# Project Overview

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

- Skill: `tools-only/project-overview-30` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/project-overview-30`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/project-overview-30/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/project-overview-30

---

# CLAUDE.md

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

## 🎯 Project Overview

**Skill Seekers** is the **universal documentation preprocessor** for AI systems. It transforms documentation websites, GitHub repositories, and PDFs into production-ready formats for **16+ platforms**: RAG pipelines (LangChain, LlamaIndex, Haystack), vector databases (Pinecone, Chroma, Weaviate, FAISS, Qdrant), AI coding assistants (Cursor, Windsurf, Cline, Continue.dev), and LLM platforms (Claude, Gemini, OpenAI).

**Current Version:** v3.1.0-dev
**Python Version:** 3.10+ required
**Status:** Production-ready, published on PyPI
**Website:** https://skillseekersweb.com/ - Browse configs, share, and access documentation

## 📚 Table of Contents

- [First Time Here?](#-first-time-here) - Start here!
- [Quick Commands](#-quick-command-reference-most-used) - Common workflows
- [Architecture](#️-architecture) - How it works
- [Development](#️-development-commands) - Building & testing
- [Testing](#-testing-guidelines) - Test strategy
- [Debugging](#-debugging-tips) - Troubleshooting
- [Contributing](#-where-to-make-changes) - How to add features

## 👋 First Time Here?

**Complete this 3-minute setup to start contributing:**

```bash
# 1. Install package in editable mode (REQUIRED for development)
pip install -e .

# 2. Verify installation
python -c "import skill_seekers; print(skill_seekers.__version__)"  # Should print: 3.1.0-dev

# 3. Run a quick test
pytest tests/test_scraper_features.py::test_detect_language -v

# 4. You're ready! Pick a task from the roadmap:
# https://github.com/users/yusufkaraaslan/projects/2
```

**Quick Navigation:**
- Building/Testing → [Development Commands](#️-development-commands)
- Architecture → [Core Design Pattern](#️-architecture)
- Common Issues → [Common Pitfalls](#-common-pitfalls--solutions)
- Contributing → See `CONTRIBUTING.md`

## ⚡ Quick Command Reference (Most Used)

**First time setup:**
```bash
pip install -e .  # REQUIRED before running tests or CLI
```

**Running tests (NEVER skip - user requirement):**
```bash
pytest tests/ -v  # All tests
pytest tests/test_scraper_features.py -v  # Single file
pytest tests/ --cov=src/skill_seekers --cov-report=html  # With coverage
```

**Code quality checks (matches CI):**
```bash
ruff check src/ tests/  # Lint
ruff format src/ tests/  # Format
mypy src/skill_seekers  # Type check
```

**Common workflows:**
```bash
# NEW unified create command (auto-detects source type)
skill-seekers create https://docs.react.dev/ -p quick
skill-seekers create facebook/react -p standard
skill-seekers create ./my-project -p comprehensive
skill-seekers create tutorial.pdf

# Legacy commands (still supported)
skill-seekers scrape --config configs/react.json
skill-seekers github --repo facebook/react
skill-seekers analyze --directory . --comprehensive

# Package for LLM platforms
skill-seekers package output/react/ --target claude
skill-seekers package output/react/ --target gemini
```

**RAG Pipeline workflows:**
```bash
# LangChain Documents
skill-seekers package output/react/ --format langchain

# LlamaIndex TextNodes
skill-seekers package output/react/ --format llama-index

# Haystack Documents
skill-seekers package output/react/ --format haystack

# ChromaDB direct upload
skill-seekers package output/react/ --format chroma --upload

# FAISS export
skill-seekers package output/react/ --format faiss

# Weaviate/Qdrant upload (requires API keys)
skill-seekers package output/react/ --format weaviate --upload
skill-seekers package output/react/ --format qdrant --upload
```

**AI Coding Assistant workflows:**
```bash
# Cursor IDE
skill-seekers package output/react/ --target claude
cp output/react-claude/SKILL.md .cursorrules

# Windsurf
cp output/react-claude/SKILL.md .windsurf/rules/react.md

# Cline (VS Code)
cp output/react-claude/SKILL.md .clinerules

# Continue.dev (universal IDE)
python examples/continue-dev-universal/context_server.py
# Configure in ~/.continue/config.json
```

