AGENTS.md - Skill Seekers

AGENTS.md - Skill Seekers

This file provides essential guidance for AI coding agents working with the Skill Seekers codebase.

Project Overview

Skill Seekers is a Python CLI tool that converts documentation websites, GitHub repositories, and PDF files into AI-ready skills for LLM platforms and RAG (Retrieval-Augmented Generation) pipelines. It serves as the universal preprocessing layer for AI systems.

Key Facts

Supported Target Platforms

Core Workflow

1. Scrape Phase - Crawl documentation/GitHub/PDF sources
2. Build Phase - Organize content into categorized references
3. Enhancement Phase - AI-powered quality improvements (optional)
4. Package Phase - Create platform-specific packages
5. Upload Phase - Auto-upload to target platform (optional)

Project Structure

/mnt/1ece809a-2821-4f10-aecb-fcdf34760c0b/Git/Skill_Seekers/
├── src/skill_seekers/              # Main source code (src/ layout)
│   ├── cli/                        # CLI tools and commands (70+ modules, ~40k lines)
│   │   ├── adaptors/               # Platform adaptors (Strategy pattern)
│   │   │   ├── base.py             # Abstract base class
│   │   │   ├── claude.py           # Claude AI adaptor
│   │   │   ├── gemini.py           # Google Gemini adaptor
│   │   │   ├── openai.py           # OpenAI ChatGPT adaptor
│   │   │   ├── markdown.py         # Generic Markdown adaptor
│   │   │   ├── chroma.py           # Chroma vector DB adaptor
│   │   │   ├── faiss_helpers.py    # FAISS index adaptor
│   │   │   ├── haystack.py         # Haystack RAG adaptor
│   │   │   ├── langchain.py        # LangChain adaptor
│   │   │   ├── llama_index.py      # LlamaIndex adaptor
│   │   │   ├── qdrant.py           # Qdrant vector DB adaptor
│   │   │   ├── weaviate.py         # Weaviate vector DB adaptor
│   │   │   └── streaming_adaptor.py # Streaming output adaptor
│   │   ├── storage/                # Cloud storage backends
│   │   │   ├── base_storage.py     # Storage interface
│   │   │   ├── s3_storage.py       # AWS S3 support
│   │   │   ├── gcs_storage.py      # Google Cloud Storage
│   │   │   └── azure_storage.py    # Azure Blob Storage
│   │   ├── parsers/                # CLI argument parsers
│   │   ├── main.py                 # Unified CLI entry point
│   │   ├── doc_scraper.py          # Documentation scraper
│   │   ├── github_scraper.py       # GitHub repository scraper
│   │   ├── pdf_scraper.py          # PDF extraction
│   │   ├── unified_scraper.py      # Multi-source scraping
│   │   ├── codebase_scraper.py     # Local codebase analysis
│   │   ├── enhance_skill_local.py  # AI enhancement (local mode)
│   │   ├── package_skill.py        # Skill packager
│   │   ├── upload_skill.py         # Upload to platforms
│   │   ├── cloud_storage_cli.py    # Cloud storage CLI
│   │   ├── benchmark_cli.py        # Benchmarking CLI
│   │   ├── sync_cli.py             # Sync monitoring CLI
│   │   └── ...                     # Additional CLI modules
│   ├── mcp/                        # MCP server integration
│   │   ├── server_fastmcp.py       # FastMCP server (main, ~708 lines)
│   │   ├── server_legacy.py        # Legacy server implementation
│   │   ├── server.py               # Server entry point
│   │   ├── agent_detector.py       # AI agent detection
│   │   ├── git_repo.py             # Git repository operations
│   │   ├── source_manager.py       # Config source management
│   │   └── tools/                  # MCP tool implementations
│   │       ├── config_tools.py     # Configuration tools
│   │       ├── scraping_tools.py   # Scraping tools
│   │       ├── packaging_tools.py  # Packaging tools
│   │       ├── source_tools.py     # Source management tools
│   │       ├── splitting_tools.py  # Config splitting tools
│   │       └── vector_db_tools.py  # Vector database tools
│   ├── sync/                       # Sync monitoring module
│   │   ├── detector.py             # Change detection
│   │   ├── models.py               # Data models
│   │   ├── monitor.py              # Monitoring logic
│   │   └── notifier.py             # Notification system
│   ├── benchmark/                  # Benchmarking framework
│   │   ├── framework.py            # Benchmark framework
│   │   ├── models.py               # Benchmark models
│   │   └── runner.py               # Benchmark runner
│   ├── embedding/                  # Embedding server
│   │   ├── server.py               # FastAPI embedding server
│   │   ├── generator.py            # Embedding generation
│   │   ├── cache.py                # Embedding cache
│   │   └── models.py               # Embedding models
│   ├── _version.py                 # Version information
│   └── __init__.py                 # Package init
├── tests/                          # Test suite (89 test files)
├── configs/                        # Preset configuration files
├── docs/                           # Documentation (80+ markdown files)
│   ├── integrations/               # Platform integration guides
│   ├── guides/                     # User guides
│   ├── reference/                  # API reference
│   ├── features/                   # Feature documentation
│   ├── blog/                       # Blog posts
│   └── roadmap/                    # Roadmap documents
├── examples/                       # Usage examples
│   ├── langchain-rag-pipeline/     # LangChain example
│   ├── llama-index-query-engine/   # LlamaIndex example
│   ├── pinecone-upsert/            # Pinecone example
│   ├── chroma-example/             # Chroma example
│   ├── weaviate-example/           # Weaviate example
│   ├── qdrant-example/             # Qdrant example
│   ├── faiss-example/              # FAISS example
│   ├── haystack-pipeline/          # Haystack example
│   ├── cursor-react-skill/         # Cursor IDE example
│   ├── windsurf-fastapi-context/   # Windsurf example
│   └── continue-dev-universal/     # Continue.dev example
├── .github/workflows/              # CI/CD workflows
├── pyproject.toml                  # Main project configuration
├── requirements.txt                # Pinned dependencies
├── mypy.ini                        # MyPy type checker configuration
├── Dockerfile                      # Main Docker image (multi-stage)
├── Dockerfile.mcp                  # MCP server Docker image
└── docker-compose.yml              # Full stack deployment

