLAGS,
-  exampleArgs: ["", "--status RUNNING", "--page-size 20 --output json"],
+  exampleArgs: ["", "--status RUNNING", "--base-model qwen3-8b", "--page-size 20"],
  async run(ctx) {
-    const { identity, settings, flags } = ctx;
-    const format = detectOutputFormat(settings.output);
+    const { settings, flags } = ctx;
    const pageNo = flags.page;
    const pageSize = flags.pageSize;
    const status = flags.status || undefined;
+    const model = flags.baseModel || undefined;

    if (settings.dryRun) {
-      emitResult({ action: "finetune.list", page: pageNo, page_size: pageSize, status }, format);
+      emitResult(
+        { action: "finetune.list", page: pageNo, page_size: pageSize, status, model },
+        "json",
+      );
      return;
    }

-    const response = await listFineTunes(ctx.client, { pageNo, pageSize, status });
+    const response = await listFineTunes(ctx.client, { pageNo, pageSize, status, model });
    const payload = response.output ?? response.data;
    const jobs = payload?.jobs ?? [];
    const total = payload?.total;

-    const items = jobs.map((item) => ({
-      job_id: item.job_id ?? "",
-      base_model: item.model ?? "",
-      status: item.status ?? "",
-      training_type: item.training_type ?? "",
-      output_model: item.finetuned_output ?? "",
-      created_at: item.create_time ?? item.gmt_create ?? "",
+    const items = jobs.map((job) => ({
+      job_id: job.job_id ?? "",
+      base_model: job.model ?? "",
+      status: job.status ?? "",
+      training_type: job.training_type ?? "",
+      output_model: job.finetuned_output ?? "",
+      created_at: job.create_time ?? job.gmt_create ?? "",
    }));

-    if (format === "json") {
-      emitResult({ items, total, request_id: response.request_id }, format);
-      return;
-    }
-
-    // text / quiet
-    if (items.length === 0) {
-      emitBare("No fine-tune jobs found.");
-      return;
-    }
-    const headers = [
-      "JOB_ID",
-      "BASE_MODEL",
-      "STATUS",
-      "TRAINING_TYPE",
-      "OUTPUT_MODEL",
-      "CREATED_AT",
-    ];
-    const rows = items.map((i) => [
-      i.job_id,
-      i.base_model,
-      i.status,
-      i.training_type,
-      i.output_model,
-      i.created_at,
-    ]);
-    for (const line of formatTable(headers, rows)) emitBare(line);
-    if (total !== undefined) emitBare(`\nTotal: ${total}`);
-    emitBare(
-      `Tip: OUTPUT_MODEL is the input for \`${identity.binName} deploy text create --model\``,
-    );
-    emitRequestId(response.request_id, settings.quiet);
+    emitResult({ items, total, request_id: response.request_id }, "json");
  },
 });
@@ -1,25 +1,24 @@
 import {
  defineCommand,
-  detectOutputFormat,
  getFineTuneLogs,
  type Client,
  type FineTuneLogEntry,
  type FlagsDef,
 } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { emitResult } from "bailian-cli-runtime";

 /**
- * Render a single log entry as a single line (mirrors the flatten logic used
- * for non-search text output: prefer common fields, fall back to JSON).
+ * Render a single log entry as a single line (used for search matching:
+ * prefer common fields, fall back to JSON).
 */
 function renderEntry(entry: FineTuneLogEntry | string): string {
  if (typeof entry === "string") return entry;
  const record = entry as Record<string, unknown>;
-  const ts = (record.timestamp ?? record.time ?? record.create_time ?? "") as string;
+  const timestamp = (record.timestamp ?? record.time ?? record.create_time ?? "") as string;
  const level = (record.level ?? "") as string;
-  const msg = (record.message ?? record.msg ?? record.log ?? "") as string;
-  if (msg || ts || level) {
-    return [ts, level, msg].filter(Boolean).join("\t");
+  const message = (record.message ?? record.msg ?? record.log ?? "") as string;
+  if (message || timestamp || level) {
+    return [timestamp, level, message].filter(Boolean).join("\t");
  }
  return JSON.stringify(entry);
 }
@@ -48,16 +47,16 @@ async function fetchAllLogs(
  let total = 0;
  // Hard cap to avoid an unbounded loop if the server misreports `total`.
  const maxPages = 200;
-  for (let i = 0; i < maxPages; i++) {
+  for (let page = 0; page < maxPages; page++) {
    const response = await getFineTuneLogs(client, jobId, { pageNo, pageSize });
    const payload = response.output ?? response.data;
-    const page = payload?.logs ?? [];
+    const logs = payload?.logs ?? [];
    total = payload?.total ?? total;
-    if (page.length === 0) break;
-    entries.push(...page);
+    if (logs.length === 0) break;
+    entries.push(...logs);
    // Stop once we've collected everything the server claims exists.
    if (total && entries.length >= total) break;
-    if (page.length < pageSize) break;
+    if (logs.length < pageSize) break;
    pageNo++;
  }
  return { entries, total };
@@ -110,7 +109,6 @@ export default defineCommand({
    const pageSize = flags.pageSize;
    const search = flags.search || undefined;
    const tail = flags.tail;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
      emitResult(
@@ -122,7 +120,7 @@ export default defineCommand({
          search,
          tail,
        },
-        format,
+        "json",
      );
      return;
    }
@@ -147,18 +145,6 @@ export default defineCommand({
      const result =
        tailApplied !== undefined ? scanned.slice(scanned.length - tailApplied) : scanned;

-      if (settings.quiet || format === "text") {
-        if (result.length === 0) {
-          emitBare(search ? `No logs matched "${search}".` : "No logs returned.");
-          return;
-        }
-        for (const entry of result) emitBare(renderEntry(entry));
-        const parts: string[] = [`${result.length} shown`];
-        if (matched !== undefined) parts.push(`matched ${matched}`);
-        parts.push(`of ${entries.length}` + (total ? ` (total ${total})` : ""));
-        emitBare(`\n${parts.join(", ")}`);
-        return;
-      }
      emitResult(
        {
          ...(matched !== undefined ? { matched } : {}),
@@ -168,28 +154,13 @@ export default defineCommand({
          ...(tailApplied !== undefined ? { tail: tailApplied } : {}),
          logs: result,
        },
-        format,
+        "json",
      );
      return;
    }

    // Default: single page, verbatim response.
    const response = await getFineTuneLogs(ctx.client, jobId, { pageNo, pageSize });
-    const payload = response.output ?? response.data;
-    const logs = payload?.logs ?? [];
-
-    if (settings.quiet || format === "text") {
-      if (logs.length === 0) {
-        emitBare("No logs returned.");
-        return;
-      }
-      for (const entry of logs) {
-        emitBare(renderEntry(entry));
-      }
-      if (payload?.total !== undefined) emitBare(`\nTotal: ${payload.total}`);
-      emitRequestId(response.request_id, settings.quiet);
-    } else {
-      emitResult(response, format);
-    }
+    emitResult(response, "json");
  },
 });
@@ -0,0 +1,139 @@
+import {
+  defineCommand,
+  fetchTrainingModelPrice,
+  estimateSftDpoTokens,
+  estimateCptTokens,
+  BailianError,
+  ExitCode,
+  type FlagsDef,
+} from "bailian-cli-core";
+import { emitResult } from "bailian-cli-runtime";
+
+const PRICE_FLAGS = {
+  baseModel: {
+    type: "string",
+    valueHint: "<model>",
+    description: "Base model to fine-tune (e.g. qwen3-8b; not the output model name)",
+    required: true,
+  },
+  datasets: {
+    type: "string",
+    valueHint: "<ids>",
+    description: "Training dataset file IDs, comma-separated (required)",
+    required: true,
+  },
+  trainingType: {
+    type: "string",
+    valueHint: "<type>",
+    description: "Training type: sft | dpo | cpt (default: sft)",
+  },
+  nEpochs: {
+    type: "number",
+    valueHint: "<n>",
+    description: "Number of training epochs (default: 3)",
+  },
+} satisfies FlagsDef;
+
+const SUPPORTED_TRAINING_TYPES = ["sft", "dpo", "cpt"];
+
+// Fixed hyper-parameters used for estimation. Only n_epochs materially affects
+// the estimate; the rest are held at representative defaults (not exposed as
+// flags to keep the command surface minimal).
+const ESTIMATE_BATCH_SIZE = 16;
+const ESTIMATE_MAX_LENGTH = 8192;
+const DEFAULT_N_EPOCHS = 3;
+
+export default defineCommand({
+  description: "Estimate the training cost for a fine-tune job (token billing)",
+  auth: "console",
+  usageArgs: "--base-model <model> --datasets <ids> [--training-type <type>] [--n-epochs <n>]",
+  flags: PRICE_FLAGS,
+  exampleArgs: [
+    "--base-model qwen3-8b --datasets file-ft-xxx",
+    "--base-model qwen3-8b --datasets file-ft-xxx,file-ft-yyy --n-epochs 2",
+    "--base-model qwen3-8b --datasets file-ft-xxx --training-type cpt",
+  ],
+  notes: [
+    "Estimate only — the server computes token usage from the datasets; final cost is subject to the bill.",
+    "Covers token billing for sft / dpo / cpt. Training-unit (MTU) billing is not supported by this command.",
+    "Hyper-parameters other than --n-epochs are fixed at representative defaults for estimation.",
+  ],
+  async run(ctx) {
+    const { settings, flags } = ctx;
+    const model = flags.baseModel;
+    const datasetIds = flags.datasets
+      .split(",")
+      .map((datasetId) => datasetId.trim())
+      .filter(Boolean);
+    const trainingType = (flags.trainingType ?? "sft").toLowerCase();
+    const nEpochs = flags.nEpochs ?? DEFAULT_N_EPOCHS;
+
+    if (!SUPPORTED_TRAINING_TYPES.includes(trainingType)) {
+      throw new BailianError(
+        `Unsupported training type "${trainingType}". Supported: ${SUPPORTED_TRAINING_TYPES.join(", ")}.`,
+        ExitCode.USAGE,
+      );
+    }
+    if (datasetIds.length === 0) {
+      throw new BailianError("--datasets must contain at least one file ID.", ExitCode.USAGE);
+    }
+
+    if (settings.dryRun) {
+      emitResult(
+        { action: "finetune.price", model, datasets: datasetIds, trainingType, nEpochs },
+        "json",
+      );
+      return;
+    }
+
+    // Unit price (yuan per 千Token).
+    const priceInfo = await fetchTrainingModelPrice(ctx.client, model);
+    const unitPrice = Number(priceInfo.price);
+    if (!Number.isFinite(unitPrice)) {
+      throw new BailianError(
+        `No training price found for model "${model}".`,
+        ExitCode.GENERAL,
+        undefined,
+        { rawResponse: JSON.stringify(priceInfo) },
+      );
+    }
+
+    // Per-epoch token estimate (min/max range).
+    const estimate =
+      trainingType === "cpt"
+        ? await estimateCptTokens(ctx.client, model, datasetIds.join(","), nEpochs)
+        : await estimateSftDpoTokens(ctx.client, datasetIds, {
+            nEpochs,
+            batchSize: ESTIMATE_BATCH_SIZE,
+            maxLength: ESTIMATE_MAX_LENGTH,
+          });
+
+    const minPerEpoch = estimate.estimatedDatasetConsumedTokensMinPerEpoch ?? 0;
+    const maxPerEpoch = estimate.estimatedDatasetConsumedTokensMaxPerEpoch ?? 0;
+    const mixedMinPerEpoch = estimate.estimatedMixedConsumedTokensMinPerEpoch ?? 0;
+    const mixedMaxPerEpoch = estimate.estimatedMixedConsumedTokensMaxPerEpoch ?? 0;
+
+    const minTokens = (minPerEpoch + mixedMinPerEpoch) * nEpochs;
+    const maxTokens = (maxPerEpoch + mixedMaxPerEpoch) * nEpochs;
+    // price is yuan per 1000 tokens.
+    const minFee = (minTokens / 1000) * unitPrice;
+    const maxFee = (maxTokens / 1000) * unitPrice;
+
+    emitResult(
+      {
+        model,
+        training_type: trainingType,
+        n_epochs: nEpochs,
+        unit_price: unitPrice,
+        price_unit: priceInfo.priceUnit ?? "千Token",
+        estimated_tokens: { min: minTokens, max: maxTokens },
+        estimated_fee_yuan: {
+          min: Number(minFee.toFixed(4)),
+          max: Number(maxFee.toFixed(4)),
+        },
+        disclaimer: "Server-side estimate; final cost is subject to the bill.",
+      },
+      "json",
+    );
+  },
+});
@@ -1,12 +1,12 @@
 import {
  defineCommand,
-  detectOutputFormat,
  getFineTune,
  BailianError,
  ExitCode,
  type FlagsDef,
 } from "bailian-cli-core";
-import { emitResult, emitBare, emitRequestId } from "bailian-cli-runtime";
+import { emitResult, emitBare } from "bailian-cli-runtime";
+import { computeActualFee } from "./fee.ts";

 const DEFAULT_INTERVAL_SEC = 10;
 const MIN_INTERVAL_SEC = 1;
@@ -103,7 +103,6 @@ export default defineCommand({
    const follow = flags.follow;
    const intervalSec = Math.max(MIN_INTERVAL_SEC, flags.interval ?? DEFAULT_INTERVAL_SEC);
    const pollTimeoutSec = flags.pollTimeout;
-    const format = detectOutputFormat(settings.output);

    if (settings.dryRun) {
      emitResult(
@@ -114,7 +113,7 @@ export default defineCommand({
          interval: intervalSec,
          timeout: pollTimeoutSec,
        },
-        format,
+        "json",
      );
      return;
    }
@@ -132,16 +131,24 @@ export default defineCommand({
      if (settings.quiet) {
        // Just the status word — ideal for `status=$(... finetune watch ... --quiet)`.
        emitBare(status || "UNKNOWN");
-      } else if (format === "text") {
-        emitBare(`${nowStamp()}  ${jobId}  ${status || "UNKNOWN"}`);
-        if (status === "SUCCEEDED") emitBare(`✓ ${jobId}  ${status}`);
-        emitRequestId(response.request_id, settings.quiet);
      } else {
-        // json: a compact, purpose-built status probe.
-        emitResult(
-          { job_id: jobId, status: status || "UNKNOWN", terminal, request_id: response.request_id },
-          format,
-        );
+        const output: Record<string, unknown> = {
+          job_id: jobId,
+          status: status || "UNKNOWN",
+          terminal,
+          request_id: response.request_id,
+        };
+        // Enrich terminal output with actual fee when usage is reported.
+        const usageTokens = typeof job?.usage === "number" ? job.usage : undefined;
+        if (terminal && usageTokens && usageTokens > 0 && job?.model) {
+          output.usage_tokens = usageTokens;
+          const fee = await computeActualFee(settings, job.model as string, usageTokens);
+          if (fee) {
+            output.training_cost = fee.cost;
+            output.cost_basis = `${fee.unitPrice} 元/${fee.priceUnit}`;
+          }
+        }
+        emitResult(output, "json");
      }

      if (terminal && status !== "SUCCEEDED") {
@@ -168,18 +175,28 @@ export default defineCommand({
        const job = response.output ?? response.data;
        const status = String(job?.status ?? "").toUpperCase();

