mirror of
https://github.com/lingzhi227/agent-research-skills.git
synced 2026-09-20 09:33:06 +08:00
Add skills design spec and implementation checklist
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Lingzhi Yang
@@ -0,0 +1,768 @@
|
||||
# Research Paper Writing: Claude Skill Modules
|
||||
|
||||
> Extracted from 17 GitHub repos studying LLM-agent-driven research automation.
|
||||
> Each skill is a self-contained, reusable Claude Code skill for the research paper lifecycle.
|
||||
|
||||
---
|
||||
|
||||
## Skill Map Overview
|
||||
|
||||
```
|
||||
Phase 0: Research Planning
|
||||
[S01] research-planning — 研究规划与论文架构设计
|
||||
[S02] idea-generation — 研究想法生成与新颖性评估
|
||||
|
||||
Phase 1: Literature & Knowledge
|
||||
[S03] literature-search — 文献检索（Semantic Scholar / arXiv / Google Scholar）
|
||||
[S04] literature-review — 文献综述生成（多视角对话模拟）
|
||||
[S05] related-work-writing — Related Work 段落撰写
|
||||
|
||||
Phase 2: Formalization
|
||||
[S06] math-reasoning — 数学推理、公式推导、定理证明
|
||||
[S07] algorithm-design — 算法设计与伪代码生成
|
||||
[S08] atomic-decomposition — 原子概念分解（数学公式 ↔ 代码双向映射）
|
||||
|
||||
Phase 3: Implementation
|
||||
[S09] experiment-code — 实验代码编写（ML training/eval pipeline）
|
||||
[S10] code-debugging — 代码调试与自动修复
|
||||
[S11] experiment-design — 实验设计（baselines、ablation、hyperparameter）
|
||||
|
||||
Phase 4: Results & Visualization
|
||||
[S12] data-analysis — 数据分析与统计检验
|
||||
[S13] figure-generation — 科研图表生成（matplotlib + VLM反馈闭环）
|
||||
[S14] table-generation — LaTeX 表格生成（实验结果 → 发表级表格）
|
||||
|
||||
Phase 5: Writing
|
||||
[S15] paper-writing-section — 逐节论文撰写（Abstract → Conclusion）
|
||||
[S16] latex-formatting — LaTeX 格式、模板、编译
|
||||
[S17] citation-management — BibTeX 引用管理与引文插入
|
||||
|
||||
Phase 6: Review & Polish
|
||||
[S18] self-review — 自动审稿（NeurIPS/ICLR review form）
|
||||
[S19] rebuttal-writing — Rebuttal 撰写（逐条回复审稿意见）
|
||||
[S20] paper-revision — 基于审稿意见的论文修改
|
||||
|
||||
Phase 7: Assembly & Delivery
|
||||
[S21] paper-assembly — 论文全流程整合（数学+图表+数据+引用 → 完整论文）
|
||||
[S22] paper-compilation — LaTeX 编译与错误修复
|
||||
[S23] backward-traceability — 数据可追溯（PDF中数字 → 生成代码行的超链接）
|
||||
|
||||
Phase 8: Extended
|
||||
[S24] survey-generation — 综述论文自动生成
|
||||
[S25] paper-to-code — 论文 → 可运行代码仓库
|
||||
[S26] slide-generation — 论文 → 演示幻灯片/Poster
|
||||
[S27] novelty-assessment — 研究新颖性评估与文献对比
|
||||
[S28] symbolic-equation — 科学方程发现（符号回归 + LLM引导）
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Detailed Skill Specifications
|
||||
|
||||
---
|
||||
|
||||
### S01: research-planning — 研究规划与论文架构设计
|
||||
|
||||
**Source repos:** AI-Researcher (plan_agent), AgentLaboratory (plan_formulation), Paper2Code (1_planning.py)
|
||||
|
||||
**What it does:**
|
||||
- Takes a research topic/idea as input
|
||||
- Outputs: research questions, methodology outline, paper structure, section-by-section plan
|
||||
- Generates Mermaid class/sequence diagrams for system architecture (from Paper2Code)
|
||||
- Creates a dependency graph of what needs to be done first
|
||||
|
||||
**Key techniques extracted:**
|
||||
1. **4-turn planning conversation** (Paper2Code): Overall plan → Architecture design (UML) → Logic analysis (task list) → Config extraction
|
||||
2. **Diverge-converge framework** (AI-Researcher): Generate 5 orthogonal research directions, score by novelty/reliability/potential, deepen the best
|
||||
3. **Plan formulation dialogue** (AgentLaboratory): Postdoc-PhD role-play to refine plan iteratively
|
||||
|
||||
**Prompt template core:**
|
||||
```
|
||||
You are an experienced research advisor. Given a research topic and optional reference papers:
|
||||
|
||||
1. Identify the core research question and its significance
|
||||
2. Propose a methodology with clear steps
|
||||
3. Design the paper structure (8 sections: Abstract, Introduction, Background, Related Work, Methods, Experiments, Results, Discussion)
|
||||
4. Create a dependency-ordered task list for implementation
|
||||
5. Identify key baselines, datasets, and evaluation metrics
|
||||
6. Flag risks and potential failure modes
|
||||
|
||||
Output format: structured JSON with sections, tasks, dependencies, and timeline.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S02: idea-generation — 研究想法生成与新颖性评估
|
||||
|
||||
**Source repos:** AI-Scientist (generate_ideas.py), SciMON, AI-Researcher (idea_agent)
|
||||
|
||||
**What it does:**
|
||||
- Given a research area + existing codebase, generates novel research ideas
|
||||
- Iterative reflection (up to 5 rounds) to refine each idea
|
||||
- Novelty check against Semantic Scholar / arXiv
|
||||
- Scores ideas on: Interestingness, Feasibility, Novelty (1-10)
|
||||
|
||||
**Key techniques:**
|
||||
1. **Seed idea bootstrapping** (AI-Scientist): Start with 1-2 hand-written ideas, generate more conditioned on archive
|
||||
2. **Reflection convergence** (AI-Scientist): Refine until LLM says "I am done"
|
||||
3. **Literature-grounded novelty check** (AI-Scientist): Up to 10 rounds of Semantic Scholar queries
|
||||
4. **Novelty optimization loop** (SciMON): Retrieve inspirations → iteratively optimize for novelty
|
||||
|
||||
**Output per idea:**
|
||||
```json
|
||||
{
|
||||
"Name": "adaptive_attention_pruning",
|
||||
"Title": "Adaptive Attention Head Pruning via Gradient-Guided Importance Scoring",
|
||||
"Experiment": "detailed implementation plan...",
|
||||
"Interestingness": 8,
|
||||
"Feasibility": 7,
|
||||
"Novelty": 9
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S03: literature-search — 文献检索
|
||||
|
||||
**Source repos:** STORM (rm.py), AI-Scientist (generate_ideas.py), data-to-paper (literature_search.py), OpenResearcher
|
||||
|
||||
**What it does:**
|
||||
- Multi-source academic search: Semantic Scholar, arXiv, OpenAlex, CrossRef, Google Scholar
|
||||
- Returns structured results: title, authors, year, venue, abstract, citation count, BibTeX
|
||||
- Supports embedding-based similarity sorting
|
||||
- Filters by citation count, recency, relevance
|
||||
|
||||
**API integrations:**
|
||||
- Semantic Scholar: `api.semanticscholar.org/graph/v1/paper/search` (fields: title, authors, venue, year, abstract, citationCount, citationStyles)
|
||||
- arXiv: `arxiv` Python package
|
||||
- OpenAlex: `pyalex` library
|
||||
- CrossRef: REST API for DOI-based lookup
|
||||
|
||||
---
|
||||
|
||||
### S04: literature-review — 文献综述生成
|
||||
|
||||
**Source repos:** STORM (knowledge_curation.py), AutoSurvey, AgentLaboratory (literature_review phase)
|
||||
|
||||
**What it does:**
|
||||
- Multi-perspective dialogue simulation: generates N expert personas, each conducts multi-turn Q&A
|
||||
- Each persona asks questions from their unique angle, grounded by live search results
|
||||
- Synthesizes all conversations into a structured knowledge base
|
||||
- Citation precision ~85%, recall ~85% (STORM benchmark)
|
||||
|
||||
**Key techniques:**
|
||||
1. **Persona generation** (STORM): Find related Wikipedia topics → extract TOC → LLM generates diverse editor personas
|
||||
2. **Grounded expert answers** (STORM): TopicExpert converts questions to search queries, retrieves top-k results, synthesizes answer with inline citations
|
||||
3. **Parallel persona execution** (STORM): ThreadPoolExecutor runs all persona conversations concurrently
|
||||
4. **Semantic retrieval** (STORM): SentenceTransformer embeddings + cosine similarity for per-section information retrieval
|
||||
|
||||
---
|
||||
|
||||
### S05: related-work-writing — Related Work 段落撰写
|
||||
|
||||
**Source repos:** STORM, LitLLM, AI-Scientist (perform_writeup.py)
|
||||
|
||||
**What it does:**
|
||||
- Takes collected literature + paper draft as input
|
||||
- Generates a Related Work section with proper citations
|
||||
- "Compare and contrast" style, not just descriptions
|
||||
- Sentence-level planning (LitLLM): keyword extraction → retrieval → re-ranking → generation
|
||||
|
||||
**Prompt core (from AI-Scientist per_section_tips):**
|
||||
```
|
||||
Related Work should compare and contrast prior work with your approach.