**Cloud Storage:**
```bash
# Upload to S3
skill-seekers cloud upload --provider s3 --bucket my-skills output/react.zip

# Upload to GCS
skill-seekers cloud upload --provider gcs --bucket my-skills output/react.zip

# Upload to Azure
skill-seekers cloud upload --provider azure --container my-skills output/react.zip
```

## 🏗️ Architecture

### Core Design Pattern: Platform Adaptors

The codebase uses the **Strategy Pattern** with a factory method to support **16 platforms** across 4 categories:

```
src/skill_seekers/cli/adaptors/
├── __init__.py          # Factory: get_adaptor(target/format)
├── base.py              # Abstract base class
# LLM Platforms (3)
├── claude.py            # Claude AI (ZIP + YAML)
├── gemini.py            # Google Gemini (tar.gz)
├── openai.py            # OpenAI ChatGPT (ZIP + Vector Store)
# RAG Frameworks (3)
├── langchain.py         # LangChain Documents
├── llama_index.py       # LlamaIndex TextNodes
├── haystack.py          # Haystack Documents
# Vector Databases (5)
├── chroma.py            # ChromaDB
├── faiss_helpers.py     # FAISS
├── qdrant.py            # Qdrant
├── weaviate.py          # Weaviate
# AI Coding Assistants (4 - via Claude format + config files)
# - Cursor, Windsurf, Cline, Continue.dev
# Generic (1)
├── markdown.py          # Generic Markdown (ZIP)
└── streaming_adaptor.py # Streaming data ingest
```

**Key Methods:**
- `package(skill_dir, output_path)` - Platform-specific packaging
- `upload(package_path, api_key)` - Platform-specific upload (where applicable)
- `enhance(skill_dir, mode)` - AI enhancement with platform-specific models
- `export(skill_dir, format)` - Export to RAG/vector DB formats

### 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) - Key Files Only

```
src/skill_seekers/
├── cli/                              # All CLI commands
│   ├── main.py                       # ⭐ Git-style CLI dispatcher
│   ├── doc_scraper.py                # ⭐ Main scraper (~790 lines)
│   │   ├── scrape_all()              # BFS traversal engine
│   │   ├── smart_categorize()        # Category detection
│   │   └── build_skill()             # SKILL.md generation
│   ├── github_scraper.py             # GitHub repo analysis
│   ├── codebase_scraper.py           # ⭐ Local analysis (C2.x+C3.x)
│   ├── package_skill.py              # Platform packaging
│   ├── unified_scraper.py            # Multi-source scraping
│   ├── unified_codebase_analyzer.py  # Three-stream GitHub+local analyzer
│   ├── enhance_skill_local.py        # AI enhancement (LOCAL mode)
│   ├── enhance_status.py             # Enhancement status monitoring
│   ├── 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
│   ├── signal_flow_analyzer.py       # C3.10 Signal flow analysis (Godot)
│   ├── pdf_scraper.py                # PDF extraction
│   └── adaptors/                     # ⭐ Platform adaptor pattern
│       ├── __init__.py               # Factory: get_adaptor()
│       ├── base_adaptor.py           # Abstract base
│       ├── claude_adaptor.py         # Claude AI
│       ├── gemini_adaptor.py         # Google Gemini
│       ├── openai_adaptor.py         # OpenAI ChatGPT
│       ├── markdown_adaptor.py       # Generic Markdown
│       ├── langchain.py              # LangChain RAG
│       ├── llama_index.py            # LlamaIndex RAG
│       ├── haystack.py               # Haystack RAG
│       ├── chroma.py                 # ChromaDB
│       ├── faiss_helpers.py          # FAISS
│       ├── qdrant.py                 # Qdrant
│       ├── weaviate.py               # Weaviate
│       └── streaming_adaptor.py      # Streaming data ingest
└── mcp/                              # MCP server (26 tools)
    ├── server_fastmcp.py             # FastMCP server
    └── tools/                        # Tool implementations
```

**Most Modified Files (when contributing):**
- Platform adaptors: `src/skill_seekers/cli/adaptors/{platform}.py`
- Tests: `tests/test_{feature}.py`
- Configs: `configs/{framework}.json`

## 🛠️ Development Commands

### Setup

```bash
# 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.

```bash
# 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
- **2,121 tests passing** (current v3.1.0), up from 700+ in v2.x
- Must run `pip install -e .` before tests (src/ layout requirement)
- Tests include create command integration tests, CLI refactor E2E tests