-        if (format === "text" && !settings.quiet && status !== lastStatus) {
-          emitBare(`${nowStamp()}  ${jobId}  ${status || "UNKNOWN"}`);
+        if (!settings.quiet && status !== lastStatus) {
+          process.stderr.write(`${nowStamp()}  ${jobId}  ${status || "UNKNOWN"}\n`);
          lastStatus = status;
        }

        if (TERMINAL_STATUSES.has(status)) {
          const elapsed = Date.now() - startedAt;
-          if (format !== "text" || settings.quiet) {
-            emitResult(response, format);
-          } else if (status === "SUCCEEDED") {
-            emitBare(`\n✓ ${jobId}  ${status}  (elapsed ${formatElapsed(elapsed)})`);
-            emitRequestId(response.request_id, settings.quiet);
+          if (settings.quiet) {
+            emitBare(status || "UNKNOWN");
+          } else {
+            // Enrich the raw response with actual fee when usage is available.
+            const usageTokens = typeof job?.usage === "number" ? job.usage : undefined;
+            const enriched: Record<string, unknown> = { ...response };
+            if (usageTokens && usageTokens > 0 && job?.model) {
+              const fee = await computeActualFee(settings, job.model as string, usageTokens);
+              if (fee) {
+                enriched.training_cost = fee.cost;
+                enriched.usage_tokens = usageTokens;
+                enriched.cost_basis = `${fee.unitPrice} 元/${fee.priceUnit}`;
+              }
+            }
+            emitResult(enriched, "json");
          }
          if (status !== "SUCCEEDED") {
            throw new BailianError(
@@ -205,7 +222,7 @@ export default defineCommand({
      // Any other error (including the BailianError thrown above) propagates to
      // the central handler.
      if (controller.signal.aborted) {
-        emitBare("\nInterrupted.");
+        process.stderr.write("\nInterrupted.\n");
        return;
      }
      throw error;
@@ -2,7 +2,6 @@ import {
  BailianError,
  ExitCode,
  defineCommand,
-  detectOutputFormat,
  detectInstalledAgents,
  fetchSkillsIndex,
  getSkillRegistryBaseUrl,
@@ -28,22 +27,31 @@ const INSTALL_CONCURRENCY = 3;
 export default defineCommand({
  description: "Install skills from the Bailian skill registry into local agents",
  auth: "none",
-  usageArgs: "--name <all|name,...>",
+  usageArgs: "--all | --name <name,...>",
  flags: {
+    all: {
+      type: "switch",
+      description: "Install all skills from the registry",
+    },
    name: {
      type: "string",
-      valueHint: "<all|name,...>",
-      description: "Skills to install: all or comma-separated skill names",
-      required: true,
+      valueHint: "<name,...>",
+      description: "Comma-separated skill names to install",
    },
  },
-  exampleArgs: ["--name all", "--name spark-video,bailian-model-recommend"],
+  validate(flags) {
+    if (flags.all && flags.name) return "Use either --all or --name, not both";
+    if (!flags.all && !flags.name)
+      return "Specify --all to install everything or --name <name,...> for specific skills";
+    return undefined;
+  },
+  exampleArgs: ["--all", "--name spark-video,bailian-model-recommend"],
  async run(ctx) {
-    const format = detectOutputFormat(ctx.settings.output);
-    const requested = parseSkillNames(ctx.flags.name, false);
+    const format = ctx.settings.outputExplicit ? ctx.settings.output : "json";
    const index = await fetchSkillsIndex();
    const remoteNames = Object.keys(index.skills);
-    const names = requested === "all" ? remoteNames : requested;
+    const parsed = ctx.flags.all ? "all" : parseSkillNames(ctx.flags.name, false);
+    const names = parsed === "all" ? remoteNames : parsed;

    const lock = readSkillLock();
    const agents = detectInstalledAgents();
@@ -0,0 +1,107 @@
+import {
+  BailianError,
+  ExitCode,
+  defineCommand,
+  detectInstalledAgents,
+  fetchSkillsIndex,
+  getSkillRegistryBaseUrl,
+  installSkillWithFanout,
+  readSkillLock,
+  runWithConcurrency,
+  writeSkillLock,
+} from "bailian-cli-core";
+import { emitBare, emitResult, formatTable } from "bailian-cli-runtime";
+
+interface InitOutcome {
+  name: string;
+  status: "installed" | "failed";
+  publishedAt?: string;
+  agents?: string[];
+  reason?: string;
+}
+
+/** Prefix used to identify first-party Bailian skills in the registry. */
+const BAILIAN_PREFIX = "bailian-";
+
+/** Max number of skills downloading/installing at the same time. */
+const INIT_CONCURRENCY = 3;
+
+export default defineCommand({
+  description: "Install all bailian-* skills (one-shot bootstrap for new environments)",
+  auth: "none",
+  usageArgs: "",
+  exampleArgs: [""],
+  notes: [
+    "Fetches the registry index and installs every skill whose name starts with bailian-",
+    "Equivalent to: bl skill add --all (filtered to bailian-* skills)",
+  ],
+  async run(ctx) {
+    const format = ctx.settings.outputExplicit ? ctx.settings.output : "json";
+    const index = await fetchSkillsIndex();
+
+    // Discover all bailian-* skills from the live registry index
+    const names = Object.keys(index.skills).filter((name) => name.startsWith(BAILIAN_PREFIX));
+
+    const lock = readSkillLock();
+    const agents = detectInstalledAgents();
+
+    const tasks = names.map((name) => async (): Promise<InitOutcome> => {
+      const entry = index.skills[name];
+      try {
+        const record = await installSkillWithFanout(
+          name,
+          entry,
+          agents,
+          lock.skills[name]?.links ?? [],
+        );
+        lock.skills[name] = record.lockEntry;
+        return {
+          name,
+          status: "installed",
+          publishedAt: entry.publishedAt,
+          agents: record.linkedAgents,
+        };
+      } catch (err) {
+        return {
+          name,
+          status: "failed",
+          reason: err instanceof Error ? err.message : String(err),
+        };
+      }
+    });
+    const results = await runWithConcurrency(tasks, INIT_CONCURRENCY);
+    writeSkillLock(lock);
+
+    if (format === "json") {
+      emitResult(
+        {
+          registry: getSkillRegistryBaseUrl(),
+          agents: agents.map((agent) => agent.id),
+          skills: results,
+        },
+        format,
+      );
+    } else if (results.length === 0) {
+      emitBare("No bailian-* skills found in the registry.");
+    } else {
+      const rows = results.map((result) => [
+        result.name,
+        result.status,
+        result.publishedAt ? result.publishedAt.slice(0, 10) : "-",
+        result.status === "installed" ? result.agents?.join(", ") || "-" : (result.reason ?? "-"),
+      ]);
+      for (const line of formatTable(["NAME", "STATUS", "PUBLISHED", "AGENTS / REASON"], rows)) {
+        emitBare(line);
+      }
+    }
+
+    const failed = results.filter((result) => result.status === "failed");
+    if (failed.length > 0) {
+      throw new BailianError(
+        `${failed.length}/${results.length} skill(s) failed to install`,
+        ExitCode.GENERAL,
+        "Check the reason for failed skills in the output; network failures can be retried with bl skill init",
+      );
+    }
+  },
+});
@@ -1,6 +1,5 @@
 import {
  defineCommand,
-  detectOutputFormat,
  computeSkillStatuses,
  fetchSkillsIndex,
  getSkillRegistryBaseUrl,
@@ -24,7 +23,7 @@ export default defineCommand({
    "STATUS: installed | outdated | not-installed | missing (lock has it, dir deleted) | untracked (dir exists, not managed)",
  ],
  async run(ctx) {
-    const format = detectOutputFormat(ctx.settings.output);
+    const format = ctx.settings.outputExplicit ? ctx.settings.output : "json";
    // Three-way reconciliation: live remote index × skill-lock.json (installation facts) × disk
    const index = await fetchSkillsIndex();
    const lock = readSkillLock();
@@ -2,7 +2,6 @@ import {
  BailianError,
  ExitCode,
  defineCommand,
-  detectOutputFormat,
  listSkillDirsOnDisk,
  parseSkillNames,
  readSkillLock,
@@ -34,7 +33,7 @@ export default defineCommand({
  exampleArgs: ["--name spark-video", "--name all"],
  async run(ctx) {
    // Purely local operation: no remote access, works offline
-    const format = detectOutputFormat(ctx.settings.output);
+    const format = ctx.settings.outputExplicit ? ctx.settings.output : "json";
    const requested = parseSkillNames(ctx.flags.name, false);
    const lock = readSkillLock();
    const names = requested === "all" ? Object.keys(lock.skills) : requested;
@@ -2,7 +2,6 @@ import {
  BailianError,
  ExitCode,
  defineCommand,
-  detectOutputFormat,
  detectInstalledAgents,
  fanOutSkillToAgents,
  fetchSkillsIndex,
@@ -29,19 +28,27 @@ const UPDATE_CONCURRENCY = 3;
 export default defineCommand({
  description: "Update installed skills to the latest registry versions",
  auth: "none",
-  usageArgs: "[--name <all|name,...>]",
+  usageArgs: "[--all] [--name <name,...>]",
  flags: {
+    all: {
+      type: "switch",
+      description: "Update all installed skills (default when neither --all nor --name is given)",
+    },
    name: {
      type: "string",
-      valueHint: "<all|name,...>",
-      description:
-        "Skills to update: all (default, only changed ones) or comma-separated names (force update installed skills)",
+      valueHint: "<name,...>",
+      description: "Comma-separated skill names to update (must be already installed)",
    },
  },
-  exampleArgs: ["", "--name spark-video"],
+  validate(flags) {
+    if (flags.all && flags.name) return "Use either --all or --name, not both";
+    return undefined;
+  },
+  exampleArgs: ["", "--all", "--name spark-video"],
  async run(ctx) {
-    const format = detectOutputFormat(ctx.settings.output);
-    const requested = parseSkillNames(ctx.flags.name, true);
+    const format = ctx.settings.outputExplicit ? ctx.settings.output : "json";
+    const updateAll = ctx.flags.all || !ctx.flags.name;
+    const requested = updateAll ? "all" : parseSkillNames(ctx.flags.name, false);
    const index = await fetchSkillsIndex();
    const lock = readSkillLock();
    const disk = new Set(listSkillDirsOnDisk());
@@ -1,6 +1,7 @@
 import {
  defineCommand,
  videoGeneratePath,
+  image2videoPath,
  taskPath,
  detectOutputFormat,
  type DashScopeVideoRequest,
@@ -43,6 +44,11 @@ export default defineCommand({
      valueHint: "<url>",
      description: "Input image URL for image-to-video generation",
    },
+    lastFrame: {
+      type: "string",
+      valueHint: "<url>",
+      description: "Last frame image URL (with --image, enables kf2v first+last frame mode)",
+    },
    negativePrompt: {
      type: "string",
      valueHint: "<text>",
@@ -110,12 +116,20 @@ export default defineCommand({
    const format = detectOutputFormat(settings.output);

    const imageUrl = flags.image;
+    const lastFrameUrl = flags.lastFrame as string | undefined;

    // Auto-upload local image file for i2v
    let resolvedImageUrl: string | undefined;
    if (imageUrl) {
      resolvedImageUrl = await ctx.client.resolveImageInput(imageUrl, model);
    }
+    let resolvedLastFrameUrl: string | undefined;
+    if (lastFrameUrl) {
+      resolvedLastFrameUrl = await ctx.client.resolveImageInput(lastFrameUrl, model);
+    }
+
+    // kf2v mode: both --image and --last-frame provided.
+    const isKf2v = Boolean(resolvedImageUrl && resolvedLastFrameUrl);

    const watermark = resolveWatermark(flags.watermark);
    const promptExtend = resolveBooleanFlag(flags.promptExtend, undefined, "prompt-extend");
@@ -125,10 +139,16 @@ export default defineCommand({
      input: {
        prompt: prompt,
        negative_prompt: flags.negativePrompt || undefined,
-        // i2v models (happyhorse-1.1-i2v) require input.media with type 'first_frame'
-        ...(resolvedImageUrl
-          ? { media: [{ type: "first_frame" as const, url: resolvedImageUrl }] }
-          : {}),
+        // kf2v: first+last frame flat fields via image2video endpoint.
+        // wan2.1~2.6 i2v: flat img_url via video-generation endpoint.
+        // wan2.7+ / happyhorse i2v: media[] via video-generation endpoint.
+        ...(isKf2v
+          ? { first_frame_url: resolvedImageUrl, last_frame_url: resolvedLastFrameUrl }
+          : resolvedImageUrl
+            ? /wan[x]?2\.[1-6]/i.test(model)
+              ? { img_url: resolvedImageUrl }
+              : { media: [{ type: "first_frame" as const, url: resolvedImageUrl }] }
+            : {}),
      },
      parameters: {
        resolution: flags.resolution || undefined,
@@ -141,15 +161,28 @@ export default defineCommand({
    };

    if (settings.dryRun) {
-      const previewBody = resolvedImageUrl
-        ? {
-            ...body,
-            input: {
-              ...body.input,
-              media: [{ type: "first_frame" as const, url: redactDataUri(resolvedImageUrl) }],
-            },
-          }
-        : body;
+      let previewBody = body;
+      if (isKf2v) {
+        previewBody = {
+          ...body,
+          input: {
+            ...body.input,
+            first_frame_url: redactDataUri(resolvedImageUrl ?? ""),
+            last_frame_url: redactDataUri(resolvedLastFrameUrl ?? ""),
+          },
+        };
+      } else if (resolvedImageUrl) {
+        const redactedUrl = redactDataUri(resolvedImageUrl);
+        previewBody = {
+          ...body,
+          input: {
+            ...body.input,
+            ...(/wan[x]?2\.[1-6]/i.test(model)
+              ? { img_url: redactedUrl }
+              : { media: [{ type: "first_frame" as const, url: redactedUrl }] }),
+          },
+        };
+      }
      emitResult({ request: previewBody }, format);
      return;
    }
@@ -162,7 +195,7 @@ export default defineCommand({
      settings,
      () =>
        ctx.client.requestJson<DashScopeAsyncResponse>({
-          path: videoGeneratePath(),
+          path: isKf2v ? image2videoPath() : videoGeneratePath(),
          method: "POST",
          body,
          async: true,
@@ -66,6 +66,7 @@ export {
  finetuneTextCreate,
  finetuneAudioCreate,
  finetuneImageCreate,
+  finetuneVideoCreate,
 } from "./commands/finetune/create.ts";
 export { default as finetuneList } from "./commands/finetune/list.ts";
 export { default as finetuneGet } from "./commands/finetune/get.ts";
@@ -76,6 +77,7 @@ export { default as finetuneCheckpoints } from "./commands/finetune/checkpoints.
 export { default as finetuneExport } from "./commands/finetune/export.ts";
 export { default as finetuneWatch } from "./commands/finetune/watch.ts";
 export { default as finetuneCapability } from "./commands/finetune/capability.ts";
+export { default as finetunePrice } from "./commands/finetune/price.ts";
 export {
  deployTextCreate,
  deployAudioCreate,
@@ -87,6 +89,8 @@ export { default as deployModels } from "./commands/deploy/models.ts";
 export { default as deployScale } from "./commands/deploy/scale.ts";
 export { default as deployUpdate } from "./commands/deploy/update.ts";
 export { default as deployDelete } from "./commands/deploy/delete.ts";
+export { default as deployPause } from "./commands/deploy/pause.ts";
+export { default as deployResume } from "./commands/deploy/resume.ts";
 export { default as tokenPlanListSeats } from "./commands/token-plan/list-seats.ts";
 export { default as tokenPlanCreateKey } from "./commands/token-plan/create-key.ts";
 export { default as tokenPlanAssignSeats } from "./commands/token-plan/assign-seats.ts";
@@ -117,3 +121,4 @@ export { default as skillAdd } from "./commands/skill/add.ts";
 export { default as skillUpdate } from "./commands/skill/update.ts";
 export { default as skillRemove } from "./commands/skill/remove.ts";
 export { default as skillList } from "./commands/skill/list.ts";
+export { default as skillInit } from "./commands/skill/init.ts";
@@ -30,7 +30,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: deploy (offline)", () => {
      "--help",
    ]);
    expect(exitCode, stderr).toBe(0);
-    expect(stderr).toMatch(/--model|--name/i);
+    expect(stderr).toMatch(/--model-name|--display-name/i);
  });

