skill-seekers-reference/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

🎯 Project Overview

Skill Seekers is a Python tool that converts documentation websites, GitHub repositories, and PDFs into LLM skills. It supports 4 platforms: Claude AI, Google Gemini, OpenAI ChatGPT, and Generic Markdown.

Current Version: v2.5.2 Python Version: 3.10+ required Status: Production-ready, published on PyPI

🏗️ Architecture

Core Design Pattern: Platform Adaptors

The codebase uses the Strategy Pattern with a factory method to support multiple LLM platforms:

src/skill_seekers/cli/adaptors/
├── __init__.py          # Factory: get_adaptor(target)
├── base_adaptor.py      # Abstract base class
├── claude_adaptor.py    # Claude AI (ZIP + YAML)
├── gemini_adaptor.py    # Google Gemini (tar.gz)
├── openai_adaptor.py    # OpenAI ChatGPT (ZIP + Vector Store)
└── markdown_adaptor.py  # Generic Markdown (ZIP)

Key Methods:

• package(skill_dir, output_path) - Platform-specific packaging
• upload(package_path, api_key) - Platform-specific upload
• enhance(skill_dir, mode) - AI enhancement with platform-specific models

Data Flow (5 Phases)

1. Scrape Phase (doc_scraper.py:scrape_all())

   • BFS traversal from base_url
   • Output: output/{name}_data/pages/*.json

2. Build Phase (doc_scraper.py:build_skill())

   • Load pages → Categorize → Extract patterns
   • Output: output/{name}/SKILL.md + references/*.md

3. Enhancement Phase (optional, enhance_skill_local.py)

   • LLM analyzes references → Rewrites SKILL.md
   • Platform-specific models (Sonnet 4, Gemini 2.0, GPT-4o)

4. Package Phase (package_skill.py → adaptor)

   • Platform adaptor packages in appropriate format
   • Output: .zip or .tar.gz

5. Upload Phase (optional, upload_skill.py → adaptor)

   • Upload via platform API

File Structure (src/ layout)

src/skill_seekers/
├── cli/                              # CLI tools
│   ├── main.py                       # Git-style CLI dispatcher
│   ├── doc_scraper.py                # Main scraper (~790 lines)
│   ├── github_scraper.py             # GitHub repo analysis
│   ├── pdf_scraper.py                # PDF extraction
│   ├── unified_scraper.py            # Multi-source scraping
│   ├── codebase_scraper.py           # Local codebase analysis (C2.x)
│   ├── unified_codebase_analyzer.py  # Three-stream GitHub+local analyzer
│   ├── enhance_skill_local.py        # AI enhancement (LOCAL mode)
│   ├── enhance_status.py             # Enhancement status monitoring
│   ├── package_skill.py              # Skill packager
│   ├── upload_skill.py               # Upload to platforms
│   ├── install_skill.py              # Complete workflow automation
│   ├── install_agent.py              # Install to AI agent directories
│   ├── pattern_recognizer.py         # C3.1 Design pattern detection
│   ├── test_example_extractor.py     # C3.2 Test example extraction
│   ├── how_to_guide_builder.py       # C3.3 How-to guide generation
│   ├── config_extractor.py           # C3.4 Configuration extraction
│   ├── generate_router.py            # C3.5 Router skill generation
│   ├── code_analyzer.py              # Multi-language code analysis
│   ├── api_reference_builder.py      # API documentation builder
│   ├── dependency_analyzer.py        # Dependency graph analysis
│   └── adaptors/                     # Platform adaptor architecture
│       ├── __init__.py
│       ├── base_adaptor.py
│       ├── claude_adaptor.py
│       ├── gemini_adaptor.py
│       ├── openai_adaptor.py
│       └── markdown_adaptor.py
└── mcp/                              # MCP server integration
    ├── server.py                     # FastMCP server (stdio + HTTP)
    └── tools/                        # 18 MCP tool implementations

🛠️ Development Commands

Setup

# Install in editable mode (required before tests due to src/ layout)
pip install -e .

# Install with all platform dependencies
pip install -e ".[all-llms]"

# Install specific platforms
pip install -e ".[gemini]"   # Google Gemini
pip install -e ".[openai]"   # OpenAI ChatGPT

Running Tests

CRITICAL: Never skip tests - User requires all tests to pass before commits.