Build and Development Commands

Prerequisites

• Python 3.10 or higher
• pip or uv package manager
• Git (for GitHub scraping features)

Setup (REQUIRED before any development)

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

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

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

# Install specific platforms only
pip install -e ".[gemini]"    # Google Gemini support
pip install -e ".[openai]"    # OpenAI ChatGPT support
pip install -e ".[mcp]"       # MCP server dependencies
pip install -e ".[s3]"        # AWS S3 support
pip install -e ".[gcs]"       # Google Cloud Storage
pip install -e ".[azure]"     # Azure Blob Storage
pip install -e ".[embedding]" # Embedding server support
pip install -e ".[rag-upload]" # Vector DB upload support

# Install dev dependencies (using dependency-groups)
pip install -e ".[dev]"

CRITICAL: The project uses a src/ layout. Tests WILL FAIL unless you install with pip install -e . first.

Building

# Build package using uv (recommended)
uv build

# Or using standard build
python -m build

# Publish to PyPI
uv publish

Docker

# Build Docker image
docker build -t skill-seekers .

# Run with docker-compose (includes vector databases)
docker-compose up -d

# Run MCP server only
docker-compose up -d mcp-server

# View logs
docker-compose logs -f mcp-server

Testing Instructions

Running Tests

CRITICAL: Never skip tests - all tests must pass before commits.

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

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

# E2E tests
pytest tests/test_e2e_three_stream_pipeline.py -v

# Skip slow tests
pytest tests/ -v -m "not slow"

# Run only integration tests
pytest tests/ -v -m integration

# Run only specific marker
pytest tests/ -v -m "not slow and not integration"

Test Architecture

• 89 test files covering all features
• 1200+ tests passing
• CI Matrix: Ubuntu + macOS, Python 3.10-3.12
• Test markers defined in pyproject.toml:

Test Configuration

From pyproject.toml:

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
addopts = "-v --tb=short --strict-markers"
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"

The conftest.py file checks that the package is installed before running tests.

Code Style Guidelines

Linting and Formatting

# Run ruff linter
ruff check src/ tests/

# Run ruff formatter check
ruff format --check src/ tests/

# Auto-fix issues
ruff check src/ tests/ --fix
ruff format src/ tests/

# Run mypy type checker
mypy src/skill_seekers --show-error-codes --pretty

Style Rules (from pyproject.toml)

• Line length: 100 characters
• Target Python: 3.10+
• Enabled rules: E, W, F, I, B, C4, UP, ARG, SIM
• Ignored rules: E501, F541, ARG002, B007, I001, SIM114
• Import sorting: isort style with skill_seekers as first-party

MyPy Configuration (from mypy.ini)

[mypy]
python_version = 3.10
warn_return_any = False
warn_unused_configs = True
disallow_untyped_defs = False
check_untyped_defs = True
ignore_missing_imports = True
no_implicit_optional = True
show_error_codes = True