  test("deploy create --dry-run 构造 lora 部署请求体", async () => {
@@ -38,9 +38,9 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: deploy (offline)", () => {
      "deploy",
      "text",
      "create",
-      "--model",
+      "--model-name",
      "qwen-plus-2025-12-01",
-      "--name",
+      "--display-name",
      "my-qwen-plus",
      "--dry-run",
      "--output",
@@ -68,9 +68,9 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: deploy (offline)", () => {
      "deploy",
      "text",
      "create",
-      "--model",
+      "--model-name",
      "qwen3-8b",
-      "--name",
+      "--display-name",
      "my-qwen3-mu",
      "--plan",
      "mu",
@@ -102,9 +102,9 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: deploy (offline)", () => {
      "deploy",
      "audio",
      "create",
-      "--model",
+      "--model-name",
      "my-cosyvoice-ft",
-      "--name",
+      "--display-name",
      "my-tts",
      "--dry-run",
      "--output",
@@ -31,7 +31,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "--help",
    ]);
    expect(exitCode, stderr).toBe(0);
-    expect(stderr).toMatch(/--model|--datasets/i);
+    expect(stderr).toMatch(/--base-model|--datasets/i);
  });

  test("finetune create --dry-run 构造 SFT 默认请求体", async () => {
@@ -39,7 +39,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      "file-aaa,file-bbb",
@@ -73,7 +73,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      "file-aaa",
@@ -135,7 +135,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
        "finetune",
        "text",
        "create",
-        "--model",
+        "--base-model",
        "qwen3-8b",
        "--datasets",
        "file-aaa",
@@ -157,7 +157,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      "file-aaa",
@@ -176,7 +176,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      `${localPath},file-bbb`,
@@ -207,7 +207,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      " , ",
@@ -228,7 +228,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      localPath,
@@ -250,7 +250,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      localPath,
@@ -272,7 +272,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
    ["cancel", ["--job-id", "ft-xxx"]],
    ["delete", ["--job-id", "ft-xxx"]],
    ["watch", ["--job-id", "ft-xxx"]],
-    ["capability", ["--model", "qwen3-8b"]],
+    ["capability", ["--base-model", "qwen3-8b"]],
  ])("finetune %s --dry-run 发出结构化动作", async (sub, extra) => {
    const { stdout, stderr, exitCode } = await runCommandE2e(FINETUNE_ROUTES, [
      "finetune",
@@ -292,7 +292,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "text",
      "create",
-      "--model",
+      "--base-model",
      "qwen3-8b",
      "--datasets",
      " file-a , ,file-b ",
@@ -314,7 +314,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "audio",
      "create",
-      "--model",
+      "--base-model",
      "cosyvoice-v3-flash",
      "--datasets",
      "file-audio",
@@ -343,7 +343,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "--help",
    ]);
    expect(exitCode, stderr).toBe(0);
-    expect(stderr).toMatch(/--model|--datasets/i);
+    expect(stderr).toMatch(/--base-model|--datasets/i);
    expect(stderr).not.toMatch(/--training-type|--n-epochs|--batch-size|--max-length/);
  });

@@ -352,7 +352,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
      "finetune",
      "image",
      "create",
-      "--model",
+      "--base-model",
      "wan2.7-image-pro",
      "--datasets",
      "file-image",
@@ -365,6 +365,104 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
    expect(data.action).toBe("finetune.create");
    expect(data.body.training_type).toBe("efficient_sft");
  });
+
+  test("finetune video create --help 暴露视频超参且不含文本超参", async () => {
+    // Video exposes --n-epochs / --batch-size / --learning-rate; the text-only
+    // --training-type / --max-length surface is not offered.
+    const { stderr, exitCode } = await runCommandE2e(FINETUNE_ROUTES, [
+      "finetune",
+      "video",
+      "create",
+      "--help",
+    ]);
+    expect(exitCode, stderr).toBe(0);
+    expect(stderr).toMatch(/--base-model/);
+    expect(stderr).toMatch(/--n-epochs/);
+    expect(stderr).toMatch(/--batch-size/);
+    expect(stderr).toMatch(/--learning-rate/);
+    expect(stderr).not.toMatch(/--training-type|--max-length/);
+  });
+
+  test("finetune video create --datasets 缺失时退出为用法错误 (2)", async () => {
+    const { stderr, exitCode } = await runCommandE2e(FINETUNE_ROUTES, [
+      "finetune",
+      "video",
+      "create",
+      "--base-model",
+      "wan2.7-i2v",
+      "--quiet",
+    ]);
+    expect(exitCode).toBe(2);
+    expect(stderr).toMatch(/--datasets|Missing required/i);
+  });
+
+  test.each([
+    // Model-family-specific defaults resolved by the sft-lora video profile.
+    ["wan2.7-i2v", 1, 102400],
+    ["wan2.5-i2v-preview", 4, 36864],
+    ["wan2.2-kf2v-flash", 4, 262144],
+  ])(
+    "finetune video create --dry-run %s 解析 batch_size=%i / max_pixels=%i",
+    async (baseModel, batchSize, maxPixels) => {
+      const { stdout, stderr, exitCode } = await runCommandE2e(FINETUNE_ROUTES, [
+        "finetune",
+        "video",
+        "create",
+        "--base-model",
+        baseModel,
+        "--datasets",
+        "file-video",
+        "--dry-run",
+        "--output",
+        "json",
+      ]);
+      expect(exitCode, stderr).toBe(0);
+      const data = parseStdoutJson<{
+        action: string;
+        body: {
+          model: string;
+          training_type: string;
+          hyper_parameters: Record<string, unknown>;
+        };
+      }>(stdout);
+      expect(data.action).toBe("finetune.create");
+      expect(data.body.model).toBe(baseModel);
+      expect(data.body.training_type).toBe("efficient_sft");
+      expect(data.body.hyper_parameters.batch_size).toBe(batchSize);
+      expect(data.body.hyper_parameters.max_pixels).toBe(maxPixels);
+      expect(data.body.hyper_parameters.learning_rate).toBe("2e-5");
+      expect(data.body.hyper_parameters.lora_rank).toBe(32);
+    },
+  );
+
+  test("finetune video create --dry-run 转发超参覆盖且不做 clamp", async () => {
+    const { stdout, stderr, exitCode } = await runCommandE2e(FINETUNE_ROUTES, [
+      "finetune",
+      "video",
+      "create",
+      "--base-model",
+      "wan2.7-i2v",
+      "--datasets",
+      "file-video",
+      "--n-epochs",
+      "100",
+      "--batch-size",
+      "2",
+      "--learning-rate",
+      "1e-5",
+      "--dry-run",
+      "--output",
+      "json",
+    ]);
+    expect(exitCode, stderr).toBe(0);
+    const data = parseStdoutJson<{
+      body: { hyper_parameters: Record<string, unknown> };
+    }>(stdout);
+    // Video overrides are forwarded verbatim (no [8, 1024] text clamp).
+    expect(data.body.hyper_parameters.n_epochs).toBe(100);
+    expect(data.body.hyper_parameters.batch_size).toBe(2);
+    expect(data.body.hyper_parameters.learning_rate).toBe("1e-5");
+  });
 });

 describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (DashScope)", () => {
@@ -17,12 +17,14 @@ describe("e2e: skill", () => {
  test("skill add --help exits successfully", async () => {
    const { stderr, exitCode } = await runCommandE2e(SKILL_ROUTES, ["skill", "add", "--help"]);
    expect(exitCode, stderr).toBe(0);
+    expect(stderr).toMatch(/--all/);
    expect(stderr).toMatch(/--name/);
  });

  test("skill update --help exits successfully", async () => {
    const { stderr, exitCode } = await runCommandE2e(SKILL_ROUTES, ["skill", "update", "--help"]);
    expect(exitCode, stderr).toBe(0);
+    expect(stderr).toMatch(/--all/);
    expect(stderr).toMatch(/--name/);
  });

@@ -37,18 +39,37 @@ describe("e2e: skill", () => {
    expect(exitCode, stderr).toBe(0);
    expect(stderr).toMatch(/list|registry/i);
  });
+
+  test("skill init --help exits successfully", async () => {
+    const { stderr, exitCode } = await runCommandE2e(SKILL_ROUTES, ["skill", "init", "--help"]);
+    expect(exitCode, stderr).toBe(0);
+    expect(stderr).toMatch(/bailian/i);
+  });
 });

 // Local-only cases: auth "none" + validation happens before any network access, no gating needed
 describe("e2e: skill (local, no credentials)", () => {
-  test("skill add without --name errors as usage error (2)", async () => {
+  test("skill add without --all or --name errors as usage error (2)", async () => {
    const { stdout, stderr, exitCode } = await runCommandE2e(SKILL_ROUTES, [
      "skill",
      "add",
      "--quiet",
    ]);
    expect(exitCode).toBe(2);
-    expect(`${stdout}\n${stderr}`).toMatch(/--name|Usage:/i);
+    expect(`${stdout}\n${stderr}`).toMatch(/--all|--name|Usage:/i);
+  });
+
+  test("skill add with both --all and --name errors as usage error (2)", async () => {
+    const { stdout, stderr, exitCode } = await runCommandE2e(SKILL_ROUTES, [
+      "skill",
+      "add",
+      "--all",
+      "--name",
+      "spark-video",
+      "--quiet",
+    ]);
+    expect(exitCode).toBe(2);
+    expect(`${stdout}\n${stderr}`).toMatch(/--all|--name|either/i);
  });

  test("skill remove without --name errors as usage error (2)", async () => {
@@ -61,19 +82,6 @@ describe("e2e: skill (local, no credentials)", () => {
    expect(`${stdout}\n${stderr}`).toMatch(/--name|Usage:/i);
  });

-  test("skill add rejects mixing all with specific names (2)", async () => {
-    // parseSkillNames throws UsageError before fetchSkillsIndex — offline-safe
-    const { stdout, stderr, exitCode } = await runCommandE2e(SKILL_ROUTES, [
-      "skill",
-      "add",
-      "--name",
-      "all,spark-video",
-      "--quiet",
-    ]);
-    expect(exitCode).toBe(2);
-    expect(`${stdout}\n${stderr}`).toMatch(/all/i);
-  });
-
  test("skill remove of a not-installed skill fails with reason (1)", async () => {
    const configDir = makeTempConfigDir();
    const { stdout, exitCode } = await runCommandE2e(
@@ -135,6 +135,7 @@ export const FINETUNE_ROUTES: E2eRouteExports = {
  "finetune text create": "finetuneTextCreate",
  "finetune audio create": "finetuneAudioCreate",
  "finetune image create": "finetuneImageCreate",
+  "finetune video create": "finetuneVideoCreate",
  "finetune list": "finetuneList",
  "finetune get": "finetuneGet",
  "finetune cancel": "finetuneCancel",
@@ -165,6 +166,7 @@ export const SKILL_ROUTES: E2eRouteExports = {
  "skill update": "skillUpdate",
  "skill remove": "skillRemove",
  "skill list": "skillList",
+  "skill init": "skillInit",
 };

 export const MANAGED_AGENT_ROUTES: E2eRouteExports = {
@@ -112,6 +112,62 @@ describe("e2e: video generate (i2v)", () => {
    }>(stdout);
    expect(data.request?.input?.media?.[0]?.url).toBe("data:image/png;base64,<omitted>");
  });
+
+  test.each([
+    // wan2.1~2.6 (legacy) use flat img_url; wan2.7+ and happyhorse use media[].
+    ["wan2.5-i2v-preview", "img_url"],
+    ["wan2.6-i2v", "img_url"],
+    ["wan2.7-i2v", "media"],
+    ["happyhorse-1.1-i2v", "media"],
+  ])("video generate --dry-run %s 首帧走 %s 字段", async (model, field) => {
+    const configDir = makeE2eOutputDir(`video-i2v-input-shape-${model}`);
+    writeFileSync(
+      join(configDir, "config.json"),
+      JSON.stringify({
+        "token-plan": {
+          api_key: "sk-sp-e2e-placeholder",
+          base_url: "https://token-plan.cn-beijing.maas.aliyuncs.com",
+        },
+      }),
+    );
+
+    const { stdout, stderr, exitCode } = await runCommandE2e(
+      VIDEO_ROUTES,
+      [
+        "video",
+        "generate",
+        "--config",
+        "token-plan",
+        "--dry-run",
+        "--model",
+        model,
+        "--image",
+        "https://example.com/placeholder.png",
+        "--prompt",
+        "干跑校验",
+        "--output",
+        "json",
+      ],
+      {
+        BAILIAN_CONFIG_DIR: configDir,
+        DASHSCOPE_API_KEY: "",
+        DASHSCOPE_BASE_URL: "",
+      },
+    );
+    expect(exitCode, stderr).toBe(0);
+    const data = parseStdoutJson<{
+      request?: {
+        input?: { img_url?: string; media?: Array<{ type?: string; url?: string }> };
+      };
+    }>(stdout);
+    if (field === "img_url") {
+      expect(data.request?.input?.img_url).toBe("https://example.com/placeholder.png");
+      expect(data.request?.input?.media).toBeUndefined();
+    } else {
+      expect(data.request?.input?.media?.[0]?.type).toBe("first_frame");
+      expect(data.request?.input?.img_url).toBeUndefined();
+    }
+  });
 });

 describe.skipIf(!isBailianE2EVideoEnabled() || !isDashScopeE2EReady())(
@@ -1,6 +1,6 @@
 {
  "name": "bailian-cli-core",
-  "version": "1.14.1",
+  "version": "1.14.2",
  "description": "Core SDK for bailian-cli. See https://www.npmjs.com/package/bailian-cli for usage.",
  "homepage": "https://bailian.console.aliyun.com/cli",
  "bugs": {
@@ -32,6 +32,11 @@ export function videoGeneratePath(): string {
  return "/api/v1/services/aigc/video-generation/video-synthesis";
 }