# All tests (must run pip install -e . first!)
pytest tests/ -v

# Specific test file
pytest tests/test_scraper_features.py -v

# Multi-platform tests
pytest tests/test_install_multiplatform.py -v

# With coverage
pytest tests/ --cov=src/skill_seekers --cov-report=term --cov-report=html

# Single test
pytest tests/test_scraper_features.py::test_detect_language -v

# MCP server tests
pytest tests/test_mcp_fastmcp.py -v

Test Architecture:

• 46 test files covering all features
• CI Matrix: Ubuntu + macOS, Python 3.10-3.13
• 700+ tests passing
• Must run pip install -e . before tests (src/ layout requirement)

Building & Publishing

# Build package (using uv - recommended)
uv build

# Or using build
python -m build

# Publish to PyPI
uv publish

# Or using twine
python -m twine upload dist/*

Testing CLI Commands

# Test scraping (dry run)
skill-seekers scrape --config configs/react.json --dry-run

# Test codebase analysis (C2.x features)
skill-seekers codebase --directory . --output output/codebase/

# Test pattern detection (C3.1)
skill-seekers patterns --file src/skill_seekers/cli/code_analyzer.py

# Test how-to guide generation (C3.3)
skill-seekers how-to-guides output/test_examples.json --output output/guides/

# Test enhancement status monitoring
skill-seekers enhance-status output/react/ --watch

# Test multi-platform packaging
skill-seekers package output/react/ --target gemini --dry-run

# Test MCP server (stdio mode)
python -m skill_seekers.mcp.server

# Test MCP server (HTTP mode)
python -m skill_seekers.mcp.server --transport http --port 8765

🔧 Key Implementation Details

CLI Architecture (Git-style)

Entry point: src/skill_seekers/cli/main.py

The unified CLI modifies sys.argv and calls existing main() functions to maintain backward compatibility:

# Example: skill-seekers scrape --config react.json
# Transforms to: doc_scraper.main() with modified sys.argv

Subcommands: scrape, github, pdf, unified, codebase, enhance, enhance-status, package, upload, estimate, install, install-agent, patterns, how-to-guides

New in v2.5.2:

• codebase - Local codebase analysis without GitHub API (C2.x features)
• enhance-status - Monitor background/daemon enhancement processes
• patterns - Detect design patterns in code (C3.1)
• how-to-guides - Generate educational guides from tests (C3.3)

Platform Adaptor Usage

from skill_seekers.cli.adaptors import get_adaptor

# Get platform-specific adaptor
adaptor = get_adaptor('gemini')  # or 'claude', 'openai', 'markdown'

# Package skill
adaptor.package(skill_dir='output/react/', output_path='output/')

# Upload to platform
adaptor.upload(
    package_path='output/react-gemini.tar.gz',
    api_key=os.getenv('GOOGLE_API_KEY')
)

# AI enhancement
adaptor.enhance(skill_dir='output/react/', mode='api')

C3.x Codebase Analysis Features

The project has comprehensive codebase analysis capabilities (C3.1-C3.7):

C3.1 Design Pattern Detection (pattern_recognizer.py):

• Detects 10 common patterns: Singleton, Factory, Observer, Strategy, Decorator, Builder, Adapter, Command, Template Method, Chain of Responsibility
• Supports 9 languages: Python, JavaScript, TypeScript, C++, C, C#, Go, Rust, Java
• Three detection levels: surface (fast), deep (balanced), full (thorough)
• 87% precision, 80% recall on real-world projects

C3.2 Test Example Extraction (test_example_extractor.py):

• Extracts real usage examples from test files
• Categories: instantiation, method_call, config, setup, workflow
• AST-based for Python, regex-based for 8 other languages
• Quality filtering with confidence scoring

C3.3 How-To Guide Generation (how_to_guide_builder.py):

• Transforms test workflows into educational guides
• 5 AI enhancements: step descriptions, troubleshooting, prerequisites, next steps, use cases
• Dual-mode AI: API (fast) or LOCAL (free with Claude Code Max)
• 4 grouping strategies: AI tutorial group, file path, test name, complexity

