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https://github.com/davila7/claude-code-templates.git
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Daniel Avila
9b2432237b
* improve: enhance ml-engineer based on automated review - Modernize tooling ecosystem with named, current tools (KServe, vLLM/Triton, Feast/Tecton/Hopsworks, Evidently/WhyLabs/Arize) and note Seldon Core v2's BSL license - Ground bare "Data validation" bullets in named tools (Great Expectations, Pandera) - Add scope-clarifying sentence distinguishing this agent from machine-learning-engineer and mlops-engineer - Reframe hardcoded performance targets as illustrative/configurable SLAs - Name fairness/explainability tooling (Fairlearn, Aequitas, SHAP, LIME) and add model card/regulatory awareness note - Add ML-specific security bullets: artifact integrity, training data PII/leakage checks, adversarial robustness testing Automated review cycle | Co-Authored-By: Claude Code <noreply@anthropic.com> * fix: move ml-engineer scope boundaries into frontmatter description Agent auto-selection reads the frontmatter description, not the body prompt text, so the scope-clarifying sentence added in the previous commit had no effect on delegation. Move it into the description field where it can actually reduce ambiguity with machine-learning- engineer, mlops-engineer, and ai-engineer. Addresses review feedback from greptile-apps[bot] on PR #787. * fix: route prompt-text optimization to prompt-engineer, not ai-engineer ai-engineer's own description explicitly hands off prompt-text-only optimization on an already-chosen model to prompt-engineer (ai-specialists category). ml-engineer's scope note was steering all "prompting work" to the generalist ai-engineer instead, which could reintroduce the routing ambiguity this review is meant to reduce. Addresses review feedback from cubic-dev-ai[bot] on PR #787. * fix: resolve contradiction between ml-engineer's example and its own delegation boundary Example 2 in the frontmatter description depicted ml-engineer handling pure inference-serving optimization (quantization, serving-strategy comparison, canary rollout) - exactly the work the new scope note says to delegate to machine-learning-engineer. Rewrote the example so ml-engineer owns the training-pipeline root cause (feature drift, retraining) and explicitly hands off the serving-side optimization, following the same hand-off pattern already used in ai-engineer.md's examples. Addresses review feedback from greptile-apps[bot] on PR #787. * clarify: distinguish model-level lifecycle work from platform infra in Example 3 Sharpens Example 3's commentary to explicitly frame deployment/ monitoring/retraining as model-level lifecycle ownership (a specific model's own rollout and retraining loop), distinct from the underlying platform/infrastructure automation (CI/CD, GPU orchestration, cross-model versioning) that mlops-engineer.md's own description already claims. Full disambiguation between the three sibling agents' descriptions would require editing mlops-engineer.md and machine-learning-engineer.md too, which is out of scope for this single-file review. Addresses review feedback from greptile-apps[bot] on PR #787. * fix: remove ambiguous "performance optimization" from opening invocation trigger The opening sentence of the frontmatter description is the first thing agent selection matches against, and it still claimed raw "performance optimization" as ml-engineer's trigger even though the same description delegates deep inference-serving optimization to machine-learning-engineer later on. Reworded the opening trigger to fold the redirect in directly, so a pure serving-latency-optimization request no longer matches ml-engineer's own invocation criteria. Addresses review feedback from greptile-apps[bot] on PR #787. --------- Co-authored-by: Claude <noreply@anthropic.com>
Sponsored by Bright Data
Bright Data proudly supports this project.
Connect Claude Code to live web data — try their Skills and MCPs for web search, scraping, and structured data feeds.
# Install the Bright Data Skills + MCP for Claude Code
npx claude-code-templates@latest --skill web-data/search,web-data/scrape,web-data/data-feeds,web-data/bright-data-mcp,web-data/bright-data-best-practices,development/brightdata-local-search --mcp web-data/brightdata --yes
Claude Code Templates (aitmpl.com)
Ready-to-use configurations for Anthropic's Claude Code. A comprehensive collection of AI agents, custom commands, settings, hooks, external integrations (MCPs), and project templates to enhance your development workflow.