+/** POST /api/v1/services/aigc/image2video/video-synthesis — kf2v (first+last frame). */
+export function image2videoPath(): string {
+  return "/api/v1/services/aigc/image2video/video-synthesis";
+}
+
 // ---- Async Task Query ----
 export function taskPath(taskId: string): string {
  return `/api/v1/tasks/${encodeURIComponent(taskId)}`;
@@ -19,6 +19,7 @@ export {
  taskPath,
  userProfilePath,
  videoGeneratePath,
+  image2videoPath,
 } from "./endpoints.ts";
 export {
  isLegacyImage2ImageModel,
@@ -2,3 +2,4 @@ export * from "./api.ts";
 export * from "./types.ts";
 export * from "./constants.ts";
 export * from "./plans.ts";
+export * from "./lifecycle.ts";
@@ -0,0 +1,99 @@
+/**
+ * Deployment lifecycle operations via the **console gateway**.
+ *
+ * Unlike the DashScope REST endpoints in `api.ts`, start/stop/list-independent
+ * are console-domain APIs (`zeldaEasy.broadscope-platform.modelInstance.*`).
+ * Commands using these must declare `auth: "console"`.
+ */
+import type { Client } from "../client/client.ts";
+import { unwrapResponse } from "../console/models.ts";
+
+// ---------------------------------------------------------------------------
+// API names
+// ---------------------------------------------------------------------------
+
+export const DEPLOY_START_API = "zeldaEasy.broadscope-platform.modelInstance.startModelService";
+export const DEPLOY_STOP_API = "zeldaEasy.broadscope-platform.modelInstance.stopModelService";
+export const DEPLOY_LIST_INDEPENDENT_API =
+  "zeldaEasy.broadscope-platform.modelInstance.listIndependentDeployedModel";
+
+// ---------------------------------------------------------------------------
+// Types
+// ---------------------------------------------------------------------------
+
+export interface ModelServiceEntry {
+  modelServiceId?: string;
+  deployedModel?: string;
+  deployed_model?: string;
+  status?: string;
+  modelName?: string;
+  model_name?: string;
+  plan?: string;
+  [key: string]: unknown;
+}
+
+// ---------------------------------------------------------------------------
+// API wrappers
+// ---------------------------------------------------------------------------
+
+/** Start (bring online) a stopped deployment. */
+export async function startModelService(
+  client: Client,
+  modelServiceId: string,
+): Promise<Record<string, unknown>> {
+  const raw = await client.console<Record<string, unknown>>(DEPLOY_START_API, {
+    input: { modelServiceId },
+  });
+  return unwrapResponse(raw);
+}
+
+/** Stop (take offline) a running deployment. Stops billing for mu/ptu plans. */
+export async function stopModelService(
+  client: Client,
+  modelServiceId: string,
+): Promise<Record<string, unknown>> {
+  const raw = await client.console<Record<string, unknown>>(DEPLOY_STOP_API, {
+    input: { modelServiceId },
+  });
+  return unwrapResponse(raw);
+}
+
+/**
+ * List independently deployed models (console domain).
+ * Used for precheck status verification and ID mapping.
+ * Paginates internally to return all entries.
+ */
+export async function listIndependentDeployedModels(client: Client): Promise<ModelServiceEntry[]> {
+  const allEntries: ModelServiceEntry[] = [];
+  let page = 1;
+
+  while (true) {
+    const raw = await client.console<Record<string, unknown>>(DEPLOY_LIST_INDEPENDENT_API, {
+      input: { pageNo: page, pageSize: 50 },
+    });
+    const resp = unwrapResponse(raw);
+    const records = (resp.records ?? []) as ModelServiceEntry[];
+    allEntries.push(...records);
+    const pageCount = (resp.pageCount as number) ?? 1;
+    if (page >= pageCount || records.length === 0) break;
+    page++;
+  }
+
+  return allEntries;
+}
+
+/**
+ * Find a deployment entry by its identifier in the console-domain list.
+ * Matches against `modelServiceId`, `deployedModel`, or `deployed_model`.
+ */
+export function findDeploymentEntry(
+  entries: ModelServiceEntry[],
+  deployedModel: string,
+): ModelServiceEntry | undefined {
+  return entries.find(
+    (entry) =>
+      entry.modelServiceId === deployedModel ||
+      entry.deployedModel === deployedModel ||
+      entry.deployed_model === deployedModel,
+  );
+}
@@ -43,6 +43,8 @@ export interface ListFineTunesParams {
  pageNo?: number;
  pageSize?: number;
  status?: string;
+  /** Filter by base model ID (server-side). */
+  model?: string;
  signal?: AbortSignal;
 }

@@ -55,6 +57,7 @@ export async function listFineTunes(
  if (params.pageNo !== undefined) qs.set("page_no", String(params.pageNo));
  if (params.pageSize !== undefined) qs.set("page_size", String(params.pageSize));
  if (params.status) qs.set("status", params.status);
+  if (params.model) qs.set("model", params.model);
  const base = finetuneJobsPath();
  const path = qs.toString() ? `${base}?${qs.toString()}` : base;
  return client.requestJson<ListFineTunesResponse>({
@@ -3,3 +3,4 @@ export * from "./api.ts";
 export * from "./capability.ts";
 export * from "./preflight.ts";
 export * from "./profiles/index.ts";
+export * from "./price.ts";
@@ -0,0 +1,121 @@
+/**
+ * Training price estimation via the **console gateway**.
+ *
+ * These are console-domain APIs (`zeldaEasy.broadscope-platform.*`); commands
+ * using them must declare `auth: "console"`. Two different argument wrappers
+ * exist: `getModelPrice` takes a top-level `query`, while the token-estimation
+ * APIs take a top-level `input`.
+ */
+import type { Client } from "../client/client.ts";
+import { unwrapResponse } from "../console/models.ts";
+
+// ---------------------------------------------------------------------------
+// API names
+// ---------------------------------------------------------------------------
+
+export const TRAINING_MODEL_PRICE_API = "zeldaEasy.broadscope-platform.modelCenter.getModelPrice";
+export const CALC_DATASETS_TOKENS_API =
+  "zeldaEasy.broadscope-platform.modelInstance.calculateDatasetsTotalTokens";
+export const ESTIMATE_FINETUNE_TOKENS_API =
+  "zeldaEasy.broadscope-platform.modelInstance.estimateFinetuneTokens";
+
+// ---------------------------------------------------------------------------
+// Types
+// ---------------------------------------------------------------------------
+
+export interface TrainingModelPrice {
+  price?: string;
+  priceUnit?: string;
+  modelId?: string;
+  [key: string]: unknown;
+}
+
+export interface TokenEstimate {
+  estimatedDatasetConsumedTokensMinPerEpoch?: number;
+  estimatedDatasetConsumedTokensMaxPerEpoch?: number;
+  estimatedMixedConsumedTokensMinPerEpoch?: number;
+  estimatedMixedConsumedTokensMaxPerEpoch?: number;
+  [key: string]: unknown;
+}
+
+// ---------------------------------------------------------------------------
+// API wrappers
+// ---------------------------------------------------------------------------
+
+/**
+ * Training unit price for a model. `price` is denominated in `priceUnit`
+ * (typically "千Token" — yuan per 1000 tokens).
+ */
+export async function fetchTrainingModelPrice(
+  client: Client,
+  modelId: string,
+): Promise<TrainingModelPrice> {
+  const raw = await client.console<Record<string, unknown>>(TRAINING_MODEL_PRICE_API, {
+    query: { type: 0, modelId },
+  });
+  return unwrapResponse(raw) as TrainingModelPrice;
+}
+
+/**
+ * Estimate training tokens for SFT / DPO jobs.
+ * Returns a per-epoch min/max range; multiply by `n_epochs` for the total.
+ */
+export async function estimateSftDpoTokens(
+  client: Client,
+  datasetIds: string[],
+  hyperParams: { nEpochs: number; batchSize: number; maxLength: number },
+): Promise<TokenEstimate> {
+  const raw = await client.console<Record<string, unknown>>(CALC_DATASETS_TOKENS_API, {
+    input: { trainDatasetIds: datasetIds, hyperParams },
+  });
+  return unwrapResponse(raw) as TokenEstimate;
+}
+
+/**
+ * Estimate training tokens for CPT jobs.
+ *
+ * The console API requires `hyperParams` as a **JSON string** with a full
+ * `userDefinedObj` payload (captured from the console frontend), plus several
+ * top-level fields (`algorithmType`, `bizType`, `priority`, …). Only
+ * `n_epochs` / `max_length` materially affect the estimate; the remaining
+ * hyper-parameters are fixed defaults.
+ */
+export async function estimateCptTokens(
+  client: Client,
+  model: string,
+  datasetIdsCsv: string,
+  nEpochs: number,
+): Promise<TokenEstimate> {
+  const userDefinedObj = {
+    batch_size: 16,
+    eval_steps: 50,
+    learning_rate: "7e-6",
+    lr_scheduler_type: "linear",
+    max_length: 8192,
+    n_epochs: nEpochs,
+    split: 0.9,
+    save_total_limit: "3",
+    resume_from_checkpoint: false,
+    save_strategy: "epoch",
+  };
+  const hyperParams = JSON.stringify({
+    useDefault: false,
+    userDefinedObj,
+    useQwenMixedStrategy: false,
+  });
+  const raw = await client.console<Record<string, unknown>>(ESTIMATE_FINETUNE_TOKENS_API, {
+    input: {
+      trainingType: "cpt",
+      instanceName: `${model}_cli_estimate`,
+      algorithmType: 100,
+      bizType: 100,
+      trainDatasetIds: datasetIdsCsv,
+      hyperParams,
+      bailianTrainModel: model,
+      validationDatasetIds: "",
+      jobName: `${model}_cli_estimate`,
+      priority: "L0",
+    },
+  });
+  return unwrapResponse(raw) as TokenEstimate;
+}
@@ -75,20 +75,24 @@ const IMAGE_HYPER_PARAMS_I2I: Record<string, unknown> = {
 *
 * Shared across all video models; `batch_size` and `max_pixels` differ by model
 * family (resolved per model in `resolveHyperParameters`):
- *   - wan2.5 (e.g. wan2.5-i2v-preview): batch_size 2, max_pixels 36864
- *   - wan2.2 (i2v-flash / kf2v-flash):  batch_size 4, max_pixels 262144
+ *   - wan2.7 (e.g. wan2.7-i2v):           batch_size 1, max_pixels 102400
+ *   - wan2.5 (e.g. wan2.5-i2v-preview):   batch_size 4, max_pixels 36864
+ *   - wan2.2 (i2v-flash / kf2v-flash):    batch_size 4, max_pixels 262144
 *
 * `learning_rate` is a string to avoid JSON-number precision loss (consistent
 * with the image defaults). `split` (0.9) + `max_split_val_dataset_sample`
 * (5) drive the automatic train/validation split when no explicit
 * validation_file_ids are provided.
+ *
+ * Values aligned with the official user guide (2026-07):
+ *   n_epochs 50, eval_epochs 20 (≥ n_epochs/10).
 */
 const VIDEO_HYPER_PARAMS_BASE: Record<string, unknown> = {
-  n_epochs: 400,
+  n_epochs: 50,
  learning_rate: "2e-5",
-  split: 0.9,
+  split: 0.5,
  max_split_val_dataset_sample: 5,
-  eval_epochs: 50,
+  eval_epochs: 20,
  save_total_limit: 10,
  lora_rank: 32,
  lora_alpha: 32,
@@ -109,6 +113,11 @@ function isWan25(model: string | undefined): boolean {
  return typeof model === "string" && /wan2\.5/i.test(model);
 }

+/** wan2.7 family uses batch_size 1 and max_pixels 102400. */
+function isWan27(model: string | undefined): boolean {
+  return typeof model === "string" && /wan2\.7/i.test(model);
+}
+
 export const sftLoraProfile: TrainingProfile = {
  clientTrainingType: "sft-lora",
  serverTrainingType: "efficient_sft",
@@ -195,15 +204,18 @@ export const sftLoraProfile: TrainingProfile = {
    }
    if (isVideo(modality)) {
      // Video: shared defaults + model-family-specific batch_size / max_pixels.
-      // wan2.5 uses batch_size 2 / max_pixels 36864; wan2.2 uses 4 / 262144.
-      const wan25 = isWan25(flags.model as string | undefined);
+      //   wan2.7: batch_size 1, max_pixels 102400
+      //   wan2.5: batch_size 4, max_pixels 36864
+      //   wan2.2: batch_size 4, max_pixels 262144
+      const model = (flags.model ?? flags.baseModel) as string | undefined;
      const hp: Record<string, unknown> = {
        ...VIDEO_HYPER_PARAMS_BASE,
-        batch_size: 4,
-        max_pixels: wan25 ? 36864 : 262144,
+        batch_size: isWan27(model) ? 1 : 4,
+        max_pixels: isWan27(model) ? 102400 : isWan25(model) ? 36864 : 262144,
      };
      // Optional overrides (no clamping — video batch_size is intentionally small).
      if (flags.nEpochs !== undefined) hp.n_epochs = flags.nEpochs as number;
+      if (flags.batchSize !== undefined) hp.batch_size = flags.batchSize as number;
      if (flags.learningRate !== undefined) hp.learning_rate = flags.learningRate as string;
      return hp;
    }
@@ -168,6 +168,8 @@ export interface DashScopeVideoRequest {
    prompt: string;
    negative_prompt?: string;
    img_url?: string;
+    first_frame_url?: string;
+    last_frame_url?: string;
    media?: Array<{
      type: "image" | "video" | "first_frame" | "last_frame" | "driving_audio" | "first_clip";
      url: string;
@@ -1,6 +1,6 @@
 {
  "name": "knowledge-studio-cli",
-  "version": "1.14.1",
+  "version": "1.14.2",
  "description": "Lightweight RAG CLI for Aliyun Model Studio — focused on knowledge-base retrieval.",
  "keywords": [
    "alibaba-cloud",
@@ -1,6 +1,6 @@
 {
  "name": "bailian-cli-runtime",
-  "version": "1.14.1",
+  "version": "1.14.2",
  "description": "Runtime framework for bailian-cli (createCli, registry, args, output, pipeline). See https://www.npmjs.com/package/bailian-cli for usage.",
  "homepage": "https://bailian.console.aliyun.com/cli",
  "bugs": {
@@ -1,7 +1,7 @@
 ---
 name: bailian-cli
 metadata:
-  version: "1.14.1"
+  version: "1.14.2"
  requires:
    bins: ["bl"]
 description: >-
@@ -52,6 +52,7 @@ Use this index for the skill-scoped quick index and global flags.
 | `bl quota request`              | Request a temporary quota increase                                                             | [quota.md](quota.md)           |
 | `bl search web`                 | Search the web using DashScope MCP WebSearch service                                           | [search.md](search.md)         |
 | `bl skill add`                  | Install skills from the Bailian skill registry into local agents                               | [skill.md](skill.md)           |
+| `bl skill init`                 | Install all bailian-\* skills (one-shot bootstrap for new environments)                        | [skill.md](skill.md)           |
 | `bl skill list`                 | List registry skills and diff against local installs                                           | [skill.md](skill.md)           |
 | `bl skill remove`               | Remove locally installed skills (registry is untouched)                                        | [skill.md](skill.md)           |
 | `bl skill update`               | Update installed skills to the latest registry versions                                        | [skill.md](skill.md)           |
@@ -86,7 +87,7 @@ Use this index for the skill-scoped quick index and global flags.
 | `plugin`     | `install`, `link`, `list`, `remove`                                          | [plugin.md](plugin.md)         |
 | `quota`      | `check`, `history`, `list`, `request`                                        | [quota.md](quota.md)           |
 | `search`     | `web`                                                                        | [search.md](search.md)         |
-| `skill`      | `add`, `list`, `remove`, `update`                                            | [skill.md](skill.md)           |
+| `skill`      | `add`, `init`, `list`, `remove`, `update`                                    | [skill.md](skill.md)           |
 | `text`       | `chat`                                                                       | [text.md](text.md)             |
 | `token-plan` | `add-member`, `assign-seats`, `create-key`, `list-seats`                     | [token-plan.md](token-plan.md) |
 | `update`     | `(root)`                                                                     | [update.md](update.md)         |
@@ -7,12 +7,13 @@ Index: [index.md](index.md)