C3.4 Configuration Pattern Extraction (config_extractor.py):

• Extracts configuration patterns from codebases
• Identifies config files, env vars, CLI arguments
• AI enhancement for better organization

C3.5 Router Skill Generation (generate_router.py):

• Creates meta-skills that route to specialized skills
• Quality improvements: 6.5/10 → 8.5/10 (+31%)
• Integrates GitHub metadata, issues, labels

Codebase Scraper Integration (codebase_scraper.py):

# All C3.x features enabled by default, use --skip-* to disable
skill-seekers codebase --directory /path/to/repo

# Disable specific features
skill-seekers codebase --directory . --skip-patterns --skip-how-to-guides

# Legacy flags (deprecated but still work)
skill-seekers codebase --directory . --build-api-reference --build-dependency-graph

Key Architecture Decision (v2.5.2):

• Changed from opt-in (--build-*) to opt-out (--skip-*) flags
• All analysis features now ON by default for maximum value
• Backward compatibility warnings for deprecated flags

Smart Categorization Algorithm

Located in doc_scraper.py:smart_categorize():

• Scores pages against category keywords
• 3 points for URL match, 2 for title, 1 for content
• Threshold of 2+ for categorization
• Auto-infers categories from URL segments if none provided
• Falls back to "other" category

Language Detection

Located in doc_scraper.py:detect_language():

1. CSS class attributes (language-*, lang-*)
2. Heuristics (keywords like def, const, func)

Configuration File Structure

Configs (configs/*.json) define scraping behavior:

{
  "name": "framework-name",
  "description": "When to use this skill",
  "base_url": "https://docs.example.com/",
  "selectors": {
    "main_content": "article",  // CSS selector
    "title": "h1",
    "code_blocks": "pre code"
  },
  "url_patterns": {
    "include": ["/docs"],
    "exclude": ["/blog"]
  },
  "categories": {
    "getting_started": ["intro", "quickstart"],
    "api": ["api", "reference"]
  },
  "rate_limit": 0.5,
  "max_pages": 500
}

🧪 Testing Guidelines

Test Coverage Requirements

• Core features: 100% coverage required
• Platform adaptors: Each platform has dedicated tests
• MCP tools: All 18 tools must be tested
• Integration tests: End-to-end workflows

Key Test Files

• test_scraper_features.py - Core scraping functionality
• test_mcp_server.py - MCP integration (18 tools)
• test_mcp_fastmcp.py - FastMCP framework
• test_unified.py - Multi-source scraping
• test_github_scraper.py - GitHub analysis
• test_pdf_scraper.py - PDF extraction
• test_install_multiplatform.py - Multi-platform packaging
• test_integration.py - End-to-end workflows
• test_install_skill.py - One-command install
• test_install_agent.py - AI agent installation

🌐 Environment Variables

# Claude AI (default platform)
export ANTHROPIC_API_KEY=sk-ant-...

# Google Gemini (optional)
export GOOGLE_API_KEY=AIza...

# OpenAI ChatGPT (optional)
export OPENAI_API_KEY=sk-...

# GitHub (for higher rate limits)
export GITHUB_TOKEN=ghp_...

# Private config repositories (optional)
export GITLAB_TOKEN=glpat-...
export GITEA_TOKEN=...
export BITBUCKET_TOKEN=...

📦 Package Structure (pyproject.toml)

Entry Points

[project.scripts]
# Main unified CLI
skill-seekers = "skill_seekers.cli.main:main"

# Individual tool entry points
skill-seekers-scrape = "skill_seekers.cli.doc_scraper:main"
skill-seekers-github = "skill_seekers.cli.github_scraper:main"
skill-seekers-pdf = "skill_seekers.cli.pdf_scraper:main"
skill-seekers-unified = "skill_seekers.cli.unified_scraper:main"
skill-seekers-codebase = "skill_seekers.cli.codebase_scraper:main"           # NEW: C2.x
skill-seekers-enhance = "skill_seekers.cli.enhance_skill_local:main"
skill-seekers-enhance-status = "skill_seekers.cli.enhance_status:main"       # NEW: Status monitoring
skill-seekers-package = "skill_seekers.cli.package_skill:main"
skill-seekers-upload = "skill_seekers.cli.upload_skill:main"
skill-seekers-estimate = "skill_seekers.cli.estimate_pages:main"
skill-seekers-install = "skill_seekers.cli.install_skill:main"
skill-seekers-install-agent = "skill_seekers.cli.install_agent:main"
skill-seekers-patterns = "skill_seekers.cli.pattern_recognizer:main"         # NEW: C3.1
skill-seekers-how-to-guides = "skill_seekers.cli.how_to_guide_builder:main" # NEW: C3.3