|
||||
Don't just describe what others did — explain how your work differs and why.
|
||||
Organize by theme, not chronologically.
|
||||
Cite broadly — not just the most popular papers.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S06: math-reasoning — 数学推理、公式推导、定理证明
|
||||
|
||||
**Source repos:** AI-Researcher (survey_agent math extraction), LLM-SR, data-to-paper (analysis coding)
|
||||
|
||||
**What it does:**
|
||||
- Formal mathematical derivation with step-by-step reasoning
|
||||
- Theorem statement and proof generation in LaTeX
|
||||
- Equation numbering and cross-referencing
|
||||
- Statistical test selection and interpretation
|
||||
- Symbolic equation discovery (LLM-SR: evolutionary search for scientific equations)
|
||||
|
||||
**Key techniques:**
|
||||
1. **Math formula extraction from papers** (AI-Researcher Paper Survey Agent): Navigate paper → find formula → extract LaTeX
|
||||
2. **Backward-traceable computations** (data-to-paper): `\num{formula}` → evaluated at compile time → hyperlinked to source
|
||||
3. **LLM-guided symbolic regression** (LLM-SR): LLM proposes candidate equations → evolutionary search optimizes fit
|
||||
|
||||
---
|
||||
|
||||
### S07: algorithm-design — 算法设计与伪代码生成
|
||||
|
||||
**Source repos:** Paper2Code (planning stage), AI-Researcher (plan_agent)
|
||||
|
||||
**What it does:**
|
||||
- Generates algorithm pseudocode in LaTeX `algorithmic` environment
|
||||
- Creates UML class diagrams (Mermaid syntax)
|
||||
- Designs sequence diagrams for system flow
|
||||
- Ensures consistency between pseudocode and actual implementation
|
||||
|
||||
**Prompt pattern:**
|
||||
```
|
||||
Given a method description:
|
||||
1. Formalize the algorithm with clear input/output
|
||||
2. Write pseudocode using \begin{algorithmic} environment
|
||||
3. Generate a Mermaid classDiagram showing data structures
|
||||
4. Generate a Mermaid sequenceDiagram showing execution flow
|
||||
5. Verify consistency: every pseudocode step must map to a code module
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S08: atomic-decomposition — 原子概念分解
|
||||
|
||||
**Source repos:** AI-Researcher (survey_agent.py) — this is its core innovation
|
||||
|
||||
**What it does:**
|
||||
- Decomposes a research idea into atomic, self-contained concepts
|
||||
- For each atom: extracts math formula from papers + finds code implementation in repos
|
||||
- Bidirectional mapping: `math_formula ↔ code_implementation`
|
||||
- Creates a structured note for each concept with: definition, formula, code, references
|
||||
|
||||
**Output format:**
|
||||
```json
|
||||
{
|
||||
"definition": "Kernelized Gumbel-Softmax Operator",
|
||||
"math_formula": "Z = softmax((log π + g) / τ), g ~ Gumbel(0,1)",
|
||||
"code_implementation": "def gumbel_softmax(logits, tau=1.0): ...",
|
||||
"reference_papers": ["NodeFormer: A Scalable Graph Structure Learning Transformer"],
|
||||
"reference_codebases": ["LarFii/nodeformer"]
|
||||
}
|
||||
```
|
||||
|
||||
**Why this matters:** Ensures every mathematical concept in the paper has a verified code implementation, and every code module traces back to a formal mathematical definition.
|
||||
|
||||
---
|
||||
|
||||
### S09: experiment-code — 实验代码编写
|
||||
|
||||
**Source repos:** AI-Scientist (perform_experiments.py), AgentLaboratory (mlesolver.py), AI-Researcher (ml_agent.py)
|
||||
|
||||
**What it does:**
|
||||
- Generates complete ML training/evaluation pipelines
|
||||
- Iterative code improvement via REPLACE/EDIT operations
|
||||
- Self-debugging: runs code, captures errors, fixes automatically (up to 4 retries)
|
||||
- Hill-climbing optimization: maintains pool of best-scoring code candidates
|
||||
|
||||
**Key techniques:**
|
||||
1. **Aider diff-mode editing** (AI-Scientist): LLM outputs unified diffs applied to experiment files
|
||||
2. **REPLACE/EDIT operations** (AgentLaboratory): Two editing primitives with line-range targeting
|
||||
3. **Mandatory project structure** (AI-Researcher): `data/`, `model/`, `training/`, `testing/`, `run_training_testing.py`
|
||||
4. **Code reflection** (AgentLaboratory): After improvement, reflect on what worked → inject insights into next iteration
|
||||
|
||||
**Constraints enforced:**
|
||||
- No placeholder code (`pass`, `...`, `raise NotImplementedError`)
|
||||
- Must use actual datasets (not toy data)
|
||||
- Must generate figures (Figure_1.png, Figure_2.png minimum)
|
||||
- PyTorch or scikit-learn only (no TensorFlow/Keras)
|
||||
|
||||
---
|
||||
|
||||
### S10: code-debugging — 代码调试与自动修复
|
||||
|
||||
**Source repos:** AI-Scientist (perform_experiments.py), data-to-paper (debugger.py), AI-Scientist-v2 (parallel_agent.py _debug())
|
||||
|
||||
**What it does:**
|
||||
- Captures runtime errors from experiment execution
|
||||
- Feeds truncated stderr + code context back to LLM
|
||||
- Structured error categorization: ImportError, FileNotFoundError, TimeoutError, etc.
|
||||
- State machine for fix strategy: "repost" / "leave" / "regen"
|
||||
- Up to 5 repair attempts with code reflection
|
||||
|
||||
**Key techniques:**
|
||||
1. **Truncated error feedback** (AI-Scientist): Last N lines of stderr fed back
|
||||
2. **Structured RunIssue objects** (data-to-paper): category + description + fix instructions
|
||||
3. **Monkey-patched sandbox** (data-to-paper): Override pandas, scipy, sklearn to track operations and enforce safety
|
||||
4. **Automated code repair tool** (AgentLaboratory): Dedicated "repair" system prompt distinct from "generate" prompt
|
||||
|
||||
---
|
||||
|
||||
### S11: experiment-design — 实验设计
|
||||
|
||||
**Source repos:** AI-Scientist-v2 (agent_manager.py 4-stage), AI-Researcher (exp_analyser.py)
|
||||
|
||||
**What it does:**
|
||||
- Designs complete experiment plans: baselines, ablations, hyperparameter sweeps
|
||||
- 4-stage progressive experiment management:
|
||||
- Stage 1: Initial implementation (simple dataset, working baseline)
|
||||
- Stage 2: Baseline tuning (hyperparameters, multiple datasets)
|
||||
- Stage 3: Creative research (novel improvements, 3+ datasets)
|
||||
- Stage 4: Ablation studies (systematic component analysis)
|
||||
- Multi-seed evaluation for statistical significance
|
||||
- VLM-evaluated stage completion (training curves checked for convergence)
|
||||
|
||||
**Key techniques:**
|
||||
1. **Progressive staging** (AI-Scientist-v2): Each stage has explicit goals and completion criteria
|
||||
2. **Ablation study design** (AI-Researcher): Analyzes results → proposes component removal experiments
|
||||
3. **Sensitivity analysis** (AI-Researcher): Tests key hyperparameters across ranges
|
||||
4. **Best-node selection** (AI-Scientist-v2): LLM holistically selects best experiment considering metrics + VLM plot analysis
|
||||
|
||||
---
|
||||
|
||||
### S12: data-analysis — 数据分析与统计检验
|
||||
|
||||
**Source repos:** data-to-paper (analysis coding), AgentLaboratory (results_interpretation)
|
||||
|
||||
**What it does:**
|
||||
- Generates Python code for statistical analysis of experimental results
|
||||
- Selects appropriate statistical tests (t-test, ANOVA, chi-square, etc.)