### Building & Publishing

```bash
# 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

```bash
# Test configuration wizard (NEW: v2.7.0)
skill-seekers config --show                          # Show current configuration
skill-seekers config --github                        # GitHub token setup
skill-seekers config --test                          # Test connections

# Test resume functionality (NEW: v2.7.0)
skill-seekers resume --list                          # List resumable jobs
skill-seekers resume --clean                         # Clean up old jobs

# Test GitHub scraping with profiles (NEW: v2.7.0)
skill-seekers github --repo facebook/react --profile personal    # Use specific profile
skill-seekers github --repo owner/repo --non-interactive         # CI/CD mode

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

# Test codebase analysis (C2.x features)
skill-seekers analyze --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_fastmcp

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

### New v3.0.0 CLI Commands

```bash
# Setup wizard (interactive configuration)
skill-seekers-setup

# Cloud storage operations
skill-seekers cloud upload --provider s3 --bucket my-bucket output/react.zip
skill-seekers cloud download --provider gcs --bucket my-bucket react.zip
skill-seekers cloud list --provider azure --container my-container

# Embedding server (for RAG pipelines)
skill-seekers embed --port 8080 --model sentence-transformers

# Sync & incremental updates
skill-seekers sync --source https://docs.react.dev/ --target output/react/
skill-seekers update --skill output/react/ --check-changes

# Quality metrics & benchmarking
skill-seekers quality --skill output/react/ --report
skill-seekers benchmark --config configs/react.json --compare-versions

# Multilingual support
skill-seekers multilang --detect output/react/
skill-seekers multilang --translate output/react/ --target zh-CN

# Streaming data ingest
skill-seekers stream --source docs/ --target output/streaming/
```

## 🔧 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:

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

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

### NEW: Unified `create` Command

**The recommended way to create skills** - Auto-detects source type and provides progressive help disclosure:

```bash
# Auto-detection examples
skill-seekers create https://docs.react.dev/         # → Web scraping
skill-seekers create facebook/react                  # → GitHub analysis
skill-seekers create ./my-project                    # → Local codebase
skill-seekers create tutorial.pdf                    # → PDF extraction
skill-seekers create configs/react.json              # → Multi-source

# Progressive help system
skill-seekers create --help           # Shows universal args only (13 flags)
skill-seekers create --help-web       # Shows web-specific options
skill-seekers create --help-github    # Shows GitHub-specific options
skill-seekers create --help-local     # Shows local analysis options
skill-seekers create --help-pdf       # Shows PDF extraction options
skill-seekers create --help-advanced  # Shows advanced/rare options
skill-seekers create --help-all       # Shows all 120+ flags

# Universal flags work for ALL sources
skill-seekers create <source> -p quick                    # Preset (-p shortcut)
skill-seekers create <source> --enhance-level 2           # AI enhancement (0-3)
skill-seekers create <source> --chunk-for-rag             # RAG chunking
skill-seekers create <source> --dry-run                   # Preview
```

**Key improvements:**
- **Single command** replaces scrape/github/analyze for most use cases
- **Smart detection** - No need to specify source type
- **Progressive disclosure** - Default help shows 13 flags, detailed help available
- **-p shortcut** - Quick preset selection (`-p quick|standard|comprehensive`)
- **Universal features** - RAG chunking, dry-run, presets work everywhere

**Recent Additions:**
- `create` - **NEW:** Unified command with auto-detection and progressive help
- `codebase` - Local codebase analysis without GitHub API (C2.x + C3.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

```python
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-C3.8):

**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 Architectural Overview** (`generate_router.py`):
- Generates comprehensive ARCHITECTURE.md files
- Router skill generation for large documentation
- Quality improvements: 6.5/10 → 8.5/10 (+31%)
- Integrates GitHub metadata, issues, labels

**C3.6 AI Enhancement** (Claude API integration):
- Enhances C3.1-C3.5 with AI-powered insights
- Pattern explanations and improvement suggestions
- Test example context and best practices
- Guide enhancement with troubleshooting and prerequisites

**C3.7 Architectural Pattern Detection** (`architectural_pattern_detector.py`):
- Detects 8 architectural patterns (MVC, MVVM, MVP, Repository, etc.)
- Framework detection (Django, Flask, Spring, React, Angular, etc.)
- Multi-file analysis with directory structure patterns
- Evidence-based detection with confidence scoring

**C3.8 Standalone Codebase Scraper** (`codebase_scraper.py`):
```bash
# Quick analysis (1-2 min, basic features only)
skill-seekers analyze --directory /path/to/repo --quick