Optional Dependencies

[project.optional-dependencies]
gemini = ["google-generativeai>=0.8.0"]
openai = ["openai>=1.0.0"]
all-llms = ["google-generativeai>=0.8.0", "openai>=1.0.0"]

[dependency-groups]  # PEP 735 (replaces tool.uv.dev-dependencies)
dev = [
    "pytest>=8.4.2",
    "pytest-asyncio>=0.24.0",
    "pytest-cov>=7.0.0",
    "coverage>=7.11.0",
]

Note: Project uses PEP 735 dependency-groups instead of deprecated tool.uv.dev-dependencies.

🚨 Critical Development Notes

Must Run Before Tests

# REQUIRED: Install package before running tests
pip install -e .

# Why: src/ layout requires package installation
# Without this, imports will fail

Never Skip Tests

Per user instructions in ~/.claude/CLAUDE.md:

• "never skipp any test. always make sure all test pass"
• All 700+ tests must pass before commits
• Run full test suite: pytest tests/ -v

Platform-Specific Dependencies

Platform dependencies are optional:

# Install only what you need
pip install skill-seekers[gemini]  # Gemini support
pip install skill-seekers[openai]  # OpenAI support
pip install skill-seekers[all-llms]  # All platforms

AI Enhancement Modes

AI enhancement transforms basic skills (2-3/10) into production-ready skills (8-9/10). Two modes available:

API Mode (default if ANTHROPIC_API_KEY is set):

• Direct Claude API calls (fast, efficient)
• Cost: ~$0.15-$0.30 per skill
• Perfect for CI/CD automation
• Requires: export ANTHROPIC_API_KEY=sk-ant-...

LOCAL Mode (fallback if no API key):

• Uses Claude Code CLI (your existing Max plan)
• Free! No API charges
• 4 execution modes:
  • Headless (default): Foreground, waits for completion
  • Background (--background): Returns immediately
  • Daemon (--daemon): Fully detached with nohup
  • Terminal (--interactive-enhancement): Opens new terminal (macOS)
• Status monitoring: skill-seekers enhance-status output/react/ --watch
• Timeout configuration: --timeout 300 (seconds)

Force Mode (default ON since v2.5.2):

• Skip all confirmations automatically
• Perfect for CI/CD, batch processing
• Use --no-force to enable prompts if needed

# API mode (if ANTHROPIC_API_KEY is set)
skill-seekers enhance output/react/

# LOCAL mode (no API key needed)
skill-seekers enhance output/react/ --mode LOCAL

# Background with status monitoring
skill-seekers enhance output/react/ --background
skill-seekers enhance-status output/react/ --watch

# Force mode OFF (enable prompts)
skill-seekers enhance output/react/ --no-force

See docs/ENHANCEMENT_MODES.md for detailed documentation.

Git Workflow

• Main branch: main
• Current branch: development
• Always create feature branches from development
• Feature branch naming: feature/{task-id}-{description} or feature/{category}

🔌 MCP Integration

MCP Server (18 Tools)

Transport modes:

• stdio: Claude Code, VS Code + Cline
• HTTP: Cursor, Windsurf, IntelliJ IDEA

Core Tools (9):

1. list_configs - List preset configurations
2. generate_config - Generate config from docs URL
3. validate_config - Validate config structure
4. estimate_pages - Estimate page count
5. scrape_docs - Scrape documentation
6. package_skill - Package to .zip (supports --target)
7. upload_skill - Upload to platform (supports --target)
8. enhance_skill - AI enhancement with platform support
9. install_skill - Complete workflow automation