|
||||
- Interprets p-values, effect sizes, confidence intervals
|
||||
- Produces structured analysis reports with numerical evidence
|
||||
|
||||
**Key techniques:**
|
||||
1. **4-round code review** (data-to-paper):
|
||||
- Round 1: Fundamental math/statistics flaws
|
||||
- Round 2: Data handling issues
|
||||
- Round 3: Per-table review
|
||||
- Round 4: Cross-table completeness
|
||||
2. **Allowed packages whitelist** (data-to-paper): pandas, numpy, scipy, statsmodels, sklearn only
|
||||
3. **Results interpretation dialogue** (AgentLaboratory): Postdoc guides PhD to extract meaningful insights
|
||||
|
||||
---
|
||||
|
||||
### S13: figure-generation — 科研图表生成
|
||||
|
||||
**Source repos:** MatPlotAgent, AI-Scientist-v2 (VLM feedback), AI-Scientist (plot.py)
|
||||
|
||||
**What it does:**
|
||||
- Generates publication-quality matplotlib/seaborn figures
|
||||
- 3-module closed loop: Query expansion → Code generation → VLM visual feedback
|
||||
- VLM (GPT-4V) evaluates figure quality: data representation, labels, colors, readability
|
||||
- Automatic code retry on execution errors (up to 4 attempts)
|
||||
- Professional styling with consistent color palettes
|
||||
|
||||
**Key techniques:**
|
||||
1. **Query expansion** (MatPlotAgent): Raw instruction → step-by-step coding instructions before code generation
|
||||
2. **VLM feedback loop** (MatPlotAgent): Generated PNG + original query → GPT-4V → improvement instructions → regenerate code
|
||||
3. **Plot aggregation** (AI-Scientist-v2): Multiple experiment `.npy` files → unified ~12 final figures
|
||||
4. **Figure-caption alignment review** (AI-Scientist-v2): VLM checks each figure's caption accuracy and informativeness
|
||||
|
||||
**Score improvement:** GPT-4 baseline 48.86 → with MatPlotAgent 61.16 (+12.3 points)
|
||||
|
||||
---
|
||||
|
||||
### S14: table-generation — LaTeX 表格生成
|
||||
|
||||
**Source repos:** data-to-paper (displayitems stage), AI-Researcher (comparison tables)
|
||||
|
||||
**What it does:**
|
||||
- Converts experimental results (JSON/CSV/DataFrame) to publication-ready LaTeX tables
|
||||
- Handles: `booktabs` styling, bold best results, multi-row/multi-column layouts
|
||||
- Baseline comparison tables from prior work (scraped from papers)
|
||||
- Ablation study tables with component-wise analysis
|
||||
|
||||
**Prompt pattern:**
|
||||
```
|
||||
Given experimental results in JSON format:
|
||||
1. Design table layout (rows = methods, columns = metrics/datasets)
|
||||
2. Generate LaTeX using booktabs package (\toprule, \midrule, \bottomrule)
|
||||
3. Bold the best result in each column
|
||||
4. Add proper captions and labels
|
||||
5. Ensure all numbers match the actual experimental logs — do not hallucinate
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S15: paper-writing-section — 逐节论文撰写
|
||||
|
||||
**Source repos:** AI-Scientist (perform_writeup.py), AgentLaboratory (papersolver.py), data-to-paper (writing stages)
|
||||
|
||||
**What it does:**
|
||||
- Writes each paper section individually with section-specific guidance
|
||||
- Two refinement passes per section (AI-Scientist): criticize → fix errors → compress
|
||||
- Scaffold generation (AgentLaboratory): skeleton → fill section by section
|
||||
- Word count enforcement (~4000 words for 8-page paper)
|
||||
|
||||
**Per-section tips (from AI-Scientist + AgentLaboratory):**
|
||||
|
||||
| Section | Key Guidance |
|
||||
|---------|-------------|
|
||||
| **Abstract** | TL;DR → why it's hard → contribution → how verified. Single paragraph only. |
|
||||
| **Introduction** | Longer abstract; list contributions as bullet points. |
|
||||
| **Background** | Problem setting with formal notation; define all symbols. |
|
||||
| **Related Work** | Compare and contrast, not just describe. Organize by theme. |
|
||||
| **Methods** | Precise mathematical equations; what we do AND why. |
|
||||
| **Experimental Setup** | Datasets, metrics, hyperparameters. Don't hallucinate hardware details. |
|
||||
| **Results** | Only report numbers from actual logs. Include ablations. Include all figures. |
|
||||
| **Discussion/Conclusion** | Brief recap → limitations → future work as "academic offspring." |
|
||||
|
||||
**Two-pass refinement (AI-Scientist):**
|
||||
```
|
||||
Pass 1: Criticize the section. Fix: unenclosed math, broken refs, LaTeX errors,
|
||||
hallucinated numbers, duplicate labels, verbosity.
|
||||
Pass 2: Identify redundancies. Save space. Be more concise without weakening the message.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S16: latex-formatting — LaTeX 格式、模板、编译
|
||||
|
||||
**Source repos:** AI-Scientist (template.tex), AgentLaboratory (papersolver.py auto-inject), data-to-paper (latex_to_pdf.py)
|
||||
|
||||
**What it does:**
|
||||
- Manages LaTeX templates for major venues (ICML, ICLR, NeurIPS, AAAI, ACL, ICBINB)
|
||||
- Auto-injects required packages (amsmath, booktabs, hyperref, algorithm, etc.)
|
||||
- Handles common formatting issues: overfull hboxes, missing labels, duplicate sections
|
||||
- Conference-specific formatting rules
|
||||
|
||||
**Auto-injected packages (from AgentLaboratory):**
|
||||
```
|
||||
amsmath, amssymb, array, algorithm, algorithmicx, algpseudocode,
|
||||
booktabs, colortbl, color, enumitem, fontawesome5, float, graphicx,
|
||||
hyperref, listings, makecell, multicol, multirow, pgffor, pifont,
|
||||
soul, sidecap, subcaption, titletoc, footmisc, url, wrapfig, xcolor, xspace
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S17: citation-management — BibTeX 引用管理
|
||||
|
||||
**Source repos:** AI-Scientist (perform_writeup.py citation harvesting), data-to-paper (literature_search.py)
|
||||
|
||||
**What it does:**
|
||||
- Iterative citation harvesting: LLM reads draft → identifies most needed citation → searches Semantic Scholar → selects from results → injects BibTeX
|
||||
- Pre-compilation validation: every `\cite{key}` must exist in `.bib`
|
||||
- Deduplication of citation entries
|
||||
- Proper BibTeX formatting with all required fields
|
||||
|
||||
**Citation loop (from AI-Scientist, 20 rounds):**
|
||||
```
|
||||
Round N:
|
||||
1. LLM reads current draft, identifies gap needing a citation
|
||||
2. LLM generates a Semantic Scholar search query
|
||||
3. API returns top-10 results with title/abstract/BibTeX
|
||||
4. LLM selects the most relevant paper(s)
|
||||
5. BibTeX appended to references.bib
|
||||
6. LLM integrates \cite{key} into the paper text
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S18: self-review — 自动审稿
|
||||
|
||||
**Source repos:** AI-Scientist (perform_review.py), AgentLaboratory (ReviewersAgent), ChatReviewer
|
||||
|
||||
**What it does:**
|
||||
- Structured review using NeurIPS/ICLR review form
|
||||
- Ensemble voting: 5 independent reviews → meta-review aggregation
|
||||
- Three reviewer personas: harsh-fair, harsh-critical, open-minded
|
||||
- Reflection loop: refine review up to 5 rounds
|
||||
- Scores: Originality, Quality, Clarity, Significance, Soundness, Presentation, Contribution (1-4), Overall (1-10)
|
||||
|
||||
**Review form fields:**
|
||||
```json
|
||||
{
|
||||
"Summary": "...",
|
||||
"Strengths": ["..."],
|
||||
"Weaknesses": ["..."],
|
||||
"Originality": 3,
|
||||
"Quality": 3,
|
||||
"Clarity": 3,
|
||||
"Significance": 3,
|
||||
"Soundness": 3,
|
||||
"Presentation": 3,
|
||||
"Contribution": 3,
|
||||
"Overall": 6,
|
||||
"Confidence": 4,
|
||||
"Decision": "Accept/Reject"
|
||||
}
|
||||
```
|
||||
|
||||
**Few-shot calibration (AI-Scientist):** Uses 3 real ICLR papers + reviews as reference:
|
||||
- "Attention Is All You Need" (scored 8/10 Accept)
|
||||
- Real ICLR submissions with known scores
|
||||
|
||||
---
|
||||
|
||||
### S19: rebuttal-writing — Rebuttal 撰写
|
||||
|
||||
**Source repos:** ChatReviewer (chat_response.py)
|
||||
|
||||
**What it does:**
|
||||
- Takes reviewer comments as input
|
||||
- Extracts concerns one by one
|
||||
- Generates point-by-point responses
|
||||
- Key instruction: "Reply with what we have done, not what we will do"