# Comprehensive analysis (20-60 min, all features + AI)
skill-seekers analyze --directory . --comprehensive

# With AI enhancement (auto-detects API or LOCAL)
skill-seekers analyze --directory . --enhance

# Granular AI enhancement control (NEW)
skill-seekers analyze --directory . --enhance-level 1  # SKILL.md only
skill-seekers analyze --directory . --enhance-level 2  # + Architecture + Config + Docs
skill-seekers analyze --directory . --enhance-level 3  # Full enhancement (all features)

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

- Generates 300+ line standalone SKILL.md files from codebases
- All C3.x features integrated (patterns, tests, guides, config, architecture, docs)
- Complete codebase analysis without documentation scraping
- **NEW**: Granular AI enhancement control with `--enhance-level` (0-3)

**C3.9 Project Documentation Extraction** (`codebase_scraper.py`):
- Extracts and categorizes all markdown files from the project
- Auto-detects categories: overview, architecture, guides, workflows, features, etc.
- Integrates documentation into SKILL.md with summaries
- AI enhancement (level 2+) adds topic extraction and cross-references
- Controlled by depth: surface=raw copy, deep=parse+summarize, full=AI-enhanced
- Default ON, use `--skip-docs` to disable

**C3.10 Signal Flow Analysis for Godot Projects** (`signal_flow_analyzer.py`):
- Complete signal flow analysis system for event-driven Godot architectures
- Signal declaration extraction (detects `signal` keyword declarations)
- Connection mapping (tracks `.connect()` calls with targets and methods)
- Emission tracking (finds `.emit()` and `emit_signal()` calls)
- Real-world metrics: 208 signals, 634 connections, 298 emissions in test project
- Signal density metrics (signals per file)
- Event chain detection (signals triggering other signals)
- Signal pattern detection:
  - **EventBus Pattern** (0.90 confidence): Centralized signal hub in autoload
  - **Observer Pattern** (0.85 confidence): Multi-observer signals (3+ listeners)
  - **Event Chains** (0.80 confidence): Cascading signal propagation
- Signal-based how-to guides (C3.10.1):
  - AI-generated step-by-step usage guides (Connect → Emit → Handle)
  - Real code examples from project
  - Common usage locations
  - Parameter documentation
- Outputs: `signal_flow.json`, `signal_flow.mmd` (Mermaid diagram), `signal_reference.md`, `signal_how_to_guides.md`
- Comprehensive Godot 4.x support:
  - GDScript (.gd), Scene files (.tscn), Resources (.tres), Shaders (.gdshader)
  - GDScript test extraction (GUT, gdUnit4, WAT frameworks)
  - 396 test cases extracted in test project
  - Framework detection (Unity, Unreal, Godot)

**Key Architecture Decision (BREAKING in 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:

```json
{
  "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

### Test Markers (from pytest.ini_options)

The project uses pytest markers to categorize tests:

```bash
# Run only fast unit tests (default)
pytest tests/ -v

# Include slow tests (>5 seconds)
pytest tests/ -v -m slow

# Run integration tests (requires external services)
pytest tests/ -v -m integration

# Run end-to-end tests (resource-intensive, creates files)
pytest tests/ -v -m e2e

# Run tests requiring virtual environment setup
pytest tests/ -v -m venv

# Run bootstrap feature tests
pytest tests/ -v -m bootstrap

# Skip slow and integration tests (fastest)
pytest tests/ -v -m "not slow and not integration"
```

### Test Execution Strategy

**By default, only fast tests run**. Use markers to control test execution:

```bash
# Default: Only fast tests (skip slow/integration/e2e)
pytest tests/ -v

# Include slow tests (>5 seconds)
pytest tests/ -v -m slow

# Include integration tests (requires external services)
pytest tests/ -v -m integration

# Include resource-intensive e2e tests (creates files)
pytest tests/ -v -m e2e

# Run ONLY fast tests (explicit)
pytest tests/ -v -m "not slow and not integration and not e2e"

# Run everything (CI does this)
pytest tests/ -v -m ""
```

**When to use which:**
- **Local development:** Default (fast tests only) - `pytest tests/ -v`
- **Pre-commit:** Fast tests - `pytest tests/ -v`
- **Before PR:** Include slow + integration - `pytest tests/ -v -m "not e2e"`
- **CI validation:** All tests run automatically

### 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
- `conftest.py` - Test configuration (checks package installation)

## 🌐 Environment Variables

```bash
# Claude AI / Compatible APIs
# Option 1: Official Anthropic API (default)
export ANTHROPIC_API_KEY=sk-ant-...