Extended Tools (9): 10. scrape_github - GitHub repository analysis 11. scrape_pdf - PDF extraction 12. unified_scrape - Multi-source scraping 13. merge_sources - Merge docs + code 14. detect_conflicts - Find discrepancies 15. split_config - Split large configs 16. generate_router - Generate router skills 17. add_config_source - Register git repos 18. fetch_config - Fetch configs from git

Starting MCP Server

# stdio mode (Claude Code, VS Code + Cline)
python -m skill_seekers.mcp.server

# HTTP mode (Cursor, Windsurf, IntelliJ)
python -m skill_seekers.mcp.server --transport http --port 8765

📋 Common Workflows

Adding a New Platform

1. Create adaptor in src/skill_seekers/cli/adaptors/{platform}_adaptor.py
2. Inherit from BaseAdaptor
3. Implement package(), upload(), enhance() methods
4. Add to factory in adaptors/__init__.py
5. Add optional dependency to pyproject.toml
6. Add tests in tests/test_install_multiplatform.py

Adding a New Feature

1. Implement in appropriate CLI module
2. Add entry point to pyproject.toml if needed
3. Add tests in tests/test_{feature}.py
4. Run full test suite: pytest tests/ -v
5. Update CHANGELOG.md
6. Commit only when all tests pass

Debugging Test Failures

# Run specific failing test with verbose output
pytest tests/test_file.py::test_name -vv

# Run with print statements visible
pytest tests/test_file.py -s

# Run with coverage to see what's not tested
pytest tests/test_file.py --cov=src/skill_seekers --cov-report=term-missing

📚 Key Code Locations

Documentation Scraper (src/skill_seekers/cli/doc_scraper.py):

• is_valid_url() - URL validation
• extract_content() - Content extraction
• detect_language() - Code language detection
• extract_patterns() - Pattern extraction
• smart_categorize() - Smart categorization
• infer_categories() - Category inference
• generate_quick_reference() - Quick reference generation
• create_enhanced_skill_md() - SKILL.md generation
• scrape_all() - Main scraping loop
• main() - Entry point

Codebase Analysis (src/skill_seekers/cli/):

• codebase_scraper.py - Main CLI for local codebase analysis
• code_analyzer.py - Multi-language AST parsing (9 languages)
• api_reference_builder.py - API documentation generation
• dependency_analyzer.py - NetworkX-based dependency graphs
• pattern_recognizer.py - C3.1 design pattern detection
• test_example_extractor.py - C3.2 test example extraction
• how_to_guide_builder.py - C3.3 guide generation
• config_extractor.py - C3.4 configuration extraction
• generate_router.py - C3.5 router skill generation
• unified_codebase_analyzer.py - Three-stream GitHub+local analyzer

AI Enhancement (src/skill_seekers/cli/):

• enhance_skill_local.py - LOCAL mode enhancement (4 execution modes)
• enhance_skill.py - API mode enhancement
• enhance_status.py - Status monitoring for background processes
• ai_enhancer.py - Shared AI enhancement logic
• guide_enhancer.py - C3.3 guide AI enhancement
• config_enhancer.py - C3.4 config AI enhancement

Platform Adaptors (src/skill_seekers/cli/adaptors/):

• __init__.py - Factory function
• base_adaptor.py - Abstract base class
• claude_adaptor.py - Claude AI implementation
• gemini_adaptor.py - Google Gemini implementation
• openai_adaptor.py - OpenAI ChatGPT implementation
• markdown_adaptor.py - Generic Markdown implementation

MCP Server (src/skill_seekers/mcp/):

• server.py - FastMCP-based server
• tools/ - 18 MCP tool implementations

🎯 Project-Specific Best Practices

1. Always use platform adaptors - Never hardcode platform-specific logic
2. Test all platforms - Changes must work for all 4 platforms
3. Maintain backward compatibility - Legacy configs must still work
4. Document API changes - Update CHANGELOG.md for every release
5. Keep dependencies optional - Platform-specific deps are optional
6. Use src/ layout - Proper package structure with pip install -e .
7. Run tests before commits - Per user instructions, never skip tests

📖 Additional Documentation

For Users:

• README.md - Complete user documentation
• BULLETPROOF_QUICKSTART.md - Beginner guide
• TROUBLESHOOTING.md - Common issues