|
||||
- Proper rebuttal formatting: Concern → Response per reviewer
|
||||
|
||||
**Output format:**
|
||||
```
|
||||
# Response to Reviewers
|
||||
|
||||
## Reviewer #1
|
||||
**Concern #1:** [extracted concern]
|
||||
**Author Response:** [detailed response with evidence]
|
||||
|
||||
**Concern #2:** [extracted concern]
|
||||
**Author Response:** [detailed response with evidence]
|
||||
|
||||
## Reviewer #2
|
||||
...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S20: paper-revision — 基于审稿意见修改论文
|
||||
|
||||
**Source repos:** AI-Scientist (perform_improvement), AgentLaboratory (report_refinement with second_round)
|
||||
|
||||
**What it does:**
|
||||
- Takes review feedback and current paper draft
|
||||
- Maps each weakness/concern to specific sections needing changes
|
||||
- Applies targeted edits preserving paper structure
|
||||
- Re-compiles and re-reviews to verify improvement
|
||||
- Supports iterative revision loops
|
||||
|
||||
**Key technique (AgentLaboratory):** When PhD decides to revise:
|
||||
1. Copy current state to `prev_*` fields
|
||||
2. Reset all downstream phases (experiments, writing)
|
||||
3. Re-run the pipeline with reviewer feedback injected as notes
|
||||
4. Compare new scores vs previous scores
|
||||
|
||||
---
|
||||
|
||||
### S21: paper-assembly — 论文全流程整合
|
||||
|
||||
**Source repos:** AI-Scientist (launch_scientist.py), AI-Researcher (main_ai_researcher.py), AgentLaboratory (ai_lab_repo.py)
|
||||
|
||||
**What it does:**
|
||||
- Orchestrates the entire paper pipeline end-to-end
|
||||
- Manages state propagation between phases
|
||||
- Handles checkpointing and resumption
|
||||
- Integrates: literature → plan → code → experiments → figures → tables → writing → review
|
||||
|
||||
**Orchestration patterns:**
|
||||
1. **Sequential pipeline** (AI-Scientist): generate_ideas → experiments → writeup → review
|
||||
2. **Multi-agent state broadcasting** (AgentLaboratory): `set_agent_attr()` propagates results to all agents
|
||||
3. **FlowModule caching** (AI-Researcher): Cache each agent's output, support resume/replay
|
||||
4. **Copilot mode checkpoints** (AgentLaboratory): Human can intervene at any phase boundary
|
||||
|
||||
---
|
||||
|
||||
### S22: paper-compilation — LaTeX 编译与错误修复
|
||||
|
||||
**Source repos:** AI-Scientist (perform_writeup.py generate_latex), data-to-paper (latex_to_pdf.py)
|
||||
|
||||
**What it does:**
|
||||
- Full LaTeX compilation pipeline: `pdflatex → bibtex → pdflatex → pdflatex`
|
||||
- Pre-compilation validation:
|
||||
- All `\cite{key}` exist in `.bib`
|
||||
- All `\includegraphics{file}` exist as `.png`
|
||||
- No duplicate figure references
|
||||
- No duplicate `\section{}` headers
|
||||
- Error correction loop: `chktex` analysis → LLM fixes → recompile (up to 5 rounds)
|
||||
- Handles common errors: unescaped underscores, HTML syntax in LaTeX, unclosed environments
|
||||
|
||||
---
|
||||
|
||||
### S23: backward-traceability — 数据可追溯
|
||||
|
||||
**Source repos:** data-to-paper (the defining innovation)
|
||||
|
||||
**What it does:**
|
||||
- Every number in the final PDF hyperlinks back to the exact code line that produced it
|
||||
- Uses `\hypertarget{label}{value}` and `\hyperlink{label}{value}` LaTeX commands
|
||||
- `\num{formula, "explanation"}` evaluated at compile time via `eval()`
|
||||
- Appendix contains full code listing with hyperlink anchors
|
||||
- Calculation Notes section shows `formula = result` for each computed value
|
||||
|
||||
**Implementation:**
|
||||
1. Code output: Numbers tagged with `\hypertarget{R1a}{45.3}`
|
||||
2. Paper text: Author writes `\num{\hyperlink{R1a}{45.3}, "mean age"}`
|
||||
3. Compile time: `eval()` verifies formula, generates hyperlink
|
||||
4. Appendix: Code listing with `\hypertarget` at relevant lines
|
||||
5. Result: Click any number in PDF → jumps to code line that produced it
|
||||
|
||||
---
|
||||
|
||||
### S24: survey-generation — 综述论文自动生成
|
||||
|
||||
**Source repos:** AutoSurvey, STORM
|
||||
|
||||
**What it does:**
|
||||
- Generates complete academic survey papers
|
||||
- Parallel multi-LLM sub-section generation
|
||||
- RAG-based real-time knowledge updates
|
||||
- Multi-perspective coverage for comprehensive topic review
|
||||
|
||||
---
|
||||
|
||||
### S25: paper-to-code — 论文 → 可运行代码仓库
|
||||
|
||||
**Source repos:** Paper2Code (PaperCoder)
|
||||
|
||||
**What it does:**
|
||||
- Takes an ML paper PDF and generates a complete, runnable code repository
|
||||
- 3-stage pipeline: Planning (UML + dependency graph) → Analysis (per-file logic) → Coding (dependency-ordered generation)
|
||||
- Dependency-ordered code generation: each file sees all previously generated files
|
||||
- Mermaid diagrams as rigid interface contracts across all stages
|
||||
|
||||
**Key metrics:** PaperBench 44.26% vs baseline 16.4%, 85% rated helpful, only 0.48% code lines need human modification
|
||||
|
||||
---
|
||||
|
||||
### S26: slide-generation — 论文 → 演示幻灯片/Poster
|
||||
|
||||
**Source repos:** (Gap identified in research — no dedicated tool exists)
|
||||
|
||||
**What it does:**
|
||||
- Converts a completed paper into presentation slides (Beamer/PowerPoint)
|
||||
- Extracts key figures, tables, and equations
|
||||
- Creates a narrative flow suitable for oral presentation
|
||||
- Poster layout for conference poster sessions
|
||||
|
||||
**Note:** This is an identified gap. This skill would be novel — to be designed from best practices.
|
||||
|
||||
---
|
||||
|
||||
### S27: novelty-assessment — 研究新颖性评估
|
||||
|
||||
**Source repos:** AI-Scientist (check_idea_novelty), data-to-paper (assess_novelty stage), SciMON
|
||||
|
||||
**What it does:**
|
||||
- Takes a research idea and searches literature systematically
|
||||
- Multi-round search-evaluate loop (up to 10 rounds)
|
||||
- Harsh critic persona for novelty evaluation
|
||||
- Binary decision: Novel / Not Novel with justification
|
||||
- Tracks "most similar papers" for positioning
|
||||
|
||||
**System prompt (from AI-Scientist):**
|
||||
```
|
||||
Be a harsh critic for novelty. Ensure there is a sufficient contribution
|
||||
for a new conference or workshop paper. You will be given access to the
|
||||
Semantic Scholar API to survey the literature.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### S28: symbolic-equation — 科学方程发现
|
||||
|
||||
**Source repos:** LLM-SR
|
||||
|
||||
**What it does:**
|
||||
- Uses LLMs to discover scientific equations from data
|
||||
- LLM-guided evolutionary search over symbolic expression space
|
||||
- Combines neural reasoning with symbolic optimization
|
||||
- Outputs interpretable mathematical relationships
|
||||
|
||||
---
|
||||
|
||||
## Cross-Cutting Patterns Used Across Skills
|
||||
|
||||
### Pattern 1: Reflection Convergence
|
||||
Used in: S02, S15, S18
|
||||
```
|
||||
Loop until LLM says "I am done" or max_rounds:
|
||||
1. Generate output
|
||||
2. LLM critiques own output
|
||||
3. LLM refines output
|
||||
```
|
||||
|
||||
### Pattern 2: Code-Execute-Fix Loop
|
||||
Used in: S09, S10, S12, S13
|
||||
```
|
||||
Loop up to max_retries:
|
||||
1. LLM generates code
|
||||
2. Execute in subprocess
|
||||
3. If error: feed error back → LLM fixes
|
||||
4. If success: evaluate output quality
|
||||
```
|
||||
|
||||
### Pattern 3: Multi-Source Search + Selection
|
||||
Used in: S03, S17, S27
|
||||
```
|
||||
1. LLM generates search query
|
||||
2. API returns top-k results
|
||||
3. LLM selects most relevant
|
||||
4. Repeat with refined queries
|
||||
```
|
||||
|
||||
### Pattern 4: VLM Feedback Loop
|
||||
Used in: S13, S11
|
||||
```
|
||||
1. Generate visual artifact (figure/plot)
|
||||
2. Send image to VLM (GPT-4V)
|
||||
3. VLM provides improvement instructions
|
||||
4. Regenerate based on feedback
|
||||
```
|
||||
|
||||
### Pattern 5: Ensemble + Meta-Aggregation
|
||||
Used in: S18
|
||||
```
|
||||
1. Generate N independent outputs (e.g., 5 reviews)
|
||||
2. Aggregate via meta-review prompt
|
||||
3. Average numerical scores