 ## Commands in this group

-| Command           | Description                                                      |
-| ----------------- | ---------------------------------------------------------------- |
-| `bl skill add`    | Install skills from the Bailian skill registry into local agents |
-| `bl skill list`   | List registry skills and diff against local installs             |
-| `bl skill remove` | Remove locally installed skills (registry is untouched)          |
-| `bl skill update` | Update installed skills to the latest registry versions          |
+| Command           | Description                                                             |
+| ----------------- | ----------------------------------------------------------------------- |
+| `bl skill add`    | Install skills from the Bailian skill registry into local agents        |
+| `bl skill init`   | Install all bailian-\* skills (one-shot bootstrap for new environments) |
+| `bl skill list`   | List registry skills and diff against local installs                    |
+| `bl skill remove` | Remove locally installed skills (registry is untouched)                 |
+| `bl skill update` | Update installed skills to the latest registry versions                 |

 ## Command details

@@ -22,24 +23,48 @@ Index: [index.md](index.md)
 | --------------- | ---------------------------------------------------------------- |
 | **Name**        | `skill add`                                                      |
 | **Description** | Install skills from the Bailian skill registry into local agents |
-| **Usage**       | `bl skill add --name <all\|name,...>`                            |
+| **Usage**       | `bl skill add --all \| --name <name,...>`                        |

 #### Flags

-| Flag                     | Type   | Required | Description                                           |
-| ------------------------ | ------ | -------- | ----------------------------------------------------- |
-| `--name <all\|name,...>` | string | yes      | Skills to install: all or comma-separated skill names |
+| Flag                | Type   | Required | Description                            |
+| ------------------- | ------ | -------- | -------------------------------------- |
+| `--all`             | switch | no       | Install all skills from the registry   |
+| `--name <name,...>` | string | no       | Comma-separated skill names to install |

 #### Examples

 ```bash
-bl skill add --name all
+bl skill add --all
 ```

 ```bash
 bl skill add --name spark-video,bailian-model-recommend
 ```

+### `bl skill init`
+
+| Field           | Value                                                                   |
+| --------------- | ----------------------------------------------------------------------- |
+| **Name**        | `skill init`                                                            |
+| **Description** | Install all bailian-\* skills (one-shot bootstrap for new environments) |
+| **Usage**       | `bl skill init`                                                         |
+
+#### Flags
+
+_No command-specific flags._
+
+#### Notes
+
+- Fetches the registry index and installs every skill whose name starts with bailian-
+- Equivalent to: bl skill add --all (filtered to bailian-\* skills)
+
+#### Examples
+
+```bash
+bl skill init
+```
+
 ### `bl skill list`

 | Field           | Value                                                |
@@ -96,13 +121,14 @@ bl skill remove --name all
 | --------------- | ------------------------------------------------------- |
 | **Name**        | `skill update`                                          |
 | **Description** | Update installed skills to the latest registry versions |
-| **Usage**       | `bl skill update [--name <all\|name,...>]`              |
+| **Usage**       | `bl skill update [--all] [--name <name,...>]`           |

 #### Flags

-| Flag                     | Type   | Required | Description                                                                                                 |
-| ------------------------ | ------ | -------- | ----------------------------------------------------------------------------------------------------------- |
-| `--name <all\|name,...>` | string | no       | Skills to update: all (default, only changed ones) or comma-separated names (force update installed skills) |
+| Flag                | Type   | Required | Description                                                                  |
+| ------------------- | ------ | -------- | ---------------------------------------------------------------------------- |
+| `--all`             | switch | no       | Update all installed skills (default when neither --all nor --name is given) |
+| `--name <name,...>` | string | no       | Comma-separated skill names to update (must be already installed)            |

 #### Examples

@@ -110,6 +136,10 @@ bl skill remove --name all
 bl skill update
 ```

+```bash
+bl skill update --all
+```
+
 ```bash
 bl skill update --name spark-video
 ```
@@ -1,7 +1,7 @@
 ---
 name: bailian-finetune
 metadata:
-  version: "1.14.1"
+  version: "1.14.2"
  requires:
    bins: ["bl"]
 description: >-
@@ -23,14 +23,14 @@ description: >-
 ```
 1. Validate data   bl dataset validate --file train.jsonl [--schema chatml|dpo|cpt|tts|image]
 2. Upload data     bl dataset upload --file train.jsonl          # returns a file-id
-3. Create job      bl finetune text|audio|image create --model <base> --datasets <file-id|path>
+3. Create job      bl finetune text|audio|image create --base-model <base> --datasets <file-id|path>
 4. Watch progress  bl finetune watch --job-id ft-xxx             # or get / logs
 5. Pick artifact   bl finetune checkpoints --job-id ft-xxx
 6. Export model    bl finetune export --job-id ft-xxx --checkpoint ckpt-N --model-name my-model
-7. Deploy service  bl deploy text|audio|image create --model my-model --name my-svc
+7. Deploy service  bl deploy text|audio|image create --model-name my-model --display-name my-svc
 ```

- Unsure which training methods a base model supports → `bl finetune capability --model <base>` or `--training-type sft|sft-lora|dpo|cpt`.
+- Unsure which training methods a base model supports → `bl finetune capability --base-model <base>` or `--training-type sft|sft-lora|dpo|cpt`.
 - Text `--training-type` values: `sft` / `sft-lora` / `dpo` / `dpo-lora` / `cpt`. Audio bases include `cosyvoice-v3-flash`; image bases include `wan2.7-image-pro`.
 - Deployment plans: audio defaults to `--plan mu`; text/image default to `lora`.
 - Preview write operations (create / delete / cancel / scale) with `--dry-run` first, and confirm with the user before deleting a job or dataset.
@@ -55,10 +55,10 @@ Flags, usage, and examples: see [`reference/`](reference/index.md) or `bl <comma
 ```bash
 bl dataset validate --file train.jsonl
 bl dataset upload --file train.jsonl
-bl finetune text create --model qwen3-8b --training-type sft-lora --datasets file-xxx
+bl finetune text create --base-model qwen3-8b --training-type sft-lora --datasets file-xxx
 bl finetune watch --job-id ft-xxx
 bl finetune export --job-id ft-xxx --checkpoint ckpt-3 --model-name my-qwen-sft
-bl deploy text create --model my-qwen-sft --name my-svc
+bl deploy text create --model-name my-qwen-sft --display-name my-svc
 ```

 ## Common hand-offs
@@ -107,23 +107,23 @@ bl dataset list --output json

 ### `bl dataset upload`

-| Field           | Value                                                                                                                            |
-| --------------- | -------------------------------------------------------------------------------------------------------------------------------- |
-| **Name**        | `dataset upload`                                                                                                                 |
-| **Description** | Upload a dataset file (.jsonl or .zip) to Bailian                                                                                |
-| **Usage**       | `bl dataset upload --file <path> [--purpose <name>] [--schema <chatml\|dpo\|cpt\|tts\|image>] [--no-validate] [--full-validate]` |
+| Field           | Value                                                                                                                                   |
+| --------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
+| **Name**        | `dataset upload`                                                                                                                        |
+| **Description** | Upload a dataset file (.jsonl or .zip) to Bailian                                                                                       |
+| **Usage**       | `bl dataset upload --file <path> [--purpose <name>] [--schema <chatml\|dpo\|cpt\|tts\|image\|video>] [--no-validate] [--full-validate]` |

 #### Flags

-| Flag               | Type   | Required | Description                                                                                                                                              |
-| ------------------ | ------ | -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `--file <path>`    | string | yes      | Local dataset file (.jsonl or .zip; ≤300MB text, ≤1GB image)                                                                                             |
-| `--purpose <name>` | string | no       | Dataset purpose tag (default: "fine-tune"; e.g. "evaluation")                                                                                            |
-| `--schema <s>`     | string | no       | Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), or "image" (image generation). Default auto-detects per record. |
-| `--no-validate`    | switch | no       | Skip the local JSONL pre-flight check (not recommended)                                                                                                  |
-| `--full-validate`  | switch | no       | JSON.parse every line instead of sampling (slower)                                                                                                       |
-| `--api-key <key>`  | string | no       | API key                                                                                                                                                  |
-| `--base-url <url>` | string | no       | API base URL                                                                                                                                             |
+| Flag               | Type   | Required | Description                                                                                                                                                                          |
+| ------------------ | ------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `--file <path>`    | string | yes      | Local dataset file (.jsonl or .zip; ≤300MB text, ≤1GB image)                                                                                                                         |
+| `--purpose <name>` | string | no       | Dataset purpose tag (default: "fine-tune"; e.g. "evaluation")                                                                                                                        |
+| `--schema <s>`     | string | no       | Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), "image" (image generation), or "video" (video generation). Default auto-detects per record. |
+| `--no-validate`    | switch | no       | Skip the local JSONL pre-flight check (not recommended)                                                                                                                              |
+| `--full-validate`  | switch | no       | JSON.parse every line instead of sampling (slower)                                                                                                                                   |
+| `--api-key <key>`  | string | no       | API key                                                                                                                                                                              |
+| `--base-url <url>` | string | no       | API base URL                                                                                                                                                                         |

 #### Notes

@@ -170,19 +170,19 @@ bl dataset upload --file train.jsonl --no-validate

 ### `bl dataset validate`

-| Field           | Value                                                                                           |
-| --------------- | ----------------------------------------------------------------------------------------------- |
-| **Name**        | `dataset validate`                                                                              |
-| **Description** | Locally validate a dataset file (.jsonl or .zip) without uploading                              |
-| **Usage**       | `bl dataset validate --file <path> [--full-validate] [--schema <chatml\|dpo\|cpt\|tts\|image>]` |
+| Field           | Value                                                                                                  |
+| --------------- | ------------------------------------------------------------------------------------------------------ |
+| **Name**        | `dataset validate`                                                                                     |
+| **Description** | Locally validate a dataset file (.jsonl or .zip) without uploading                                     |
+| **Usage**       | `bl dataset validate --file <path> [--full-validate] [--schema <chatml\|dpo\|cpt\|tts\|image\|video>]` |

 #### Flags

-| Flag              | Type   | Required | Description                                                                                                                                              |
-| ----------------- | ------ | -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `--file <path>`   | string | yes      | Local dataset file (.jsonl or .zip)                                                                                                                      |
-| `--full-validate` | switch | no       | JSON.parse every line instead of sampling (slower)                                                                                                       |
-| `--schema <s>`    | string | no       | Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), or "image" (image generation). Default auto-detects per record. |
+| Flag              | Type   | Required | Description                                                                                                                                                                          |
+| ----------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `--file <path>`   | string | yes      | Local dataset file (.jsonl or .zip)                                                                                                                                                  |
+| `--full-validate` | switch | no       | JSON.parse every line instead of sampling (slower)                                                                                                                                   |
+| `--schema <s>`    | string | no       | Record schema: "chatml" (SFT), "dpo" (chosen/rejected), "cpt" (raw text), "tts" (audio), "image" (image generation), or "video" (video generation). Default auto-detects per record. |

 #### Notes

@@ -191,13 +191,15 @@ bl dataset upload --file train.jsonl --no-validate
 - Schemas: chatml = {messages:[...]} (SFT); dpo = {messages:[...], chosen,
 - rejected}; cpt = {text:"..."} (continual pre-training, raw text);
 - tts = {wav_fn:"train/xxx.wav", text:"..."} (audio fine-tuning);
- image = {img_path:"..."} (image generation). With no --schema, a record
- carrying wav_fn is validated as TTS, img_path as image, chosen/rejected
- as DPO, text (no messages) as CPT, otherwise ChatML. Pass --schema to
- require a specific shape on every record. ZIP archives (.zip) are
- validated structurally (data.jsonl present, media references resolve) in
- addition to per-record content checks. Use --full-validate to JSON.parse
- every line.
+- image = {img_path:"..."} (image generation);
+- video = {first_frame_path:"...", video_path:"..."} (video generation,
+- i2v first-frame or kf2v first+last-frame with last_frame_path). With no
+- --schema, a record carrying wav_fn is validated as TTS, img_path as image,
+- first_frame_path/video_path as video, chosen/rejected as DPO, text (no
+- messages) as CPT, otherwise ChatML. Pass --schema to require a specific
+- shape on every record. ZIP archives (.zip) are validated structurally
+- (data.jsonl present, media references resolve) in addition to per-record
+- content checks. Use --full-validate to JSON.parse every line.

 #### Examples

@@ -217,6 +219,10 @@ bl dataset validate --file cpt.jsonl --schema cpt
 bl dataset validate --file audio.zip --schema tts
 ```

+```bash
+bl dataset validate --file wan-i2v-training-dataset.zip --schema video
+```
+
 ```bash
 bl dataset validate --file eval.jsonl --full-validate
 ```
@@ -7,43 +7,45 @@ Index: [index.md](index.md)

 ## Commands in this group

-| Command                  | Description                                               |
-| ------------------------ | --------------------------------------------------------- |
-| `bl deploy audio create` | Create an audio (TTS) model deployment                    |
-| `bl deploy delete`       | Delete a model deployment (must be STOPPED or FAILED)     |
-| `bl deploy get`          | Get details of a single model deployment                  |
-| `bl deploy image create` | Create an image generation model deployment               |
-| `bl deploy list`         | List model deployments                                    |
-| `bl deploy models`       | List models available for deployment                      |
-| `bl deploy scale`        | Scale a deployment's capacity                             |
-| `bl deploy text create`  | Create a text model deployment                            |
-| `bl deploy update`       | Update a deployment's rate limits (rpm_limit / tpm_limit) |
+| Command                  | Description                                                   |
+| ------------------------ | ------------------------------------------------------------- |
+| `bl deploy audio create` | Create an audio (TTS) model deployment                        |
+| `bl deploy delete`       | Delete a model deployment (must be STOPPED or FAILED)         |
+| `bl deploy get`          | Get details of a single model deployment                      |
+| `bl deploy image create` | Create an image generation model deployment                   |
+| `bl deploy list`         | List model deployments                                        |
+| `bl deploy models`       | List models available for deployment                          |
+| `bl deploy pause`        | Pause a running model deployment (stops billing for mu/ptu)   |
+| `bl deploy resume`       | Resume a paused model deployment (brings service back online) |
+| `bl deploy scale`        | Scale a deployment's capacity                                 |
+| `bl deploy text create`  | Create a text model deployment                                |
+| `bl deploy update`       | Update a deployment's rate limits (rpm_limit / tpm_limit)     |

 ## Command details

 ### `bl deploy audio create`

-| Field           | Value                                                                                                                                                                                                             |
-| --------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Name**        | `deploy audio create`                                                                                                                                                                                             |
-| **Description** | Create an audio (TTS) model deployment                                                                                                                                                                            |
-| **Usage**       | `bl deploy audio create --model <model_name> --name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]` |
+| Field           | Value                                                                                                                                                                                                                          |
+| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| **Name**        | `deploy audio create`                                                                                                                                                                                                          |
+| **Description** | Create an audio (TTS) model deployment                                                                                                                                                                                         |
+| **Usage**       | `bl deploy audio create --model-name <model_name> --display-name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]` |