For Developers:

• CHANGELOG.md - Release history
• FLEXIBLE_ROADMAP.md - 134 tasks across 22 feature groups
• docs/UNIFIED_SCRAPING.md - Multi-source scraping
• docs/MCP_SETUP.md - MCP server setup
• docs/ENHANCEMENT_MODES.md - AI enhancement modes
• docs/PATTERN_DETECTION.md - C3.1 pattern detection
• docs/THREE_STREAM_STATUS_REPORT.md - Three-stream architecture
• docs/MULTI_LLM_SUPPORT.md - Multi-platform support

🎓 Understanding the Codebase

Why src/ Layout?

Modern Python best practice (PEP 517/518):

• Prevents accidental imports from repo root
• Forces proper package installation
• Better isolation between package and tests
• Required: pip install -e . before running tests

Why Platform Adaptors?

Strategy pattern benefits:

• Single codebase supports 4 platforms
• Platform-specific optimizations (format, APIs, models)
• Easy to add new platforms (implement BaseAdaptor)
• Clean separation of concerns
• Testable in isolation

Why Git-style CLI?

User experience benefits:

• Familiar to developers (like git)
• Single entry point: skill-seekers
• Backward compatible: individual tools still work
• Cleaner than multiple separate commands
• Easier to document and teach

Three-Stream GitHub Architecture

The unified_codebase_analyzer.py splits GitHub repositories into three independent streams:

Stream 1: Code Analysis (C3.x features)

• Deep AST parsing (9 languages)
• Design pattern detection (C3.1)
• Test example extraction (C3.2)
• How-to guide generation (C3.3)
• Configuration extraction (C3.4)
• Architectural overview (C3.5)
• API reference + dependency graphs

Stream 2: Documentation

• README, CONTRIBUTING, LICENSE
• docs/ directory markdown files
• Wiki pages (if available)
• CHANGELOG and version history

Stream 3: Community Insights

• GitHub metadata (stars, forks, watchers)
• Issue analysis (top problems and solutions)
• PR trends and contributor stats
• Release history
• Label-based topic detection

Key Benefits:

• Unified interface for GitHub URLs and local paths
• Analysis depth control: 'basic' (1-2 min) or 'c3x' (20-60 min)
• Enhanced router generation with GitHub context
• Smart keyword extraction weighted by GitHub labels (2x weight)
• 81 E2E tests passing (0.44 seconds)

🔍 Performance Characteristics

Operation	Time	Notes
Scraping (sync)	15-45 min	First time, thread-based
Scraping (async)	5-15 min	2-3x faster with --async
Building	1-3 min	Fast rebuild from cache
Re-building	<1 min	With --skip-scrape
Enhancement (LOCAL)	30-60 sec	Uses Claude Code Max
Enhancement (API)	20-40 sec	Requires API key
Packaging	5-10 sec	Final .zip creation

🎉 Recent Achievements

v2.5.2 (Latest):

• UX Improvement: Analysis features now default ON with --skip-* flags (BREAKING)
• Changed from opt-in (--build-) to opt-out (--skip-) for better discoverability
• Router quality improvements: 6.5/10 → 8.5/10 (+31%)
• C3.5 Architectural Overview & Skill Integrator
• All 107 codebase analysis tests passing

v2.5.1:

• Fixed critical PyPI packaging bug (missing adaptors module)
• 100% of multi-platform features working

v2.5.0:

• Multi-platform support (4 LLM platforms)
• Platform adaptor architecture
• 18 MCP tools (up from 9)
• Complete feature parity across platforms
• 700+ tests passing

C3.x Series (Code Analysis Features):

• C3.1: Design pattern detection (10 patterns, 9 languages, 87% precision)
• C3.2: Test example extraction (AST-based, 19 tests)
• C3.3: How-to guide generation with AI enhancement (5 improvements)
• C3.4: Configuration pattern extraction
• C3.5: Router skill generation
• C3.6: AI enhancement (dual-mode: API + LOCAL)
• C3.7: Architectural pattern detection

v2.0.0:

• Unified multi-source scraping
• Conflict detection between docs and code
• 5 unified configs (React, Django, FastAPI, Godot)
• 22 unified tests passing