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## GitHub Repos Cloned → `/Users/lingzhi/Code/research-engine/github/`
|
||||
|
||||
| Repo | Primary Skills | Stars |
|
||||
|------|---------------|-------|
|
||||
| AI-Scientist | S01, S02, S09, S15, S17, S18, S22 | 12.1k |
|
||||
| AI-Scientist-v2 | S11, S13, S15, S21 | 2.1k |
|
||||
| AI-Researcher | S01, S08, S09, S10, S11, S12 | 4.5k |
|
||||
| AgentLaboratory | S01, S04, S09, S15, S18, S20, S21 | 5.3k |
|
||||
| data-to-paper | S06, S10, S12, S14, S22, S23 | 756 |
|
||||
| storm | S04, S05, S24 | 27.9k |
|
||||
| AutoSurvey | S24 | 458 |
|
||||
| gpt-researcher | S03, S04 | 25.4k |
|
||||
| Paper2Code | S07, S25 | 4.2k |
|
||||
| MatPlotAgent | S13 | 105 |
|
||||
| ChatReviewer | S18, S19 | — |
|
||||
| SciMON | S02, S27 | — |
|
||||
| LitLLM | S05 | 21 |
|
||||
| LLM-SR | S28 | — |
|
||||
| MLR-Copilot | S01, S09, S11 | 67 |
|
||||
| OpenResearcher | S03 | 492 |
|
||||
| ReviewAdvisor | S18 | — |
|
||||
|
||||
---
|
||||
|
||||
## Recommended Priority for Implementation
|
||||
|
||||
### Tier 1 — Core (must-have, daily use)
|
||||
1. **S15** paper-writing-section — 最常用，每篇论文都需要
|
||||
2. **S03** literature-search — 基础设施
|
||||
3. **S17** citation-management — 每篇论文都需要
|
||||
4. **S06** math-reasoning — 理论论文核心
|
||||
5. **S13** figure-generation — 每篇论文都需要
|
||||
6. **S16** latex-formatting — 基础设施
|
||||
7. **S22** paper-compilation — 基础设施
|
||||
8. **S18** self-review — 提交前必做
|
||||
|
||||
### Tier 2 — High Value (significant time savings)
|
||||
9. **S01** research-planning — 大幅提升效率
|
||||
10. **S02** idea-generation — 创新起点
|
||||
11. **S09** experiment-code — 加速实现
|
||||
12. **S14** table-generation — 节省大量格式化时间
|
||||
13. **S05** related-work-writing — 耗时最多的部分之一
|
||||
14. **S04** literature-review — 深度文献综述
|
||||
15. **S19** rebuttal-writing — 审稿后必需
|
||||
|
||||
### Tier 3 — Advanced (specialized scenarios)
|
||||
16. **S11** experiment-design — 完善实验设计
|
||||
17. **S12** data-analysis — 数据密集型研究
|
||||
18. **S10** code-debugging — 复杂实验调试
|
||||
19. **S20** paper-revision — 大修场景
|
||||
20. **S27** novelty-assessment — 提交前评估
|
||||
21. **S08** atomic-decomposition — 复杂系统论文
|
||||
22. **S07** algorithm-design — 算法论文
|
||||
23. **S21** paper-assembly — 端到端自动化
|
||||
|
||||
### Tier 4 — Extended (nice-to-have)
|
||||
24. **S23** backward-traceability — 可重复性
|
||||
25. **S24** survey-generation — 综述论文
|
||||
26. **S25** paper-to-code — 复现工作
|
||||
27. **S26** slide-generation — 报告展示
|
||||
28. **S28** symbolic-equation — 特定领域
|
||||
@@ -0,0 +1,585 @@
|
||||
# Research Paper Writing: Implemented Claude Skills
|
||||
|
||||
> Implementation record for 30 skills deployed at `~/.claude/skills/`.
|
||||
> Companion to [SKILLS_DESIGN.md](./SKILLS_DESIGN.md) which contains the original design specifications.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
| Category | Count | With Scripts | Prompt-Only |
|
||||
|----------|-------|-------------|-------------|
|
||||
| Research Discovery & Planning | 6 | 4 | 2 |
|
||||
| Method Design | 4 | 0 | 4 |
|
||||
| Experiment Pipeline | 4 | 2 | 2 |
|
||||
| Paper Writing | 4 | 1 | 3 |
|
||||
| Figures, Tables & Citations | 4 | 4 | 0 |
|
||||
| LaTeX & Compilation | 3 | 3 | 0 |
|
||||
| Review & Polish | 5 | 3 | 2 |
|
||||
| **Total** | **30** | **17** | **13** |
|
||||
|
||||
Scripts total: 27 Python + 7 CJS (Excalidraw) across 15 skill directories.
|
||||
|
||||
---
|
||||
|
||||
## Phase 0: Research Discovery & Planning
|
||||
|
||||
### 1. `deep-research` — Systematic Literature Survey
|
||||
|
||||
**Design ref:** Pre-dates SKILLS_DESIGN.md (not in S01-S28). Most script-heavy skill.
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Orchestrates a 6-phase literature survey: Frontier (latest conferences) → Survey (35-80 papers) → Deep Dive (8-15 detailed reads) → Code & Tools (GitHub extraction) → Synthesis (cross-paper analysis) → Compilation (final report).
|
||||
- **Scripts (7):**
|
||||
- `search_semantic_scholar.py` — Semantic Scholar API search, returns JSONL with title/authors/year/venue/abstract/citations
|
||||
- `search_arxiv.py` — arXiv API search via `arxiv` package
|
||||
- `download_papers.py` — Download PDFs from URLs or arXiv IDs
|
||||
- `extract_pdf.py` — Extract text from PDF using PyMuPDF
|
||||
- `paper_db.py` — Merge, deduplicate, and manage paper databases (JSONL format)
|
||||
- `bibtex_manager.py` — Convert JSONL paper records to BibTeX entries
|
||||
- `compile_report.py` — Assemble final survey report from phase outputs
|
||||
|
||||
**Usage pattern:** Scripts handle API calls, PDF processing, and data management. Prompt guides Claude through analysis, synthesis, and gap identification at each phase.
|
||||
|
||||
---
|
||||
|
||||
### 2. `literature-search` — Academic Paper Search
|
||||
|
||||
**Design ref:** S03
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Expands user query into 2-4 complementary searches, runs across 3+ APIs, merges and deduplicates results, ranks by citations (0.3) + recency (0.3) + venue quality (0.2) + relevance (0.2).
|
||||
- **Scripts (4 own + shared from deep-research):**
|
||||
- `search_crossref.py` — CrossRef API search with BibTeX generation, type mapping (article/inproceedings/book). Stdlib-only. *New.*
|
||||
- `download_arxiv_source.py` — Search arXiv by title, download source tarball, extract .tex files. Stdlib-only (urllib + xml.etree). *New.*
|
||||
- `search_openalex.py` — OpenAlex API with citation count and year filtering
|
||||
- Also uses: `deep-research/scripts/search_semantic_scholar.py`, `search_arxiv.py`, `paper_db.py`, `bibtex_manager.py`
|
||||
|
||||
**Usage pattern:** Scripts call search APIs and return structured JSONL. Prompt handles query expansion, result ranking, and relevance filtering.
|
||||
|
||||
---
|
||||
|
||||
### 3. `literature-review` — Multi-Perspective Literature Review
|
||||
|
||||
**Design ref:** S04
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Generates 3-5 expert personas from different research perspectives. Each persona conducts a multi-turn grounded Q&A conversation (3-5 turns). All conversations are synthesized into a unified knowledge base with inline citations.
|
||||
- **Scripts:** Shares search scripts from `literature-search` and `deep-research`.
|
||||
|
||||
**Usage pattern:** Prompt-driven role-play simulation. Scripts provide the search backbone for grounding expert answers in real literature.
|
||||
|
||||
---
|
||||
|
||||
### 4. `idea-generation` — Research Idea Generation
|
||||
|
||||
**Design ref:** S02
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Generates 3-5 diverse research ideas, each with Name/Title/Experiment plan. Iterative reflection (up to 5 rounds) to refine. Scores each idea on Interestingness, Feasibility, Novelty (1-10 scale).
|
||||
- **Scripts (1):**
|
||||
- `novelty_check.py` — Searches Semantic Scholar for similar work, evaluates overlap in multiple rounds
|
||||
|
||||
**Usage pattern:** Prompt generates and refines ideas. Script validates novelty against existing literature.
|
||||
|
||||
---
|
||||
|
||||
### 5. `novelty-assessment` — Research Novelty Evaluation
|
||||
|
||||
**Design ref:** S27
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Adopts a harsh critic persona. Runs up to 10 rounds of search-evaluate loops. Final output is a binary Novel/Not Novel decision with justification. Identifies the most similar existing papers and explains differentiation.
|
||||
- **Scripts:** Shares `idea-generation/scripts/novelty_check.py` and `deep-research/scripts/search_semantic_scholar.py`.
|
||||
|
||||
**Usage pattern:** Prompt drives the adversarial evaluation. Scripts perform iterative literature searches.
|
||||
|
||||
---
|
||||
|
||||
### 6. `research-planning` — Research Plan Design
|
||||
|
||||
**Design ref:** S01
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** 4-stage planning framework: Overall Plan → Architecture Design (UML) → Logic Design (task list) → Configuration. Outputs paper structure, section-by-section plan, dependency-ordered task graph, baselines, datasets, metrics, and risk flags.