# Gradual typing - be lenient for now
disallow_incomplete_defs = False
disallow_untyped_calls = False

Code Conventions

1. Use type hints where practical (gradual typing approach)
2. Docstrings: Use Google-style or standard docstrings
3. Error handling: Use specific exceptions, provide helpful messages
4. Async code: Use asyncio, mark tests with @pytest.mark.asyncio
5. File naming: Use snake_case for all Python files
6. Class naming: Use PascalCase for classes
7. Function naming: Use snake_case for functions and methods
8. Constants: Use UPPER_CASE for module-level constants

Architecture Patterns

Platform Adaptor Pattern (Strategy Pattern)

All platform-specific logic is encapsulated in adaptors:

from skill_seekers.cli.adaptors import get_adaptor

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

# 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')
)

Each adaptor inherits from SkillAdaptor base class and implements:

• format_skill_md() - Format SKILL.md content
• package() - Create platform-specific package
• upload() - Upload to platform API
• validate_api_key() - Validate API key format
• supports_enhancement() - Whether AI enhancement is supported

CLI Architecture (Git-style)

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

The CLI uses subcommands that delegate to existing modules:

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

Available subcommands:

• config - Configuration wizard
• scrape - Documentation scraping
• github - GitHub repository scraping
• pdf - PDF extraction
• unified - Multi-source scraping
• analyze / codebase - Local codebase analysis
• enhance - AI enhancement
• package - Package skill for target platform
• upload - Upload to platform
• cloud - Cloud storage operations
• sync - Sync monitoring
• benchmark - Performance benchmarking
• embed - Embedding server
• install / install-agent - Complete workflow
• stream - Streaming ingestion
• update - Incremental updates
• multilang - Multi-language support
• quality - Quality metrics

MCP Server Architecture

Two implementations:

• server_fastmcp.py - Modern, decorator-based (recommended, ~708 lines)
• server_legacy.py - Legacy implementation

Tools are organized by category:

• Config tools (3 tools): generate_config, list_configs, validate_config
• Scraping tools (8 tools): estimate_pages, scrape_docs, scrape_github, scrape_pdf, scrape_codebase, detect_patterns, extract_test_examples, build_how_to_guides
• Packaging tools (4 tools): package_skill, upload_skill, enhance_skill, install_skill
• Source tools (5 tools): fetch_config, submit_config, add_config_source, list_config_sources, remove_config_source
• Splitting tools (2 tools): split_config, generate_router
• Vector Database tools (4 tools): export_to_weaviate, export_to_chroma, export_to_faiss, export_to_qdrant

Running MCP Server:

# Stdio transport (default)
python -m skill_seekers.mcp.server_fastmcp

# HTTP transport
python -m skill_seekers.mcp.server_fastmcp --http --port 8765

Cloud Storage Architecture

Abstract base class pattern for cloud providers:

• base_storage.py - Defines CloudStorage interface
• s3_storage.py - AWS S3 implementation
• gcs_storage.py - Google Cloud Storage implementation
• azure_storage.py - Azure Blob Storage implementation

Git Workflow

Branch Structure

main (production)
  ↑
  │ (only maintainer merges)
  │
development (integration) ← default branch for PRs
  ↑
  │ (all contributor PRs go here)
  │
feature branches

• main - Production, always stable, protected
• development - Active development, default for PRs
• Feature branches - Your work, created from development

Creating a Feature Branch

# 1. Checkout development
git checkout development
git pull upstream development

# 2. Create feature branch
git checkout -b my-feature

# 3. Make changes, commit, push
git add .
git commit -m "Add my feature"
git push origin my-feature

# 4. Create PR targeting 'development' branch

CI/CD Configuration

GitHub Actions Workflows

All workflows are in .github/workflows/:

tests.yml:

• Runs on: push/PR to main and development
• Lint job: Ruff + MyPy
• Test matrix: Ubuntu + macOS, Python 3.10-3.12
• Coverage: Uploads to Codecov

release.yml:

• Triggered on version tags (v*)
• Builds and publishes to PyPI using uv
• Creates GitHub release with changelog

docker-publish.yml:

• Builds and publishes Docker images

vector-db-export.yml:

• Tests vector database exports

scheduled-updates.yml:

• Scheduled sync monitoring

quality-metrics.yml:

• Quality metrics tracking

test-vector-dbs.yml:

• Vector database integration tests

Pre-commit Checks (Manual)