Browse & Install Components and Templates
Browse All Templates - Interactive web interface to explore and install 100+ agents, commands, settings, hooks, and MCPs.
🚀 Quick Installation
# Install a complete development stack
npx claude-code-templates@latest --agent development-team/frontend-developer --command testing/generate-tests --mcp development/github-integration --yes
# Browse and install interactively
npx claude-code-templates@latest
# Install specific components
npx claude-code-templates@latest --agent development-tools/code-reviewer --yes
npx claude-code-templates@latest --command performance/optimize-bundle --yes
npx claude-code-templates@latest --setting performance/mcp-timeouts --yes
npx claude-code-templates@latest --hook git/pre-commit-validation --yes
npx claude-code-templates@latest --mcp database/postgresql-integration --yes
What You Get
| Component | Description | Examples |
|---|---|---|
| 🤖 Agents | AI specialists for specific domains | Security auditor, React performance optimizer, database architect |
| ⚡ Commands | Custom slash commands | /generate-tests, /optimize-bundle, /check-security |
| 🔌 MCPs | External service integrations | GitHub, PostgreSQL, Stripe, AWS, OpenAI |
| ⚙️ Settings | Claude Code configurations | Timeouts, memory settings, output styles |
| 🪝 Hooks | Automation triggers | Pre-commit validation, post-completion actions |
| 🎨 Skills | Reusable capabilities with progressive disclosure | PDF processing, Excel automation, custom workflows |
🛠️ Additional Tools
Beyond the template catalog, Claude Code Templates includes powerful development tools:
📊 Claude Code Analytics
Monitor your AI-powered development sessions in real-time with live state detection and performance metrics.
npx claude-code-templates@latest --analytics
💬 Conversation Monitor
Mobile-optimized interface to view Claude responses in real-time with secure remote access.
# Local access
npx claude-code-templates@latest --chats
# Secure remote access via Cloudflare Tunnel
npx claude-code-templates@latest --chats --tunnel
🔍 Health Check
Comprehensive diagnostics to ensure your Claude Code installation is optimized.
npx claude-code-templates@latest --health-check
🔌 Plugin Dashboard
View marketplaces, installed plugins, and manage permissions from a unified interface.
npx claude-code-templates@latest --plugins
📖 Documentation
📚 docs.aitmpl.com - Complete guides, examples, and API reference for all components and tools.
Contributing
We welcome contributions! Browse existing templates to see what's available, then check our contributing guidelines to add your own agents, commands, MCPs, settings, or hooks.
Please read our Code of Conduct before contributing.
Attribution
This collection includes components from multiple sources:
Scientific Skills:
- K-Dense-AI/claude-scientific-skills by K-Dense Inc. - MIT License (139 scientific skills for biology, chemistry, medicine, and computational research)
Official Anthropic:
- anthropics/skills - Official Anthropic skills (21 skills)
- anthropics/claude-code - Development guides and examples (10 skills)
Community Skills & Agents:
- obra/superpowers by Jesse Obra - MIT License (14 workflow skills)
- alirezarezvani/claude-skills by Alireza Rezvani - MIT License (36 professional role skills)
- wshobson/agents by wshobson - MIT License (48 agents)
- NerdyChefsAI Skills - Community contribution - MIT License (specialized enterprise skills)
Commands & Tools:
- awesome-claude-code by hesreallyhim - CC0 1.0 Universal (21 commands)
- awesome-claude-skills - Apache 2.0 (community skills)
- move-code-quality-skill - MIT License
- cocoindex-claude - Apache 2.0
Each of these resources retains its original license and attribution, as defined by their respective authors. We respect and credit all original creators for their work and contributions to the Claude ecosystem.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🔗 Links
- 🌐 Browse Templates: aitmpl.com
- 📚 Documentation: docs.aitmpl.com
- 💬 Community: GitHub Discussions
- 🐛 Issues: GitHub Issues
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