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude produces structured research plans as JSON or Markdown.
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Method Design
|
||||
|
||||
### 7. `atomic-decomposition` — Atomic Concept Decomposition
|
||||
|
||||
**Design ref:** S08
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Decomposes a complex research idea into atomic, self-contained concepts. For each concept, performs a Paper Survey (extract math formula from papers) and Code Survey (find implementation in repos). Creates bidirectional mapping: `math_formula ↔ code_implementation`.
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude reads papers and code to build a structured knowledge base of atomic definitions.
|
||||
|
||||
---
|
||||
|
||||
### 8. `algorithm-design` — Algorithm Design & Pseudocode
|
||||
|
||||
**Design ref:** S07
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Formalizes algorithms with clear input/output/parameters. Generates LaTeX pseudocode using `algorithm` + `algpseudocode` environments. Creates Mermaid UML class diagrams and sequence diagrams. Verifies consistency: every pseudocode step maps to a code module.
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Outputs LaTeX algorithm environments and Mermaid diagram code.
|
||||
|
||||
---
|
||||
|
||||
### 9. `math-reasoning` — Mathematical Reasoning
|
||||
|
||||
**Design ref:** S06
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Six task modes:
|
||||
- `derive` — Step-by-step equation derivation with justifications, boxed final result
|
||||
- `prove` — Formal theorem proof using appropriate technique (induction, contradiction, construction)
|
||||
- `formalize` — Problem setting formalization with variable definitions and assumptions
|
||||
- `stats` — Statistical test selection and proper reporting
|
||||
- `notation` — Generate notation table with all symbols used in the paper
|
||||
- `verify` — Check mathematical correctness of existing derivations
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Outputs LaTeX math notation.
|
||||
|
||||
---
|
||||
|
||||
### 10. `symbolic-equation` — Scientific Equation Discovery
|
||||
|
||||
**Design ref:** S28
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Implements the LLM-SR framework: LLM-guided evolutionary search over symbolic expression space. Multi-island algorithm with softmax-based cluster sampling for diversity. Island reset mechanism prevents premature convergence. LLM proposes candidate equations, evaluates fitness against data, iteratively improves.
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude generates and evaluates candidate equations in an evolutionary loop.
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Experiment Pipeline
|
||||
|
||||
### 11. `experiment-design` — Experiment Plan Design
|
||||
|
||||
**Design ref:** S11
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** 4-stage progressive framework (from AI-Scientist-v2):
|
||||
- Stage 1: Initial Implementation — basic working baseline on simple dataset
|
||||
- Stage 2: Baseline Tuning — hyperparameters on 2+ datasets, no architecture changes
|
||||
- Stage 3: Creative Research — novel improvements on 3+ datasets
|
||||
- Stage 4: Ablation Studies — systematic component analysis
|
||||
- **Scripts (1):**
|
||||
- `design_experiments.py` — Generates experiment design JSON/Markdown: baseline list, ablation matrix, hyperparameter grid, metric selection by task type (classification/regression/generation/detection/segmentation/retrieval), total run estimation. Stdlib-only. *New.*
|
||||
|
||||
**Usage pattern:** Script generates the structural experiment plan. Prompt fills in domain-specific details and rationale.
|
||||
|
||||
---
|
||||
|
||||
### 12. `experiment-code` — Experiment Code Writing
|
||||
|
||||
**Design ref:** S09
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Three actions:
|
||||
- `generate` — Create complete training/evaluation pipeline with logging and figure generation
|
||||
- `improve` — Read results, reflect on what worked, apply targeted edits, re-run and compare
|
||||
- `debug` — Identify root cause, apply minimal fix, up to 4 retries
|
||||
- Enforces constraints: no placeholder code, must use real datasets, must generate figures, PyTorch/scikit-learn only.
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude writes, executes, and iteratively improves experiment code.
|
||||
|
||||
---
|
||||
|
||||
### 13. `code-debugging` — Code Debugging
|
||||
|
||||
**Design ref:** S10
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Structured error analysis pipeline:
|
||||
1. Categorize error (SyntaxError, ImportError, RuntimeError, TimeoutError, OutputError, LogicError)
|
||||
2. Analyze root cause from traceback
|
||||
3. Apply targeted fix (up to 4 retries)
|
||||
4. Reflect: explain error, identify lines, describe fix, note patterns to avoid
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude follows the categorize→analyze→fix→reflect workflow.
|
||||
|
||||
---
|
||||
|
||||
### 14. `data-analysis` — Statistical Data Analysis
|
||||
|
||||
**Design ref:** S12
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Generates analysis code in 7 sections (IMPORT → LOAD DATA → DATASET PREPARATIONS → DESCRIPTIVE STATISTICS → PREPROCESSING → ANALYSIS → SAVE). Then runs a 4-round code review: Round 1 (code flaws) → Round 2 (data handling) → Round 3 (per-table) → Round 4 (cross-table completeness). Statistical test selection table guides appropriate test choice.
|
||||
- **Scripts (2):**
|
||||
- `stat_summary.py` — Loads CSV/JSON, detects data types, recommends statistical tests (t-test, Mann-Whitney, Wilcoxon, ANOVA, Kruskal-Wallis), computes effect sizes (Cohen's d), outputs significance stars. Requires numpy + scipy. *New.*
|
||||
- `format_pvalue.py` — Formats p-values as text, significance stars (`*`/`**`/`***`/`ns`), LaTeX notation, or JSON. Supports batch processing from CLI values, CSV, or stdin. Stdlib-only. *New.*
|
||||
|
||||
**Usage pattern:** Scripts handle statistical computation and formatting. Prompt performs the 4-round review and generates the full analysis code.
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Paper Writing
|
||||
|
||||
### 15. `paper-writing-section` — Section-by-Section Writing
|
||||
|
||||
**Design ref:** S15
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Writes each paper section with section-specific guidance:
|
||||
|
||||
| Section | Guidance |
|
||||
|---------|----------|
|
||||
| Abstract | TL;DR → why hard → contribution → how verified. Single paragraph. |
|
||||
| Introduction | Longer abstract; list contributions as bullet points. |
|
||||
| Background | Problem setting with formal notation; define all symbols. |
|
||||
| Related Work | Compare and contrast, not just describe. Organize by theme. |
|
||||
| Methods | Precise equations; what we do AND why. |
|
||||
| Experiments | Datasets, metrics, hyperparameters. No hallucinated numbers. |
|
||||
| Results | Only from actual logs. Include ablations and all figures. |
|
||||
| Discussion | Brief recap → limitations → future work. |
|
||||
|
||||
Two-pass refinement: Pass 1 (fix errors, unenclosed math, broken refs, hallucinated numbers) → Pass 2 (remove redundancies, compress, smooth transitions).
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude reads existing materials, writes the section, then self-refines in two passes.
|
||||
|
||||
---
|
||||
|
||||
### 16. `related-work-writing` — Related Work Section
|
||||
|
||||
**Design ref:** S05
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** 4-step process:
|
||||
1. Analyze paper's key contributions and novelty claims
|
||||
2. Organize literature into thematic clusters
|
||||
3. Write each theme paragraph: topic sentence → describe key works → compare/contrast with this paper
|
||||
4. Refine: verify citation reasons are clear, novelty is explicit, all cite keys resolve
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Focused specifically on the Related Work section with compare-and-contrast emphasis.
|
||||
|
||||
---
|
||||
|
||||
### 17. `survey-generation` — Survey Paper Generation
|
||||
|
||||
**Design ref:** S24
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** AutoSurvey pipeline:
|
||||
1. Collect 50-200 papers via Semantic Scholar/arXiv
|
||||
2. Generate N outlines in parallel, merge best elements
|
||||
3. RAG-based subsection writing: retrieve relevant papers per subsection, generate with inline citations
|
||||
4. Validate citations: check titles match, verify claims are supported
|
||||
5. Enhance local coherence: read adjacent sections, refine transitions
|
||||
6. Convert paper title citations to BibTeX `\cite{key}` format
|
||||
- **Scripts:** Shares `deep-research/scripts/search_semantic_scholar.py`.
|
||||
|
||||
**Usage pattern:** Prompt orchestrates the multi-step RAG pipeline. Script provides the search backend.
|
||||
|
||||
---
|
||||
|
||||
### 18. `paper-to-code` — Paper to Runnable Code
|
||||
|
||||
**Design ref:** S25
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Paper2Code 3-stage pipeline:
|
||||
1. Planning — Overall plan, architecture design (UML), task breakdown, configuration extraction
|
||||
2. Analysis — Per-file detailed logic analysis
|
||||
3. Coding — Dependency-ordered code generation, each file sees all previously generated files
|
||||
4. Debugging — If execution fails, identify root cause and apply fixes
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude reads the paper, designs the architecture, then generates code file by file in dependency order.
