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Contributing to Claude AI Research Skills

AI community intermediate

Description

Thank you for your interest in contributing! This guide will help you add new skills to the library. ---

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

Repository README

This is the README for Orchestra-Research/AI-Research-SKILLs, shared by 7 entries in this directory. It describes the repository, not this entry specifically.

Contributing to Claude AI Research Skills

Thank you for your interest in contributing! This guide will help you add new skills to the library.


🎯 What We're Building

**Vision**: The most comprehensive open-source library of AI research skills for Claude Code.

**Target**: 86 comprehensive skills covering the entire AI research lifecycle — from ideation to paper writing. ✅ Achieved.

**Current Progress**: 86/86 skills across 22 categories (100%)

**Philosophy**: Quality > Quantity. We deleted 9 low-quality skills to maintain high standards.


🤝 How to Contribute

Ways to Contribute

  1. Add a new skill - Most valuable contribution
  2. Improve existing skills - Update docs, add examples, fix errors
  3. Report issues - Outdated information, broken links, missing content
  4. Share feedback - What skills do you need? What's missing?

📝 Adding a New Skill

Step 1: Choose a Skill

Step 2: Fork and Clone

# Fork the repository on GitHub first
git clone https://github.com/YOUR_USERNAME/AI-research-SKILLs.git
cd claude-ai-research-skills

# Create a feature branch
git checkout -b add-vllm-skill

Step 3: Use Skill Seeker MCP

**Option A: Documentation Scraping**

# Create config file
python3 cli/doc_scraper.py --interactive
# Or copy and modify an existing config
cp configs/react.json configs/vllm.json

# Scrape and build
python3 cli/doc_scraper.py --config configs/vllm.json

**Option B: GitHub Scraping**

# Scrape from GitHub repository
export GITHUB_TOKEN=$(gh auth token)
python3 cli/github_scraper.py --repo vllm-project/vllm --name vllm --description "High-performance LLM inference with PagedAttention"

**Option C: Unified Scraping** (recommended for comprehensive skills)

# Combine documentation + GitHub + PDF
python3 cli/unified_scraper.py --config configs/vllm_unified.json

Step 4: Move to Correct Directory

# Determine the category (see directory structure below)
mv output/vllm/ 12-inference-serving/vllm/

# Move metadata
mv output/vllm_data/ .metadata/vllm_data/

Step 5: Validate Quality

**Based on [Anthropic Official Best Practices](anthropic_official_docs/best_practices.md)**

**Core Requirements** (or skill will be rejected):

  • ✅ YAML frontmatter with name (gerund form, e.g., "serving-llms") and description (third person, includes what AND when)
  • ✅ SKILL.md body: 200-300 lines (under 500 lines maximum)
  • ✅ Progressive disclosure: SKILL.md as overview, details in separate reference files
  • ✅ Workflows with copy-paste checklists for complex tasks
  • ✅ When to use vs alternatives guidance
  • ✅ Common issues section with solutions
  • ✅ Concise content: assume Claude is smart, no over-explaining basics
  • ✅ Code examples with language detection (python, bash, etc.)

**Gold Standard** (aim for this):

  • ✅ SKILL.md: 200-300 lines of focused, actionable guidance
  • ✅ 2-3 complete workflows with step-by-step checklists
  • ✅ R