# Option 2: GLM-4.7 Claude-compatible API (or any compatible endpoint)
export ANTHROPIC_API_KEY=your-api-key
export ANTHROPIC_BASE_URL=https://glm-4-7-endpoint.com/v1

# 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=...
```

**All AI enhancement features respect these settings**:
- `enhance_skill.py` - API mode SKILL.md enhancement
- `ai_enhancer.py` - C3.1/C3.2 pattern and test example enhancement
- `guide_enhancer.py` - C3.3 guide enhancement
- `config_enhancer.py` - C3.4 configuration enhancement
- `adaptors/claude.py` - Claude platform adaptor enhancement

**Note**: Setting `ANTHROPIC_BASE_URL` allows you to use any Claude-compatible API endpoint, such as GLM-4.7 (智谱 AI).

## 📦 Package Structure (pyproject.toml)

### Entry Points

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

# Individual tool entry points (Core)
skill-seekers-config = "skill_seekers.cli.config_command:main"                # v2.7.0 Configuration wizard
skill-seekers-resume = "skill_seekers.cli.resume_command:main"                # v2.7.0 Resume interrupted jobs
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"           # C2.x Local codebase analysis
skill-seekers-enhance = "skill_seekers.cli.enhance_skill_local:main"
skill-seekers-enhance-status = "skill_seekers.cli.enhance_status:main"       # 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"         # C3.1 Pattern detection
skill-seekers-how-to-guides = "skill_seekers.cli.how_to_guide_builder:main" # C3.3 Guide generation
skill-seekers-workflows = "skill_seekers.cli.workflows_command:main"         # NEW: Workflow preset management

# New v3.0.0 Entry Points
skill-seekers-setup = "skill_seekers.cli.setup_wizard:main"                  # NEW: v3.0.0 Setup wizard
skill-seekers-cloud = "skill_seekers.cli.cloud_storage_cli:main"             # NEW: v3.0.0 Cloud storage
skill-seekers-embed = "skill_seekers.embedding.server:main"                  # NEW: v3.0.0 Embedding server
skill-seekers-sync = "skill_seekers.cli.sync_cli:main"                       # NEW: v3.0.0 Sync & monitoring
skill-seekers-benchmark = "skill_seekers.cli.benchmark_cli:main"             # NEW: v3.0.0 Benchmarking
skill-seekers-stream = "skill_seekers.cli.streaming_ingest:main"             # NEW: v3.0.0 Streaming ingest
skill-seekers-update = "skill_seekers.cli.incremental_updater:main"          # NEW: v3.0.0 Incremental updates
skill-seekers-multilang = "skill_seekers.cli.multilang_support:main"         # NEW: v3.0.0 Multilingual
skill-seekers-quality = "skill_seekers.cli.quality_metrics:main"             # NEW: v3.0.0 Quality metrics
```

### Optional Dependencies

**Project uses PEP 735 `[dependency-groups]` (Python 3.13+)**:
- Replaces deprecated `tool.uv.dev-dependencies`
- Dev dependencies: `[dependency-groups] dev = [...]` in pyproject.toml
- Install with: `pip install -e .` (installs only core deps)
- Install dev deps: See CI workflow or manually install pytest, ruff, mypy

```toml
[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",
]
```

## 🚨 Critical Development Notes

### Must Run Before Tests

```bash
# 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 skip any test. always make sure all test pass"
- All 2,121 tests must pass before commits (v3.1.0)
- Run full test suite: `pytest tests/ -v`
- New tests added for create command and CLI refactor work

### Platform-Specific Dependencies

Platform dependencies are optional (install only what you need):

```bash
# Install specific platform support
pip install -e ".[gemini]"         # Google Gemini
pip install -e ".[openai]"         # OpenAI ChatGPT
pip install -e ".[chroma]"         # ChromaDB
pip install -e ".[weaviate]"       # Weaviate
pip install -e ".[s3]"             # AWS S3
pip install -e ".[gcs]"            # Google Cloud Storage
pip install -e ".[azure]"          # Azure Blob Storage
pip install -e ".[mcp]"            # MCP integration
pip install -e ".[all]"            # Everything (16 platforms + cloud + embedding)