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Figures, Tables & Citations
|
||||
|
||||
### 19. `figure-generation` — Scientific Figure Generation
|
||||
|
||||
**Design ref:** S13
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** 3-phase pipeline:
|
||||
1. Query Expansion — expand user description into step-by-step coding specifications
|
||||
2. Code Generation with Execution Loop — generate matplotlib/seaborn script, execute, fix errors (up to 4 retries)
|
||||
3. Visual Refinement — read generated PNG, inspect using VLM feedback, generate corrections
|
||||
- **Scripts (1):**
|
||||
- `figure_template.py` — Generates starter matplotlib code for 10 figure types: `bar`, `line`, `heatmap`, `scatter`, `training_curve`, `ablation`, `radar`, `violin`, `tsne`, `attention`. The last 4 templates were added in the upgrade. *Updated.*
|
||||
|
||||
**Usage pattern:** Script generates code scaffolds for common figure types. Prompt handles the iterative refinement loop with VLM feedback.
|
||||
|
||||
---
|
||||
|
||||
### 20. `table-generation` — LaTeX Table Generation
|
||||
|
||||
**Design ref:** S14
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Converts JSON/CSV experiment results to publication-ready LaTeX tables with `booktabs` styling, bold best results, proper captions and labels. Supports comparison, ablation, descriptive, and multi-dataset table types.
|
||||
- **Scripts (1):**
|
||||
- `results_to_table.py` — Converts JSON/CSV data to LaTeX. 4 table types: `comparison` (methods × metrics), `ablation` (variants × metrics), `descriptive` (dataset stats), `multi-dataset` (methods × datasets × metrics). Flags: `--bold-best`, `--significance` (p-value stars), `--underline-second` (second-best results). *Updated.*
|
||||
|
||||
**Usage pattern:** Script handles the mechanical conversion. Prompt decides table layout and which results to include.
|
||||
|
||||
---
|
||||
|
||||
### 21. `citation-management` — BibTeX Citation Management
|
||||
|
||||
**Design ref:** S17
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Four actions:
|
||||
- `harvest` — Iterative citation harvesting: scan draft for uncited claims, search Semantic Scholar, add candidate BibTeX entries (up to 20 rounds)
|
||||
- `validate` — Pre-compilation check: every `\cite{key}` must exist in `.bib`, every `\includegraphics` must exist
|
||||
- `add` — Add a specific paper by title or DOI
|
||||
- `format` — Standardize and deduplicate `.bib` file
|
||||
- **Scripts (2 own + shared):**
|
||||
- `harvest_citations.py` — Scans .tex for sentences lacking `\cite`, searches Semantic Scholar API, outputs candidate BibTeX. Stdlib-only. *New.*
|
||||
- `validate_citations.py` — Checks cite keys vs .bib, label vs ref consistency, figure file existence. `--fix` mode generates placeholder entries for missing keys. *Updated.*
|
||||
- Also uses: `deep-research/scripts/bibtex_manager.py`, `search_semantic_scholar.py`
|
||||
|
||||
**Usage pattern:** Scripts automate search and validation. Prompt handles citation selection and integration into paper text.
|
||||
|
||||
---
|
||||
|
||||
### 22. `backward-traceability` — Numeric Value Traceability
|
||||
|
||||
**Design ref:** S23
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Ensures every number in the final PDF traces to the exact code line that produced it. Workflow:
|
||||
1. Tag code outputs with `\hypertarget{label}{value}`
|
||||
2. Reference in paper with `\hyperlink{label}{value}`
|
||||
3. Use `\num{formula}` for derived values (compile-time evaluation)
|
||||
4. Generate appendix code listing with hypertarget anchors
|
||||
5. Verify all hyperlinks resolve correctly
|
||||
- **Scripts (1):**
|
||||
- `ref_numeric_values.py` — Two modes: `--scan` (report all hypertarget/hyperlink usage, orphan references, unreferenced numbers) and `--verify` (cross-reference integrity between .tex and code output, value mismatch detection). Stdlib-only. *New.*
|
||||
|
||||
**Usage pattern:** Script scans and verifies traceability. Prompt guides how to add hypertarget/hyperlink tags.
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: LaTeX & Compilation
|
||||
|
||||
### 23. `latex-formatting` — LaTeX Formatting & Templates
|
||||
|
||||
**Design ref:** S16
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Three actions:
|
||||
- `setup` — Create project directory with conference template (ICML, ICLR, NeurIPS, AAAI, ACL)
|
||||
- `fix` — Fix common LaTeX issues: unescaped characters, math mode errors, float placement, cross-references
|
||||
- `check` — Pre-submission validation: word count, section structure, anonymization, citation consistency
|
||||
- **Scripts (2):**
|
||||
- `latex_checker.py` — Checks word count, section completeness (flags missing expected sections), citation/figure/equation counts, venue-specific rules, anonymization. `--fix` mode calls `clean_latex.py` after checking. *Updated.*
|
||||
- `clean_latex.py` — Replaces special characters with LaTeX equivalents (28 special chars + 20 non-UTF8 chars). Skips math environments, comments, command definitions, tabular environments, and LaTeX commands. Stdlib-only. *New.*
|
||||
|
||||
**Usage pattern:** Scripts handle automated checking and cleaning. Prompt does venue-specific template setup and complex formatting fixes.
|
||||
|
||||
---
|
||||
|
||||
### 24. `paper-compilation` — LaTeX Compilation
|
||||
|
||||
**Design ref:** S22
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Full compilation pipeline: pdflatex → bibtex → pdflatex → pdflatex. Pre-compilation validation, up to 5 rounds of error correction, post-compilation report (page count, warnings, style issues).
|
||||
- **Scripts (2):**
|
||||
- `compile_paper.py` — Runs the full pdflatex+bibtex pipeline, optional `chktex` style checking. `--auto-fix` flag runs `fix_latex_errors.py` + recompile up to 3 rounds automatically. *Updated.*
|
||||
- `fix_latex_errors.py` — Parses pdflatex `.log` files, classifies errors (undefined commands, missing math mode, mismatched environments, missing files), applies automated fixes: HTML tag conversion, environment balancing, missing figure commenting. `--dry-run` mode for preview. Stdlib-only. *New.*
|
||||
|
||||
**Usage pattern:** Scripts automate the compile-fix-recompile cycle. Prompt handles complex errors that require understanding paper content.
|
||||
|
||||
---
|
||||
|
||||
### 25. `excalidraw-skill` — Excalidraw Diagramming
|
||||
|
||||
**Design ref:** Not in S01-S28. Uses MCP tools, not traditional scripts.
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Programmatic canvas control via MCP Server tools. Mandatory quality gate after every diagram: check text truncation, element overlap, arrow crossing, spacing, and readability. Workflows include: Draw (plan grid → create elements → bind arrows → verify), Iterative Refinement (screenshot → evaluate → fix), File I/O (export/import .excalidraw), and Sharing (export to excalidraw.com URL).
|
||||
- **Scripts (7 CJS):** MCP server implementation files (not called directly by users).
|
||||
|
||||
**Usage pattern:** Claude calls MCP tools (`create_element`, `batch_create_elements`, `describe_scene`, `get_canvas_screenshot`) to manipulate a live Excalidraw canvas in the browser.
|
||||
|
||||
---
|
||||
|
||||
## Phase 6: Review & Polish
|
||||
|
||||
### 26. `self-review` — Automated Paper Review
|
||||
|
||||
**Design ref:** S18
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Simulates peer review using the NeurIPS review form. Three independent reviewer personas (harsh-fair, harsh-critical, open-minded) each produce a full review. Reflection refinement loop (up to 3 rounds). Reviews are aggregated into a meta-review with averaged scores. Scores: Originality, Quality, Clarity, Significance, Soundness, Presentation, Contribution (1-4), Overall (1-10).
|
||||
- **Scripts (2):**
|
||||
- `extract_pdf_text.py` — Extracts raw text from PDF, outputs as plain text or markdown
|
||||
- `parse_pdf_sections.py` — Parses PDF into structured sections using PyMuPDF font-size analysis. Detects title (largest font), headings (ALL CAPS or larger font), and body text. Outputs `{title, pages, sections: [{name, text, page}]}`. Requires pymupdf. *New.*
|
||||
|
||||
**Usage pattern:** Scripts extract paper content from PDF. Prompt runs 3 independent reviews, refines, and aggregates.
|
||||
|
||||
---
|
||||
|
||||
### 27. `paper-revision` — Paper Revision from Reviews
|
||||
|
||||
**Design ref:** S20
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** 5-step process:
|
||||
1. Parse reviewer concerns — extract, classify (major/minor), map to specific paper sections, prioritize
|
||||
2. Plan revisions — create mapping: Concern → Section → Action → New Content
|
||||
3. Execute revisions — read section, apply edits, run additional experiments if needed, mark changes with `\revised{}`
|
||||
4. Verify improvements — re-run self-review, check all concerns addressed, check page count
|
||||
5. Write revision summary — list all changes with cross-references to reviewer concerns
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Claude systematically addresses each reviewer concern.