 #### Flags

-| Flag                        | Type   | Required | Description                                                                     |
-| --------------------------- | ------ | -------- | ------------------------------------------------------------------------------- |
-| `--model <name>`            | string | yes      | Model name (catalog model or fine-tuned output) (required)                      |
-| `--name <display_name>`     | string | yes      | Console display name for the deployment (required)                              |
-| `--plan <plan>`             | string | no       | Billing plan: lora (default, Token-billed) \| ptu (Token-billed) \| mu          |
-| `--deploy-spec <id>`        | string | no       | Deploy spec (only used by plan=mu; auto-picked if omitted)                      |
-| `--capacity <n>`            | number | no       | Resource units (plan=mu only; required by API; defaults to the template's unit) |
-| `--billing-method <m>`      | string | no       | Billing method (plan=mu only; default "POST_PAY", the only supported value)     |
-| `--input-tpm <n>`           | number | no       | PTU max input tokens/min (required for plan=ptu)                                |
-| `--output-tpm <n>`          | number | no       | PTU max output tokens/min (required for plan=ptu)                               |
-| `--thinking-output-tpm <n>` | number | no       | PTU max thinking-output tokens/min (optional, some models)                      |
-| `--api-key <key>`           | string | no       | API key                                                                         |
-| `--base-url <url>`          | string | no       | API base URL                                                                    |
+| Flag                            | Type   | Required | Description                                                                     |
+| ------------------------------- | ------ | -------- | ------------------------------------------------------------------------------- |
+| `--model-name <model_name>`     | string | yes      | Model to deploy — fine-tuned output name or catalog model (required)            |
+| `--display-name <display_name>` | string | yes      | Console display name for the deployment (required)                              |
+| `--plan <plan>`                 | string | no       | Billing plan: lora (default, Token-billed) \| ptu (Token-billed) \| mu          |
+| `--deploy-spec <id>`            | string | no       | Deploy spec (only used by plan=mu; auto-picked if omitted)                      |
+| `--capacity <n>`                | number | no       | Resource units (plan=mu only; required by API; defaults to the template's unit) |
+| `--billing-method <m>`          | string | no       | Billing method (plan=mu only; default "POST_PAY", the only supported value)     |
+| `--input-tpm <n>`               | number | no       | PTU max input tokens/min (required for plan=ptu)                                |
+| `--output-tpm <n>`              | number | no       | PTU max output tokens/min (required for plan=ptu)                               |
+| `--thinking-output-tpm <n>`     | number | no       | PTU max thinking-output tokens/min (optional, some models)                      |
+| `--api-key <key>`               | string | no       | API key                                                                         |
+| `--base-url <url>`              | string | no       | API base URL                                                                    |

 #### Notes

@@ -58,27 +60,24 @@ Index: [index.md](index.md)
 - Use `bl deploy models --source base` to inspect available templates.
 - After creation, status starts at PENDING and transitions to RUNNING.
 - Invoke the deployed model with: bl text chat --model <deployed_model>
- WARNING: --model is overloaded across commands and refers to DIFFERENT
- values. `bl deploy <modality> create --model` takes the exported model_name
- (e.g. `qwen3-8b-ft-...`), but the create response also returns a
- `deployed_model` field (the deployment instance id, e.g.
- `qwen3-8b-5ecb5f068d79`). The inference call `bl text chat --model` must use
- the `deployed_model` from the create response — NOT the `model_name` you
- passed to `deploy <modality> create`. Do not reuse the value across the two
- commands.
+- NOTE: --model-name is the model being deployed (e.g. `qwen3-8b-ft-...`).
+- The create response also returns a `deployed_model` field — the deployment
+- instance id (e.g. `qwen3-8b-5ecb5f068d79`). Use that id for inference
+- (`bl text chat --model <deployed_model>`) and lifecycle commands
+- (`deploy get/scale/pause/resume/delete --deployed-model <id>`).

 #### Examples

 ```bash
-bl deploy audio create --model my-cosyvoice-ft --name my-tts
+bl deploy audio create --model-name my-cosyvoice-ft --display-name my-tts
 ```

 ```bash
-bl deploy audio create --model my-cosyvoice-ft --name my-tts --deploy-spec dps-xxxx --capacity 1
+bl deploy audio create --model-name my-cosyvoice-ft --display-name my-tts --deploy-spec dps-xxxx --capacity 1
 ```

 ```bash
-bl deploy audio create --model my-cosyvoice-ft --name my-tts --dry-run
+bl deploy audio create --model-name my-cosyvoice-ft --display-name my-tts --dry-run
 ```

 ### `bl deploy delete`
@@ -136,27 +135,27 @@ bl deploy get --deployed-model qwen-plus-2025-12-01-b6d61c71 --output json

 ### `bl deploy image create`

-| Field           | Value                                                                                                                                                                                                             |
-| --------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Name**        | `deploy image create`                                                                                                                                                                                             |
-| **Description** | Create an image generation model deployment                                                                                                                                                                       |
-| **Usage**       | `bl deploy image create --model <model_name> --name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]` |
+| Field           | Value                                                                                                                                                                                                                          |
+| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| **Name**        | `deploy image create`                                                                                                                                                                                                          |
+| **Description** | Create an image generation model deployment                                                                                                                                                                                    |
+| **Usage**       | `bl deploy image create --model-name <model_name> --display-name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]` |

 #### Flags

-| Flag                        | Type   | Required | Description                                                                     |
-| --------------------------- | ------ | -------- | ------------------------------------------------------------------------------- |
-| `--model <name>`            | string | yes      | Model name (catalog model or fine-tuned output) (required)                      |
-| `--name <display_name>`     | string | yes      | Console display name for the deployment (required)                              |
-| `--plan <plan>`             | string | no       | Billing plan: lora (default, Token-billed) \| ptu (Token-billed) \| mu          |
-| `--deploy-spec <id>`        | string | no       | Deploy spec (only used by plan=mu; auto-picked if omitted)                      |
-| `--capacity <n>`            | number | no       | Resource units (plan=mu only; required by API; defaults to the template's unit) |
-| `--billing-method <m>`      | string | no       | Billing method (plan=mu only; default "POST_PAY", the only supported value)     |
-| `--input-tpm <n>`           | number | no       | PTU max input tokens/min (required for plan=ptu)                                |
-| `--output-tpm <n>`          | number | no       | PTU max output tokens/min (required for plan=ptu)                               |
-| `--thinking-output-tpm <n>` | number | no       | PTU max thinking-output tokens/min (optional, some models)                      |
-| `--api-key <key>`           | string | no       | API key                                                                         |
-| `--base-url <url>`          | string | no       | API base URL                                                                    |
+| Flag                            | Type   | Required | Description                                                                     |
+| ------------------------------- | ------ | -------- | ------------------------------------------------------------------------------- |
+| `--model-name <model_name>`     | string | yes      | Model to deploy — fine-tuned output name or catalog model (required)            |
+| `--display-name <display_name>` | string | yes      | Console display name for the deployment (required)                              |
+| `--plan <plan>`                 | string | no       | Billing plan: lora (default, Token-billed) \| ptu (Token-billed) \| mu          |
+| `--deploy-spec <id>`            | string | no       | Deploy spec (only used by plan=mu; auto-picked if omitted)                      |
+| `--capacity <n>`                | number | no       | Resource units (plan=mu only; required by API; defaults to the template's unit) |
+| `--billing-method <m>`          | string | no       | Billing method (plan=mu only; default "POST_PAY", the only supported value)     |
+| `--input-tpm <n>`               | number | no       | PTU max input tokens/min (required for plan=ptu)                                |
+| `--output-tpm <n>`              | number | no       | PTU max output tokens/min (required for plan=ptu)                               |
+| `--thinking-output-tpm <n>`     | number | no       | PTU max thinking-output tokens/min (optional, some models)                      |
+| `--api-key <key>`               | string | no       | API key                                                                         |
+| `--base-url <url>`              | string | no       | API base URL                                                                    |

 #### Notes

@@ -171,27 +170,24 @@ bl deploy get --deployed-model qwen-plus-2025-12-01-b6d61c71 --output json
 - Use `bl deploy models --source base` to inspect available templates.
 - After creation, status starts at PENDING and transitions to RUNNING.
 - Invoke the deployed model with: bl text chat --model <deployed_model>
- WARNING: --model is overloaded across commands and refers to DIFFERENT
- values. `bl deploy <modality> create --model` takes the exported model_name
- (e.g. `qwen3-8b-ft-...`), but the create response also returns a
- `deployed_model` field (the deployment instance id, e.g.
- `qwen3-8b-5ecb5f068d79`). The inference call `bl text chat --model` must use
- the `deployed_model` from the create response — NOT the `model_name` you
- passed to `deploy <modality> create`. Do not reuse the value across the two
- commands.
+- NOTE: --model-name is the model being deployed (e.g. `qwen3-8b-ft-...`).
+- The create response also returns a `deployed_model` field — the deployment
+- instance id (e.g. `qwen3-8b-5ecb5f068d79`). Use that id for inference
+- (`bl text chat --model <deployed_model>`) and lifecycle commands
+- (`deploy get/scale/pause/resume/delete --deployed-model <id>`).

 #### Examples

 ```bash
-bl deploy image create --model my-wan-ft --name my-wan
+bl deploy image create --model-name my-wan-ft --display-name my-wan
 ```

 ```bash
-bl deploy image create --model my-wan-ft --name my-wan-mu --plan mu
+bl deploy image create --model-name my-wan-ft --display-name my-wan-mu --plan mu
 ```

 ```bash
-bl deploy image create --model my-wan-ft --name my-wan --dry-run
+bl deploy image create --model-name my-wan-ft --display-name my-wan --dry-run
 ```

 ### `bl deploy list`
@@ -263,6 +259,82 @@ bl deploy models --source custom --page-size 50
 bl deploy models --catalog-version v1.0 --output json
 ```

+### `bl deploy pause`
+
+| Field           | Value                                                       |
+| --------------- | ----------------------------------------------------------- |
+| **Name**        | `deploy pause`                                              |
+| **Description** | Pause a running model deployment (stops billing for mu/ptu) |
+| **Usage**       | `bl deploy pause --deployed-model <id> [--skip-precheck]`   |
+
+#### Flags
+
+| Flag                           | Type   | Required | Description                                              |
+| ------------------------------ | ------ | -------- | -------------------------------------------------------- |
+| `--deployed-model <id>`        | string | yes      | Deployed model identifier (required)                     |
+| `--skip-precheck`              | switch | no       | Skip the local RUNNING/PENDING status precheck           |
+| `--console-region <region>`    | string | no       | Console gateway region (e.g. cn-beijing, ap-southeast-1) |
+| `--console-site <site>`        | string | no       | Console site: domestic, international                    |
+| `--console-switch-agent <uid>` | number | no       | Switch agent UID for delegated access                    |
+| `--workspace-id <id>`          | string | no       | Workspace ID (env: BAILIAN_WORKSPACE_ID)                 |
+
+#### Notes
+
+- While paused, billing ceases for mu/ptu plans. Use `deploy resume` to bring it back online or `deploy delete` to remove.
+- Precheck verifies status is RUNNING/PENDING before issuing the pause; pass --skip-precheck to bypass.
+
+#### Examples
+
+```bash
+bl deploy pause --deployed-model dep-...
+```
+
+```bash
+bl deploy pause --deployed-model dep-... --skip-precheck
+```
+
+```bash
+bl deploy pause --deployed-model dep-... --dry-run
+```
+
+### `bl deploy resume`
+
+| Field           | Value                                                         |
+| --------------- | ------------------------------------------------------------- |
+| **Name**        | `deploy resume`                                               |
+| **Description** | Resume a paused model deployment (brings service back online) |
+| **Usage**       | `bl deploy resume --deployed-model <id> [--skip-precheck]`    |
+
+#### Flags
+
+| Flag                           | Type   | Required | Description                                              |
+| ------------------------------ | ------ | -------- | -------------------------------------------------------- |
+| `--deployed-model <id>`        | string | yes      | Deployed model identifier (required)                     |
+| `--skip-precheck`              | switch | no       | Skip the local STOPPED status precheck                   |
+| `--console-region <region>`    | string | no       | Console gateway region (e.g. cn-beijing, ap-southeast-1) |
+| `--console-site <site>`        | string | no       | Console site: domestic, international                    |
+| `--console-switch-agent <uid>` | number | no       | Switch agent UID for delegated access                    |
+| `--workspace-id <id>`          | string | no       | Workspace ID (env: BAILIAN_WORKSPACE_ID)                 |
+
+#### Notes
+
+- Precheck verifies status is STOPPED before issuing the resume; pass --skip-precheck to bypass.
+- For mu/ptu plans, billing resumes once the service is back online.
+
+#### Examples
+
+```bash
+bl deploy resume --deployed-model dep-...
+```
+
+```bash
+bl deploy resume --deployed-model dep-... --skip-precheck
+```
+
+```bash
+bl deploy resume --deployed-model dep-... --dry-run
+```
+
 ### `bl deploy scale`

 | Field           | Value                                                                                       |
@@ -294,27 +366,27 @@ bl deploy scale --deployed-model dep-... --capacity 2

 ### `bl deploy text create`

-| Field           | Value                                                                                                                                                                                                            |
-| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Name**        | `deploy text create`                                                                                                                                                                                             |
-| **Description** | Create a text model deployment                                                                                                                                                                                   |
-| **Usage**       | `bl deploy text create --model <model_name> --name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]` |
+| Field           | Value                                                                                                                                                                                                                         |
+| --------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Name**        | `deploy text create`                                                                                                                                                                                                          |
+| **Description** | Create a text model deployment                                                                                                                                                                                                |
+| **Usage**       | `bl deploy text create --model-name <model_name> --display-name <display_name> [--plan <plan>] [--deploy-spec <id>] [--capacity <n>] [--billing-method <m>] [--input-tpm <n>] [--output-tpm <n>] [--thinking-output-tpm <n>]` |

 #### Flags

-| Flag                        | Type   | Required | Description                                                                     |
-| --------------------------- | ------ | -------- | ------------------------------------------------------------------------------- |
-| `--model <name>`            | string | yes      | Model name (catalog model or fine-tuned output) (required)                      |
-| `--name <display_name>`     | string | yes      | Console display name for the deployment (required)                              |
-| `--plan <plan>`             | string | no       | Billing plan: lora (default, Token-billed) \| ptu (Token-billed) \| mu          |
-| `--deploy-spec <id>`        | string | no       | Deploy spec (only used by plan=mu; auto-picked if omitted)                      |
-| `--capacity <n>`            | number | no       | Resource units (plan=mu only; required by API; defaults to the template's unit) |
-| `--billing-method <m>`      | string | no       | Billing method (plan=mu only; default "POST_PAY", the only supported value)     |
-| `--input-tpm <n>`           | number | no       | PTU max input tokens/min (required for plan=ptu)                                |
-| `--output-tpm <n>`          | number | no       | PTU max output tokens/min (required for plan=ptu)                               |
-| `--thinking-output-tpm <n>` | number | no       | PTU max thinking-output tokens/min (optional, some models)                      |
-| `--api-key <key>`           | string | no       | API key                                                                         |
-| `--base-url <url>`          | string | no       | API base URL                                                                    |
+| Flag                            | Type   | Required | Description                                                                     |
+| ------------------------------- | ------ | -------- | ------------------------------------------------------------------------------- |
+| `--model-name <model_name>`     | string | yes      | Model to deploy — fine-tuned output name or catalog model (required)            |
+| `--display-name <display_name>` | string | yes      | Console display name for the deployment (required)                              |
+| `--plan <plan>`                 | string | no       | Billing plan: lora (default, Token-billed) \| ptu (Token-billed) \| mu          |
+| `--deploy-spec <id>`            | string | no       | Deploy spec (only used by plan=mu; auto-picked if omitted)                      |
+| `--capacity <n>`                | number | no       | Resource units (plan=mu only; required by API; defaults to the template's unit) |
+| `--billing-method <m>`          | string | no       | Billing method (plan=mu only; default "POST_PAY", the only supported value)     |
+| `--input-tpm <n>`               | number | no       | PTU max input tokens/min (required for plan=ptu)                                |
+| `--output-tpm <n>`              | number | no       | PTU max output tokens/min (required for plan=ptu)                               |
+| `--thinking-output-tpm <n>`     | number | no       | PTU max thinking-output tokens/min (optional, some models)                      |
+| `--api-key <key>`               | string | no       | API key                                                                         |
+| `--base-url <url>`              | string | no       | API base URL                                                                    |

 #### Notes

@@ -329,31 +401,28 @@ bl deploy scale --deployed-model dep-... --capacity 2
 - Use `bl deploy models --source base` to inspect available templates.
 - After creation, status starts at PENDING and transitions to RUNNING.
 - Invoke the deployed model with: bl text chat --model <deployed_model>
- WARNING: --model is overloaded across commands and refers to DIFFERENT
- values. `bl deploy <modality> create --model` takes the exported model_name
- (e.g. `qwen3-8b-ft-...`), but the create response also returns a
- `deployed_model` field (the deployment instance id, e.g.
- `qwen3-8b-5ecb5f068d79`). The inference call `bl text chat --model` must use
- the `deployed_model` from the create response — NOT the `model_name` you
- passed to `deploy <modality> create`. Do not reuse the value across the two
- commands.
+- NOTE: --model-name is the model being deployed (e.g. `qwen3-8b-ft-...`).
+- The create response also returns a `deployed_model` field — the deployment
+- instance id (e.g. `qwen3-8b-5ecb5f068d79`). Use that id for inference
+- (`bl text chat --model <deployed_model>`) and lifecycle commands
+- (`deploy get/scale/pause/resume/delete --deployed-model <id>`).

 #### Examples