# Or install from PyPI:
pip install skill-seekers[gemini]    # Google Gemini support
pip install skill-seekers[openai]    # OpenAI ChatGPT support
pip install skill-seekers[all-llms]  # All LLM platforms
pip install skill-seekers[chroma]    # ChromaDB support
pip install skill-seekers[weaviate]  # Weaviate support
pip install skill-seekers[s3]        # AWS S3 support
pip install skill-seekers[all]       # All optional dependencies
```

### 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)

### Enhancement Flag Consolidation (Phase 1)

**IMPORTANT CHANGE:** Three enhancement flags have been unified into a single granular control:

**Old flags (deprecated):**
- `--enhance` - Enable AI enhancement
- `--enhance-local` - Use LOCAL mode (Claude Code)
- `--api-key KEY` - Anthropic API key

**New unified flag:**
- `--enhance-level LEVEL` - Granular AI enhancement control (0-3, default: 2)
  - `0` - Disabled, no AI enhancement
  - `1` - SKILL.md only (core documentation)
  - `2` - + Architecture + Config + Docs (default, balanced)
  - `3` - Full enhancement (all features, comprehensive)

**Auto-detection:** Mode (API vs LOCAL) is auto-detected:
- If `ANTHROPIC_API_KEY` is set → API mode
- Otherwise → LOCAL mode (Claude Code Max)

**Examples:**
```bash
# Auto-detect mode, default enhancement level (2)
skill-seekers create https://docs.react.dev/

# Disable enhancement
skill-seekers create facebook/react --enhance-level 0

# SKILL.md only (fast)
skill-seekers create ./my-project --enhance-level 1

# Full enhancement (comprehensive)
skill-seekers create tutorial.pdf --enhance-level 3

# Force LOCAL mode with specific level
skill-seekers enhance output/react/ --mode LOCAL --enhance-level 2

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

**Migration:** Old flags still work with deprecation warnings, will be removed in v4.0.0.

See `docs/ENHANCEMENT_MODES.md` for detailed documentation.

### Git Workflow

**Git Workflow Notes:**
- Main branch: `main`
- Development branch: `development`
- Always create feature branches from `development`
- Branch naming: `feature/{task-id}-{description}` or `feature/{category}`

**To see current status:** `git status`

### CI/CD Pipeline

The project has GitHub Actions workflows in `.github/workflows/`:

**tests.yml** - Runs on every push and PR to `main` or `development`:

1. **Lint Job** (Python 3.12, Ubuntu):
   - `ruff check src/ tests/` - Code linting with GitHub annotations
   - `ruff format --check src/ tests/` - Format validation
   - `mypy src/skill_seekers` - Type checking (continue-on-error)

2. **Test Job** (Matrix):
   - **OS:** Ubuntu + macOS
   - **Python:** 3.10, 3.11, 3.12
   - **Exclusions:** macOS + Python 3.10 (speed optimization)
   - **Steps:**
     - Install dependencies + `pip install -e .`
     - Run CLI tests (scraper, config, integration)
     - Run MCP server tests
     - Generate coverage report → Upload to Codecov

3. **Summary Job** - Single status check for branch protection
   - Ensures both lint and test jobs succeed
   - Provides single "All Checks Complete" status

**release.yml** - Triggers on version tags (e.g., `v2.9.0`):
- Builds package with `uv build`
- Publishes to PyPI with `uv publish`
- Creates GitHub release

**Local Pre-Commit Validation**

Run the same checks as CI before pushing:

```bash
# 1. Code quality (matches lint job) - WITH AUTO-FIX
uvx ruff check --fix --unsafe-fixes src/ tests/  # Auto-fix issues
uvx ruff format src/ tests/                      # Auto-format
uvx ruff check src/ tests/                       # Verify clean
uvx ruff format --check src/ tests/              # Verify formatted
mypy src/skill_seekers

# 2. Tests (matches test job)
pip install -e .
pytest tests/ -v --cov=src/skill_seekers --cov-report=term

# 3. If all pass, you're good to push!
git add -A  # Stage any auto-fixes
git commit --amend --no-edit  # Add fixes to commit (or new commit)
git push origin feature/my-feature
```

**Branch Protection Rules:**
- **main:** Requires tests + 1 review, only maintainers merge
- **development:** Requires tests to pass, default target for PRs