|
||||
|
||||
---
|
||||
|
||||
### 28. `rebuttal-writing` — Rebuttal Writing
|
||||
|
||||
**Design ref:** S19
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Extracts reviewer concerns one by one. For each concern, generates a response following the pattern: Acknowledge → Respond with evidence → Describe what was done (not what will be done). Outputs formatted rebuttal with `## Reviewer #N` / `**Concern #N**` / `**Author Response**` structure.
|
||||
- **Scripts:** None.
|
||||
|
||||
**Usage pattern:** Purely prompt-driven. Emphasis on evidence-based responses to specific concerns.
|
||||
|
||||
---
|
||||
|
||||
### 29. `slide-generation` — Presentation Slide Generation
|
||||
|
||||
**Design ref:** S26
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Converts a completed paper into Beamer LaTeX slides or poster. Standard flow: extract key content → design 15-20 slide structure → generate Beamer code → simplify for presentation (max 1 key message per slide, max 6 bullets, large figures). Optional poster layout with 4-column structure.
|
||||
- **Scripts (1):**
|
||||
- `extract_paper_elements.py` — Parses .tex (resolves `\input{}` directives), extracts title, authors, abstract, sections (with hierarchy), figures (path + caption + label), equations, and tables. Generates complete Beamer skeleton or raw JSON. Supports theme selection. Stdlib-only. *New.*
|
||||
|
||||
**Usage pattern:** Script extracts paper structure and generates slide skeleton. Prompt fills in content and optimizes the narrative flow.
|
||||
|
||||
---
|
||||
|
||||
### 30. `paper-assembly` — End-to-End Paper Orchestrator
|
||||
|
||||
**Design ref:** S21
|
||||
|
||||
**How it works:**
|
||||
- **Prompt:** Manages the full 9-phase paper pipeline: literature → planning → code → results → figures → tables → bibliography → sections → compilation. Supports checkpointing after each phase and resumption. Quality gates verify outputs before proceeding. State propagation passes results to downstream phases.
|
||||
- **Scripts (1):**
|
||||
- `assembly_checker.py` — Scans a paper project directory, checks completeness of all 9 pipeline phases, analyzes .tex section coverage (abstract, introduction, method, experiment, conclusion), verifies citation cross-references, reports missing artifacts, suggests next steps with skill recommendations. Stdlib-only. *New.*
|
||||
|
||||
**Usage pattern:** Script assesses current pipeline state. Prompt orchestrates calls to other skills in dependency order.
|
||||
|
||||
---
|
||||
|
||||
## Skill Dependency Graph
|
||||
|
||||
Every SKILL.md includes a `## Related Skills` section linking upstream, downstream, and complementary skills. The full graph:
|
||||
|
||||
```
|
||||
deep-research ──→ literature-search ──→ citation-management ──→ paper-compilation
|
||||
literature-review related-work-writing latex-formatting
|
||||
novelty-assessment survey-generation
|
||||
|
||||
idea-generation ──→ research-planning ──→ experiment-design ──→ experiment-code
|
||||
atomic-decomposition code-debugging
|
||||
|
||||
experiment-code ──→ data-analysis ──→ figure-generation ──→ paper-writing-section
|
||||
table-generation related-work-writing
|
||||
backward-traceability
|
||||
|
||||
paper-writing-section ──→ latex-formatting ──→ paper-compilation ──→ self-review
|
||||
citation-management paper-revision
|
||||
rebuttal-writing
|
||||
|
||||
paper-assembly (orchestrator) ──→ all skills above ──→ slide-generation
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Scripts Inventory
|
||||
|
||||
### New Scripts (12)
|
||||
|
||||
| Script | Skill | Lines | Dependencies | Source |
|
||||
|--------|-------|-------|-------------|--------|
|
||||
| `search_crossref.py` | literature-search | ~260 | stdlib | data-to-paper crossref.py |
|
||||
| `download_arxiv_source.py` | literature-search | ~230 | stdlib | AI-Researcher arxiv.py |
|
||||
| `harvest_citations.py` | citation-management | ~245 | stdlib | AI-Scientist citation loop pattern |
|
||||
| `clean_latex.py` | latex-formatting | ~240 | stdlib | data-to-paper clean_latex.py |
|
||||
| `fix_latex_errors.py` | paper-compilation | ~305 | stdlib | data-to-paper + AI-Scientist patterns |
|
||||
| `parse_pdf_sections.py` | self-review | ~260 | pymupdf | ChatReviewer get_paper_from_pdf.py |
|
||||
| `ref_numeric_values.py` | backward-traceability | ~265 | stdlib | data-to-paper ref_numeric_values.py |
|
||||
| `stat_summary.py` | data-analysis | ~320 | numpy, scipy | data-to-paper 4-round review pattern |
|
||||
| `format_pvalue.py` | data-analysis | ~145 | stdlib | data-to-paper pvalue.py |
|
||||
| `design_experiments.py` | experiment-design | ~275 | stdlib | AI-Scientist-v2 4-stage pattern |
|
||||
| `assembly_checker.py` | paper-assembly | ~290 | stdlib | New |
|
||||
| `extract_paper_elements.py` | slide-generation | ~270 | stdlib | New |
|
||||
|
||||
### Updated Scripts (5)
|
||||
|
||||
| Script | Skill | Change |
|
||||
|--------|-------|--------|
|
||||
| `validate_citations.py` | citation-management | Added `--fix` auto-fix mode |
|
||||
| `compile_paper.py` | paper-compilation | Added `--auto-fix` flag (fix + recompile 3 rounds) |
|
||||
| `latex_checker.py` | latex-formatting | Added `--fix` flag (calls clean_latex.py) |
|
||||
| `figure_template.py` | figure-generation | Added 4 templates: radar, violin, tsne, attention |
|
||||
| `results_to_table.py` | table-generation | Added multi-dataset type, --significance, --underline-second |
|
||||
|
||||
### Pre-existing Scripts (17)
|
||||
|
||||
| Skill | Scripts |
|
||||
|-------|---------|
|
||||
| deep-research | search_semantic_scholar.py, search_arxiv.py, download_papers.py, extract_pdf.py, paper_db.py, bibtex_manager.py, compile_report.py |
|
||||
| literature-search | search_openalex.py |
|
||||
| citation-management | validate_citations.py (pre-upgrade) |
|
||||
| figure-generation | figure_template.py (pre-upgrade) |
|
||||
| idea-generation | novelty_check.py |
|
||||
| latex-formatting | latex_checker.py (pre-upgrade) |
|
||||
| paper-compilation | compile_paper.py (pre-upgrade) |
|
||||
| self-review | extract_pdf_text.py |
|
||||
| table-generation | results_to_table.py (pre-upgrade) |
|
||||
| excalidraw-skill | 7 CJS files (MCP server) |
|
||||
|
||||
---
|
||||
|
||||
## Verification Results
|
||||
|
||||
Tested against real paper: RIGID metamaterials (OpenResearcher/2401.00003, with main.tex + references.bib + 14 figures).
|
||||
|
||||
| Test | Script | Result |
|
||||
|------|--------|--------|
|
||||
| CrossRef API search | `search_crossref.py --query "attention mechanism transformer" --rows 3` | PASS — 3 results |
|
||||
| LaTeX error fixer | `fix_latex_errors.py --tex main.tex --dry-run` | PASS — no fixes needed |
|
||||
| PDF section parser | `parse_pdf_sections.py --pdf report.pdf --format json` | PASS — title + 7 sections |
|
||||
| Citation validator | `validate_citations.py --tex main.tex --bib references.bib` | PASS — 71 citations, 7 unused entries |
|
||||
| Beamer skeleton | `extract_paper_elements.py --tex main.tex --format beamer` | PASS — 19 sections, 12 figures, 3 equations |
|
||||
| Assembly checker | `assembly_checker.py --dir paper/ --verbose` | PASS — 2/9 phases complete |
|
||||
| LaTeX cleaner | `clean_latex.py --input main.tex --dry-run` | PASS — 2 legitimate changes |
|
||||
| LaTeX checker | `latex_checker.py main.tex` | PASS — 7515 words, 12 sections |
|
||||
| Traceability scan | `ref_numeric_values.py --scan main.tex` | PASS — 0 targets (paper has none) |
|
||||
| P-value formatter | `format_pvalue.py --values "0.0001 0.003 0.012 0.048 0.067 0.5" --format latex` | PASS |
|
||||
| Experiment design | `design_experiments.py --method "inverse design" --task classification` | PASS |
|
||||
| Element extraction | `extract_paper_elements.py --tex main.tex --format json` | PASS |
|
||||
|
||||
Bugs found and fixed during verification:
|
||||
1. `parse_pdf_sections.py`: `len(doc)` called after `doc.close()` — fixed by saving page count before closing
|
||||
2. `clean_latex.py`: Escaped `%` in comments and `#` in `\newcommand` — fixed by adding comment/command-definition skip patterns; removed `~` from escape chars (it's a valid LaTeX tie); added `tabular`/`array` to skip environments
|
||||
Reference in New Issue
Block a user