 ```bash
-bl deploy text create --model my-qwen-sft --name my-sft-test
+bl deploy text create --model-name my-qwen-sft --display-name my-sft-test
 ```

 ```bash
-bl deploy text create --model qwen3.6-flash-2026-04-16 --name my-flash --plan ptu --input-tpm 10000 --output-tpm 1000
+bl deploy text create --model-name qwen3.6-flash-2026-04-16 --display-name my-flash --plan ptu --input-tpm 10000 --output-tpm 1000
 ```

 ```bash
-bl deploy text create --model qwen3-8b --name my-qwen3-mu --plan mu
+bl deploy text create --model-name qwen3-8b --display-name my-qwen3-mu --plan mu
 ```

 ```bash
-bl deploy text create --model qwen3-8b --name my-qwen3 --plan mu --deploy-spec MU1 --capacity 2
+bl deploy text create --model-name qwen3-8b --display-name my-qwen3 --plan mu --deploy-spec MU1 --capacity 2
 ```

 ### `bl deploy update`
@@ -19,24 +19,26 @@ Index: [index.md](index.md)
 | `bl finetune image create` | Create an image generation model fine-tune job (sft-lora)                                                                       |
 | `bl finetune list`         | List fine-tune jobs                                                                                                             |
 | `bl finetune logs`         | Fetch training logs for a fine-tune job                                                                                         |
+| `bl finetune price`        | Estimate the training cost for a fine-tune job (token billing)                                                                  |
 | `bl finetune text create`  | Create a text model fine-tune job (sft \| sft-lora \| dpo \| dpo-lora \| cpt)                                                   |
+| `bl finetune video create` | Create a video generation model fine-tune job (Wan i2v/kf2v, efficient_sft)                                                     |
 | `bl finetune watch`        | Probe a fine-tune job's status (default: single non-blocking fetch). Pass --follow to poll until terminal.                      |

 ## Command details

 ### `bl finetune audio create`

-| Field           | Value                                                                                                                               |
-| --------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
-| **Name**        | `finetune audio create`                                                                                                             |
-| **Description** | Create an audio TTS model fine-tune job (sft-lora)                                                                                  |
-| **Usage**       | `bl finetune audio create --model <model> --datasets <id\|path> [--validations <id\|path>] [--model-name <name>] [--suffix <text>]` |
+| Field           | Value                                                                                                                                    |
+| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
+| **Name**        | `finetune audio create`                                                                                                                  |
+| **Description** | Create an audio TTS model fine-tune job (sft-lora)                                                                                       |
+| **Usage**       | `bl finetune audio create --base-model <model> --datasets <id\|path> [--validations <id\|path>] [--model-name <name>] [--suffix <text>]` |

 #### Flags

 | Flag                         | Type   | Required | Description                                                                                                                                                        |
 | ---------------------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `--model <model>`            | string | yes      | Base model to fine-tune                                                                                                                                            |
+| `--base-model <model>`       | string | yes      | Base model to fine-tune (e.g. qwen3-8b; not the output model name)                                                                                                 |
 | `--datasets <ids\|paths>`    | string | yes      | Comma-separated dataset file IDs or local paths (.jsonl for text, .zip for audio/image). Local paths are uploaded (validated) first, then their file-ids are used. |
 | `--validations <ids\|paths>` | string | no       | Comma-separated validation dataset file IDs or local paths (auto-uploaded like --datasets).                                                                        |
 | `--model-name <name>`        | string | no       | Output model name (after training)                                                                                                                                 |
@@ -58,23 +60,23 @@ Index: [index.md](index.md)
 #### Examples

 ```bash
-bl finetune audio create --model cosyvoice-v3-flash --datasets ./audio.zip
+bl finetune audio create --base-model cosyvoice-v3-flash --datasets ./audio.zip
 ```

 ```bash
-bl finetune audio create --model cosyvoice-v3-flash --datasets file-xxx
+bl finetune audio create --base-model cosyvoice-v3-flash --datasets file-xxx
 ```

 ```bash
-bl finetune audio create --model cosyvoice-v3-flash --datasets ./audio.zip --model-name my-tts
+bl finetune audio create --base-model cosyvoice-v3-flash --datasets ./audio.zip --model-name my-tts
 ```

 ```bash
-bl finetune audio create --model cosyvoice-v3-flash --datasets file-xxx --output json
+bl finetune audio create --base-model cosyvoice-v3-flash --datasets file-xxx --output json
 ```

 ```bash
-bl finetune audio create --model cosyvoice-v3-flash --datasets ./audio.zip --dry-run
+bl finetune audio create --base-model cosyvoice-v3-flash --datasets ./audio.zip --dry-run
 ```

 ### `bl finetune cancel`
@@ -114,18 +116,18 @@ bl finetune cancel --job-id ft-xxx --dry-run
 | --------------- | ------------------------------------------------------------------------------------------------------------------------------- |
 | **Name**        | `finetune capability`                                                                                                           |
 | **Description** | Query fine-tune training capability — by model (which training types it supports) or by training type (which models support it) |
-| **Usage**       | `bl finetune capability --model <m> \| --training-type <t>`                                                                     |
+| **Usage**       | `bl finetune capability --base-model <m> \| --training-type <t>`                                                                |

 #### Flags

 | Flag                  | Type   | Required | Description                                                                           |
 | --------------------- | ------ | -------- | ------------------------------------------------------------------------------------- |
-| `--model <m>`         | string | no       | List training types supported by this base model.                                     |
+| `--base-model <m>`    | string | no       | List training types supported by this base model.                                     |
 | `--training-type <t>` | string | no       | List models supporting this training type: sft \| sft-lora \| dpo \| dpo-lora \| cpt. |

 #### Notes

- Exactly one of --model / --training-type is required.
+- Exactly one of --base-model / --training-type is required.
 - Training-type values use the `<method>` / `<method>-lora` convention:
 - sft | sft-lora | dpo | dpo-lora | cpt. (cpt has no -lora variant server-side.)
 - Queries listFoundationModels, a public API — no console login needed.
@@ -133,7 +135,7 @@ bl finetune cancel --job-id ft-xxx --dry-run
 #### Examples

 ```bash
-bl finetune capability --model qwen3-8b
+bl finetune capability --base-model qwen3-8b
 ```

 ```bash
@@ -166,8 +168,8 @@ bl finetune capability --training-type sft --quiet

 #### Notes

- Use the returned `checkpoint` value with `finetune export` to publish
- a deployable model.
+- `model_name` (shown for SUCCEEDED checkpoints) is the direct input for `deploy create --model-name`.
+- Checkpoints expire ~15 days after creation; `expire_time` shows the deadline. Export or deploy before expiry.

 #### Examples

@@ -268,17 +270,17 @@ bl finetune get --job-id ft-xxx --output json

 ### `bl finetune image create`

-| Field           | Value                                                                                                                                                                                      |
-| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| **Name**        | `finetune image create`                                                                                                                                                                    |
-| **Description** | Create an image generation model fine-tune job (sft-lora)                                                                                                                                  |
-| **Usage**       | `bl finetune image create --model <model> --datasets <id\|path> [--validations <id\|path>] [--model-name <name>] [--suffix <text>] [--generation-type <t2i\|i2i>] [--learning-rate <str>]` |
+| Field           | Value                                                                                                                                                                                           |
+| --------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Name**        | `finetune image create`                                                                                                                                                                         |
+| **Description** | Create an image generation model fine-tune job (sft-lora)                                                                                                                                       |
+| **Usage**       | `bl finetune image create --base-model <model> --datasets <id\|path> [--validations <id\|path>] [--model-name <name>] [--suffix <text>] [--generation-type <t2i\|i2i>] [--learning-rate <str>]` |

 #### Flags

 | Flag                           | Type   | Required | Description                                                                                                                                                         |
 | ------------------------------ | ------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `--model <model>`              | string | yes      | Base model to fine-tune                                                                                                                                             |
+| `--base-model <model>`         | string | yes      | Base model to fine-tune (e.g. qwen3-8b; not the output model name)                                                                                                  |
 | `--datasets <ids\|paths>`      | string | yes      | Comma-separated dataset file IDs or local paths (.jsonl for text, .zip for audio/image). Local paths are uploaded (validated) first, then their file-ids are used.  |
 | `--validations <ids\|paths>`   | string | no       | Comma-separated validation dataset file IDs or local paths (auto-uploaded like --datasets).                                                                         |
 | `--model-name <name>`          | string | no       | Output model name (after training)                                                                                                                                  |
@@ -304,46 +306,47 @@ bl finetune get --job-id ft-xxx --output json
 #### Examples

 ```bash
-bl finetune image create --model wan2.7-image-pro --datasets ./images.zip
+bl finetune image create --base-model wan2.7-image-pro --datasets ./images.zip
 ```

 ```bash
-bl finetune image create --model wan2.7-image-pro --datasets file-xxx
+bl finetune image create --base-model wan2.7-image-pro --datasets file-xxx
 ```

 ```bash
-bl finetune image create --model wan2.7-image-pro --datasets file-xxx --generation-type i2i
+bl finetune image create --base-model wan2.7-image-pro --datasets file-xxx --generation-type i2i
 ```

 ```bash
-bl finetune image create --model wan2.7-image-pro --datasets ./images.zip --model-name my-wan
+bl finetune image create --base-model wan2.7-image-pro --datasets ./images.zip --model-name my-wan
 ```

 ```bash
-bl finetune image create --model wan2.7-image-pro --datasets file-xxx --output json
+bl finetune image create --base-model wan2.7-image-pro --datasets file-xxx --output json
 ```

 ```bash
-bl finetune image create --model wan2.7-image-pro --datasets ./images.zip --dry-run
+bl finetune image create --base-model wan2.7-image-pro --datasets ./images.zip --dry-run
 ```

 ### `bl finetune list`

-| Field           | Value                                                            |
-| --------------- | ---------------------------------------------------------------- |
-| **Name**        | `finetune list`                                                  |
-| **Description** | List fine-tune jobs                                              |
-| **Usage**       | `bl finetune list [--page <n>] [--page-size <n>] [--status <s>]` |
+| Field           | Value                                                                                   |
+| --------------- | --------------------------------------------------------------------------------------- |
+| **Name**        | `finetune list`                                                                         |
+| **Description** | List fine-tune jobs                                                                     |
+| **Usage**       | `bl finetune list [--page <n>] [--page-size <n>] [--status <s>] [--base-model <model>]` |

 #### Flags

-| Flag               | Type   | Required | Description                                                          |
-| ------------------ | ------ | -------- | -------------------------------------------------------------------- |
-| `--page <n>`       | number | no       | Page number (default: 1)                                             |
-| `--page-size <n>`  | number | no       | Results per page (default: 10, max 100)                              |
-| `--status <s>`     | string | no       | Filter by status (PENDING / RUNNING / SUCCEEDED / FAILED / CANCELED) |
-| `--api-key <key>`  | string | no       | API key                                                              |
-| `--base-url <url>` | string | no       | API base URL                                                         |
+| Flag                   | Type   | Required | Description                                                          |
+| ---------------------- | ------ | -------- | -------------------------------------------------------------------- |
+| `--page <n>`           | number | no       | Page number (default: 1)                                             |
+| `--page-size <n>`      | number | no       | Results per page (default: 10, max 100)                              |
+| `--status <s>`         | string | no       | Filter by status (PENDING / RUNNING / SUCCEEDED / FAILED / CANCELED) |
+| `--base-model <model>` | string | no       | Filter by base model ID (server-side)                                |
+| `--api-key <key>`      | string | no       | API key                                                              |
+| `--base-url <url>`     | string | no       | API base URL                                                         |

 #### Examples

@@ -356,7 +359,11 @@ bl finetune list --status RUNNING
 ```

 ```bash
-bl finetune list --page-size 20 --output json
+bl finetune list --base-model qwen3-8b
+```
+
+```bash
+bl finetune list --page-size 20
 ```

 ### `bl finetune logs`
@@ -405,19 +412,60 @@ bl finetune logs --job-id ft-xxx --tail 20
 bl finetune logs --job-id ft-xxx --search checkpoint --tail 5
 ```

+### `bl finetune price`
+
+| Field           | Value                                                                                               |
+| --------------- | --------------------------------------------------------------------------------------------------- |
+| **Name**        | `finetune price`                                                                                    |
+| **Description** | Estimate the training cost for a fine-tune job (token billing)                                      |
+| **Usage**       | `bl finetune price --base-model <model> --datasets <ids> [--training-type <type>] [--n-epochs <n>]` |
+
+#### Flags
+
+| Flag                           | Type   | Required | Description                                                        |
+| ------------------------------ | ------ | -------- | ------------------------------------------------------------------ |
+| `--base-model <model>`         | string | yes      | Base model to fine-tune (e.g. qwen3-8b; not the output model name) |
+| `--datasets <ids>`             | string | yes      | Training dataset file IDs, comma-separated (required)              |
+| `--training-type <type>`       | string | no       | Training type: sft \| dpo \| cpt (default: sft)                    |
+| `--n-epochs <n>`               | number | no       | Number of training epochs (default: 3)                             |
+| `--console-region <region>`    | string | no       | Console gateway region (e.g. cn-beijing, ap-southeast-1)           |
+| `--console-site <site>`        | string | no       | Console site: domestic, international                              |
+| `--console-switch-agent <uid>` | number | no       | Switch agent UID for delegated access                              |
+| `--workspace-id <id>`          | string | no       | Workspace ID (env: BAILIAN_WORKSPACE_ID)                           |
+
+#### Notes
+
+- Estimate only — the server computes token usage from the datasets; final cost is subject to the bill.
+- Covers token billing for sft / dpo / cpt. Training-unit (MTU) billing is not supported by this command.
+- Hyper-parameters other than --n-epochs are fixed at representative defaults for estimation.
+
+#### Examples
+
+```bash
+bl finetune price --base-model qwen3-8b --datasets file-ft-xxx
+```
+
+```bash
+bl finetune price --base-model qwen3-8b --datasets file-ft-xxx,file-ft-yyy --n-epochs 2
+```
+
+```bash
+bl finetune price --base-model qwen3-8b --datasets file-ft-xxx --training-type cpt
+```
+
 ### `bl finetune text create`