**Common CI Failure Patterns and Fixes**

If CI fails after your changes, follow this debugging checklist:

```bash
# 1. Fix linting errors automatically
uvx ruff check --fix --unsafe-fixes src/ tests/

# 2. Fix formatting issues
uvx ruff format src/ tests/

# 3. Check for remaining issues
uvx ruff check src/ tests/
uvx ruff format --check src/ tests/

# 4. Verify tests pass locally
pip install -e .
pytest tests/ -v

# 5. Push fixes
git add -A
git commit -m "fix: resolve CI linting/formatting issues"
git push
```

**Critical dependency patterns to check:**
- **MCP version mismatch**: Ensure `requirements.txt` and `pyproject.toml` have matching MCP versions
- **Missing module-level imports**: If a tool file imports a module at top level (e.g., `import yaml`), that module MUST be in core dependencies
- **Try/except ImportError**: Silent failures in try/except blocks can hide missing dependencies

**Timing-sensitive tests:**
- Benchmark tests may fail on slower CI runners (macOS)
- If a test times out or exceeds threshold only in CI, consider relaxing the threshold
- Local passing doesn't guarantee CI passing for performance tests

## 🚨 Common Pitfalls & Solutions

### 1. Import Errors
**Problem:** `ModuleNotFoundError: No module named 'skill_seekers'`

**Solution:** Must install package first due to src/ layout
```bash
pip install -e .
```

**Why:** The src/ layout prevents imports from repo root. Package must be installed.

### 2. Tests Fail with "No module named..."
**Problem:** Package not installed in test environment

**Solution:** CI runs `pip install -e .` before tests - do the same locally
```bash
pip install -e .
pytest tests/ -v
```

### 3. Platform-Specific Dependencies Not Found
**Problem:** `ModuleNotFoundError: No module named 'google.generativeai'`

**Solution:** Install platform-specific dependencies
```bash
pip install -e ".[gemini]"   # For Gemini
pip install -e ".[openai]"   # For OpenAI
pip install -e ".[all-llms]" # For all platforms
```

### 4. Git Branch Confusion
**Problem:** PR targets `main` instead of `development`

**Solution:** Always create PRs targeting `development` branch
```bash
git checkout development
git pull upstream development
git checkout -b feature/my-feature
# ... make changes ...
git push origin feature/my-feature
# Create PR: feature/my-feature → development
```

**Important:** See `CONTRIBUTING.md` for complete branch workflow.

### 5. Tests Pass Locally But Fail in CI
**Problem:** Different Python version or missing dependency

**Solution:** Test with multiple Python versions locally
```bash
# CI tests: Python 3.10, 3.11, 3.12 on Ubuntu + macOS
# Use pyenv or docker to test locally:
pyenv install 3.10.13 3.11.7 3.12.1

pyenv local 3.10.13
pip install -e . && pytest tests/ -v

pyenv local 3.11.7
pip install -e . && pytest tests/ -v

pyenv local 3.12.1
pip install -e . && pytest tests/ -v
```

### 6. Enhancement Not Working
**Problem:** AI enhancement fails or hangs

**Solutions:**
```bash
# Check if API key is set
echo $ANTHROPIC_API_KEY

# Try LOCAL mode instead (uses Claude Code Max, no API key needed)
skill-seekers enhance output/react/ --mode LOCAL

# Monitor enhancement status for background jobs
skill-seekers enhance-status output/react/ --watch
```

### 7. Rate Limit Errors from GitHub
**Problem:** `403 Forbidden` from GitHub API

**Solutions:**
```bash
# Check current rate limit
curl -H "Authorization: token $GITHUB_TOKEN" https://api.github.com/rate_limit

# Configure multiple GitHub profiles (recommended)
skill-seekers config --github

# Use specific profile
skill-seekers github --repo owner/repo --profile work

# Test all configured tokens
skill-seekers config --test
```

### 8. Confused About Command Options
**Problem:** "Too many flags!" or "Which flags work with which sources?"

**Solution:** Use the progressive disclosure help system in the `create` command:
```bash
# Start with universal options (13 flags)
skill-seekers create --help

# Need web scraping options?
skill-seekers create --help-web

# GitHub-specific flags?
skill-seekers create --help-github

# See ALL options (120+ flags)?
skill-seekers create --help-all

# Quick preset shortcut
skill-seekers create <source> -p quick
skill-seekers create <source> -p standard
skill-seekers create <source> -p comprehensive
```

**Why:** The create command shows only relevant flags by default to reduce cognitive load.

**Legacy commands** 

…(truncated)