-| Field           | Value                                                                                                                                                                                                                                                                           |
-| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Name**        | `finetune text create`                                                                                                                                                                                                                                                          |
-| **Description** | Create a text model fine-tune job (sft \| sft-lora \| dpo \| dpo-lora \| cpt)                                                                                                                                                                                                   |
-| **Usage**       | `bl finetune text create --model <model> --datasets <id\|path,...> [--validations <id\|path,...>] [--model-name <name>] [--suffix <text>] [--n-epochs <n>] [--batch-size <n>] [--learning-rate <str>] [--max-length <n>] [--training-type <sft\|sft-lora\|dpo\|dpo-lora\|cpt>]` |
+| Field           | Value                                                                                                                                                                                                                                                                                |
+| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| **Name**        | `finetune text create`                                                                                                                                                                                                                                                               |
+| **Description** | Create a text model fine-tune job (sft \| sft-lora \| dpo \| dpo-lora \| cpt)                                                                                                                                                                                                        |
+| **Usage**       | `bl finetune text create --base-model <model> --datasets <id\|path,...> [--validations <id\|path,...>] [--model-name <name>] [--suffix <text>] [--n-epochs <n>] [--batch-size <n>] [--learning-rate <str>] [--max-length <n>] [--training-type <sft\|sft-lora\|dpo\|dpo-lora\|cpt>]` |

 #### Flags

 | Flag                         | Type   | Required | Description                                                                                                                                                                              |
 | ---------------------------- | ------ | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `--model <model>`            | string | yes      | Base model to fine-tune                                                                                                                                                                  |
+| `--base-model <model>`       | string | yes      | Base model to fine-tune (e.g. qwen3-8b; not the output model name)                                                                                                                       |
 | `--datasets <ids\|paths>`    | string | yes      | Comma-separated dataset file IDs or local paths (.jsonl for text, .zip for audio/image). Local paths are uploaded (validated) first, then their file-ids are used.                       |
 | `--validations <ids\|paths>` | string | no       | Comma-separated validation dataset file IDs or local paths (auto-uploaded like --datasets).                                                                                              |
 | `--model-name <name>`        | string | no       | Output model name (after training)                                                                                                                                                       |
@@ -453,35 +501,89 @@ bl finetune logs --job-id ft-xxx --search checkpoint --tail 5
 #### Examples

 ```bash
-bl finetune text create --model qwen3-8b --datasets file-xxx
+bl finetune text create --base-model qwen3-8b --datasets file-xxx
 ```

 ```bash
-bl finetune text create --model qwen3-8b --datasets ./train.jsonl
+bl finetune text create --base-model qwen3-8b --datasets ./train.jsonl
 ```

 ```bash
-bl finetune text create --model qwen3-8b --datasets ./train.jsonl --validations ./eval.jsonl
+bl finetune text create --base-model qwen3-8b --datasets ./train.jsonl --validations ./eval.jsonl
 ```

 ```bash
-bl finetune text create --model qwen3-8b --datasets file-aaa,./extra.jsonl
+bl finetune text create --base-model qwen3-8b --datasets file-aaa,./extra.jsonl
 ```

 ```bash
-bl finetune text create --model qwen3-8b --datasets ./train.jsonl --training-type sft
+bl finetune text create --base-model qwen3-8b --datasets ./train.jsonl --training-type sft
 ```

 ```bash
-bl finetune text create --model qwen3-8b --datasets file-xxx --learning-rate "1.6e-5" --n-epochs 4
+bl finetune text create --base-model qwen3-8b --datasets file-xxx --learning-rate "1.6e-5" --n-epochs 4
 ```

 ```bash
-bl finetune text create --model qwen3-8b --datasets file-xxx --output json
+bl finetune text create --base-model qwen3-8b --datasets file-xxx --output json
 ```

 ```bash
-bl finetune text create --model qwen3-8b --datasets file-xxx --dry-run
+bl finetune text create --base-model qwen3-8b --datasets file-xxx --dry-run
+```
+
+### `bl finetune video create`
+
+| Field           | Value                                                                                                                                                                                                |
+| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Name**        | `finetune video create`                                                                                                                                                                              |
+| **Description** | Create a video generation model fine-tune job (Wan i2v/kf2v, efficient_sft)                                                                                                                          |
+| **Usage**       | `bl finetune video create --base-model <model> --datasets <id\|path> [--validations <id\|path>] [--model-name <name>] [--suffix <text>] [--n-epochs <n>] [--batch-size <n>] [--learning-rate <str>]` |
+
+#### Flags
+
+| Flag                         | Type   | Required | Description                                                                                                                                                        |
+| ---------------------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `--base-model <model>`       | string | yes      | Base model to fine-tune (e.g. qwen3-8b; not the output model name)                                                                                                 |
+| `--datasets <ids\|paths>`    | string | yes      | Comma-separated dataset file IDs or local paths (.jsonl for text, .zip for audio/image). Local paths are uploaded (validated) first, then their file-ids are used. |
+| `--validations <ids\|paths>` | string | no       | Comma-separated validation dataset file IDs or local paths (auto-uploaded like --datasets).                                                                        |
+| `--model-name <name>`        | string | no       | Output model name (after training)                                                                                                                                 |
+| `--suffix <text>`            | string | no       | Output suffix appended by the platform (finetuned_output_suffix)                                                                                                   |
+| `--n-epochs <n>`             | number | no       | Training epochs (default: 50)                                                                                                                                      |
+| `--batch-size <n>`           | number | no       | Batch size (default: model-specific, 1 for wan2.7, 4 for wan2.5/2.2)                                                                                               |
+| `--learning-rate <str>`      | string | no       | Learning rate as a string to preserve precision (default: "2e-5")                                                                                                  |
+| `--api-key <key>`            | string | no       | API key                                                                                                                                                            |
+| `--base-url <url>`           | string | no       | API base URL                                                                                                                                                       |
+
+#### Notes
+
+- Creating a job uploads any local datasets and consumes training quota.
+- Use --dry-run to preview the request body without submitting.
+- --datasets / --validations accept either file-ids (from `dataset upload`)
+- or local paths. Local paths are validated and uploaded first, then their
+- file-ids are submitted — a one-step upload-and-train.
+- Video generation training (Wan i2v/kf2v) runs efficient_sft with model-
+- specific defaults: wan2.7 (batch_size=1, max_pixels=102400), wan2.5/2.2
+- (batch_size=4, max_pixels per model). Override with --batch-size/--n-epochs.
+- Datasets are .zip archives with data.jsonl + frame images + videos.
+- Recommended: ≥10 training samples, 20-100 for stable results.
+
+#### Examples
+
+```bash
+bl finetune video create --base-model wan2.7-i2v --datasets file-xxx
+```
+
+```bash
+bl finetune video create --base-model wan2.7-i2v --datasets ./i2v-data.zip
+```
+
+```bash
+bl finetune video create --base-model wan2.2-kf2v-flash --datasets file-xxx --n-epochs 100
+```
+
+```bash
+bl finetune video create --base-model wan2.7-i2v --datasets file-xxx --dry-run
 ```

 ### `bl finetune watch`
@@ -22,6 +22,8 @@ Use this index for the skill-scoped quick index and global flags.
 | `bl deploy image create`   | Create an image generation model deployment                                                                                     | [deploy.md](deploy.md)     |
 | `bl deploy list`           | List model deployments                                                                                                          | [deploy.md](deploy.md)     |
 | `bl deploy models`         | List models available for deployment                                                                                            | [deploy.md](deploy.md)     |
+| `bl deploy pause`          | Pause a running model deployment (stops billing for mu/ptu)                                                                     | [deploy.md](deploy.md)     |
+| `bl deploy resume`         | Resume a paused model deployment (brings service back online)                                                                   | [deploy.md](deploy.md)     |
 | `bl deploy scale`          | Scale a deployment's capacity                                                                                                   | [deploy.md](deploy.md)     |
 | `bl deploy text create`    | Create a text model deployment                                                                                                  | [deploy.md](deploy.md)     |
 | `bl deploy update`         | Update a deployment's rate limits (rpm_limit / tpm_limit)                                                                       | [deploy.md](deploy.md)     |
@@ -35,16 +37,18 @@ Use this index for the skill-scoped quick index and global flags.
 | `bl finetune image create` | Create an image generation model fine-tune job (sft-lora)                                                                       | [finetune.md](finetune.md) |
 | `bl finetune list`         | List fine-tune jobs                                                                                                             | [finetune.md](finetune.md) |
 | `bl finetune logs`         | Fetch training logs for a fine-tune job                                                                                         | [finetune.md](finetune.md) |
+| `bl finetune price`        | Estimate the training cost for a fine-tune job (token billing)                                                                  | [finetune.md](finetune.md) |
 | `bl finetune text create`  | Create a text model fine-tune job (sft \| sft-lora \| dpo \| dpo-lora \| cpt)                                                   | [finetune.md](finetune.md) |
+| `bl finetune video create` | Create a video generation model fine-tune job (Wan i2v/kf2v, efficient_sft)                                                     | [finetune.md](finetune.md) |
 | `bl finetune watch`        | Probe a fine-tune job's status (default: single non-blocking fetch). Pass --follow to poll until terminal.                      | [finetune.md](finetune.md) |

 ## By group

-| Group      | Commands                                                                                                                                 | Reference                  |
-| ---------- | ---------------------------------------------------------------------------------------------------------------------------------------- | -------------------------- |
-| `dataset`  | `delete`, `get`, `list`, `upload`, `validate`                                                                                            | [dataset.md](dataset.md)   |
-| `deploy`   | `audio create`, `delete`, `get`, `image create`, `list`, `models`, `scale`, `text create`, `update`                                      | [deploy.md](deploy.md)     |
-| `finetune` | `audio create`, `cancel`, `capability`, `checkpoints`, `delete`, `export`, `get`, `image create`, `list`, `logs`, `text create`, `watch` | [finetune.md](finetune.md) |
+| Group      | Commands                                                                                                                                                          | Reference                  |
+| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------- |
+| `dataset`  | `delete`, `get`, `list`, `upload`, `validate`                                                                                                                     | [dataset.md](dataset.md)   |
+| `deploy`   | `audio create`, `delete`, `get`, `image create`, `list`, `models`, `pause`, `resume`, `scale`, `text create`, `update`                                            | [deploy.md](deploy.md)     |
+| `finetune` | `audio create`, `cancel`, `capability`, `checkpoints`, `delete`, `export`, `get`, `image create`, `list`, `logs`, `price`, `text create`, `video create`, `watch` | [finetune.md](finetune.md) |

 ## Global flags

@@ -1,7 +1,7 @@
 ---
 name: bailian-gen
 metadata:
-  version: "1.14.1"
+  version: "1.14.2"
  requires:
    bins: ["bl"]
 description: >-
@@ -108,6 +108,7 @@ bl video edit --video https://example.com/input.mp4 --prompt "Put clothes on the
 | `--model <model>`           | string  | no       | Model ID (default: happyhorse-1.1-t2v, or happyhorse-1.1-i2v with --image)              |
 | `--prompt <text>`           | string  | yes      | Video description                                                                       |
 | `--image <url>`             | string  | no       | Input image URL for image-to-video generation                                           |
+| `--last-frame <url>`        | string  | no       | Last frame image URL (with --image, enables kf2v first+last frame mode)                 |
 | `--negative-prompt <text>`  | string  | no       | Negative prompt to exclude unwanted content                                             |
 | `--resolution <res>`        | string  | no       | Resolution: 720P or 1080P (default: 1080P)                                              |
 | `--ratio <ratio>`           | string  | no       | Aspect ratio (e.g. 16:9, 9:16, 1:1)                                                     |
@@ -1,7 +1,7 @@
 ---
 name: bailian-managed-agent
 metadata:
-  version: "1.14.1"
+  version: "1.14.2"
  requires:
    bins: ["bl"]
 description: >-
@@ -1,7 +1,7 @@
 ---
 name: bailian-protocol
 metadata:
-  version: "1.14.1"
+  version: "1.14.2"
  requires:
    bins: ["bl"]
 description: >-
Author	SHA1	Message	Date
故璃	1c76749ee5	feat: update datalist validate	2026-08-11 13:33:31 +08:00
故璃	5d1b7aac3a	Merge branch 'main' into feat/cli-skill-sync	2026-08-11 10:47:45 +08:00
gujieye	2389681ad6	Merge pull request #144 from modelstudioai/feat/add-version-tag feat: add version 1.14.2	2026-08-07 18:02:08 +08:00
故璃	946b7029c6	feat: add version 1.14.2	2026-08-07 17:42:31 +08:00
gujieye	b9ecd5c43b	Merge pull request #143 from modelstudioai/feat/skill-init-commend feat: add skill init & opt commend flags	2026-08-07 17:26:21 +08:00
Gong Shiqi	978f332fea	Merge pull request #142 from modelstudioai/docs/update-readme-and-agent-guides docs: refresh READMEs and auth maintenance guidance	2026-08-07 17:24:52 +08:00
故璃	4502424200	feat: add skill init & opt commend flags	2026-08-07 17:17:27 +08:00
若麒	03839766bc	docs: update READMEs	2026-08-07 17:15:26 +08:00
故璃	5007b9b574	feat: change output to json	2026-08-07 16:35:47 +08:00
若麒	1f8b9ace7e	docs: refine auth maintenance guidance	2026-08-07 15:27:46 +08:00
故璃	8286a74fb6	test: remove sync pipeline verification marker	2026-08-07 14:40:33 +08:00
故璃	9eb2acbb65	test: trigger skills sync pipeline	2026-08-07 14:38:40 +08:00
故璃	1d35326c86	ci: read FC trigger url from variables	2026-08-07 14:36:42 +08:00
故璃	eb6c2b8e2a	Merge branch 'feat/model-finetune-opt' into feat/cli-skill-sync	2026-08-07 14:26:38 +08:00
故璃	ebd6226a9f	ci: rename trigger secret to FC_TRIGGER_URL	2026-08-07 14:25:49 +08:00
故璃	e25d3b0b8e	ci: add workflow to publish skills to OSS	2026-08-07 14:13:47 +08:00
故璃	3c64461cca	feat: video finetune/deploy/invoke full pipeline + training cost calculation - Add finetune video create subcommand (wan2.7/2.5/2.2 i2v + kf2v) - Align video hyperparams with official docs (n_epochs=50, per-model batch_size/max_pixels) - Add --last-frame flag to video generate for kf2v (image2video endpoint) - Fix wan2.1-2.6 i2v input format (img_url instead of media[]) - Add training_cost field to finetune get/watch (catalog ft price, API-key domain only) - Add --aigc-* flags to deploy create (optional, for video LoRA prompt config)	2026-08-07 07:03:01 +08:00
故璃	f30fff9065	feat(finetune): clarify model flags; add price estimate & actual cost - Rename for clarity: finetune --model → --base-model (create/price/ capability/list); deploy create --model → --model-name, --name → --display-name - Add `finetune price` (console domain) for pre-training cost estimate (sft/dpo/cpt) - Add actual training cost (fee.ts) enriched into finetune get/watch from catalog price × reported usage	2026-08-06 15:08:02 +08:00
故璃	a7245c0f62	feat(deploy): add pause/resume commands; JSON-only output for dataset/finetune/deploy - Add `bl deploy pause` and `bl deploy resume` (console domain, first console-auth commands in deploy group) via modelInstance start/stop APIs - Add core deploy/lifecycle.ts with input-wrapped console gateway calls - Switch all dataset/finetune/deploy commands to JSON-only output, removing text formatting logic - Expose usage/charge_type in finetune get, model_name/expire_time in finetune checkpoints with near-expiry warning - Update deploy delete hint to suggest `bl deploy pause`	2026-08-06 11:34:48 +08:00