# Before committing, run:
ruff check src/ tests/
ruff format --check src/ tests/
pytest tests/ -v -x  # Stop on first failure

Security Considerations

API Keys and Secrets

1. Never commit API keys to the repository
2. Use environment variables:
   • ANTHROPIC_API_KEY - Claude AI
   • GOOGLE_API_KEY - Google Gemini
   • OPENAI_API_KEY - OpenAI
   • GITHUB_TOKEN - GitHub API
   • AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY - AWS S3
   • GOOGLE_APPLICATION_CREDENTIALS - GCS
   • AZURE_STORAGE_CONNECTION_STRING - Azure
3. Configuration storage:
   • Stored at ~/.config/skill-seekers/config.json
   • Permissions: 600 (owner read/write only)

Rate Limit Handling

• GitHub API has rate limits (5000 requests/hour for authenticated)
• The tool has built-in rate limit handling with retry logic
• Use --non-interactive flag for CI/CD environments

Custom API Endpoints

Support for Claude-compatible APIs:

export ANTHROPIC_API_KEY=your-custom-api-key
export ANTHROPIC_BASE_URL=https://custom-endpoint.com/v1

Common Development Tasks

Adding a New CLI Command

1. Create module in src/skill_seekers/cli/my_command.py
2. Implement main() function with argument parsing
3. Add entry point in pyproject.toml:

   [project.scripts]
   skill-seekers-my-command = "skill_seekers.cli.my_command:main"
   

4. Add subcommand handler in src/skill_seekers/cli/main.py
5. Add tests in tests/test_my_command.py

Adding a New Platform Adaptor

1. Create src/skill_seekers/cli/adaptors/my_platform.py
2. Inherit from SkillAdaptor base class
3. Implement required methods: package(), upload(), format_skill_md()
4. Register in src/skill_seekers/cli/adaptors/__init__.py
5. Add optional dependencies in pyproject.toml
6. Add tests in tests/test_adaptors/

Adding an MCP Tool

1. Implement tool logic in src/skill_seekers/mcp/tools/category_tools.py
2. Register in src/skill_seekers/mcp/server_fastmcp.py
3. Add test in tests/test_mcp_fastmcp.py

Adding Cloud Storage Provider

1. Create module in src/skill_seekers/cli/storage/my_storage.py
2. Inherit from CloudStorage base class
3. Implement required methods: upload(), download(), list(), delete()
4. Register in src/skill_seekers/cli/storage/__init__.py
5. Add optional dependencies in pyproject.toml

Documentation

Project Documentation

• README.md - Main project documentation
• README.zh-CN.md - Chinese translation
• CLAUDE.md - Detailed implementation guidance
• QUICKSTART.md - Quick start guide
• CONTRIBUTING.md - Contribution guidelines
• TROUBLESHOOTING.md - Common issues and solutions
• AGENTS.md - This file, for AI coding agents
• docs/ - Comprehensive documentation (80+ files)
  • docs/integrations/ - Integration guides for each platform
  • docs/guides/ - User guides
  • docs/reference/ - API reference
  • docs/features/ - Feature documentation
  • docs/blog/ - Blog posts and articles
  • docs/roadmap/ - Roadmap documents

Configuration Documentation

Preset configs are in configs/ directory:

• godot.json - Godot Engine
• blender.json / blender-unified.json - Blender Engine
• claude-code.json - Claude Code
• httpx_comprehensive.json - HTTPX library
• medusa-mercurjs.json - Medusa/MercurJS
• astrovalley_unified.json - Astrovalley
• configs/integrations/ - Integration-specific configs

Key Dependencies

Core Dependencies (Required)

Optional Dependencies

Dev Dependencies (in dependency-groups)

Troubleshooting

Common Issues

ImportError: No module named 'skill_seekers'

• Solution: Run pip install -e .

Tests failing with "package not installed"

• Solution: Ensure you ran pip install -e . in the correct virtual environment

MCP server import errors

• Solution: Install with pip install -e ".[mcp]"

Type checking failures

• MyPy is configured to be lenient (gradual typing)
• Focus on critical paths, not full coverage

Docker build failures

• Ensure you have BuildKit enabled: DOCKER_BUILDKIT=1
• Check that all submodules are initialized: git submodule update --init

Rate limit errors from GitHub

• Set GITHUB_TOKEN environment variable for authenticated requests
• Improves rate limit from 60 to 5000 requests/hour

Getting Help

• Check TROUBLESHOOTING.md for detailed solutions
• Review docs/FAQ.md for common questions
• Visit https://skillseekersweb.com/ for documentation
• Open an issue on GitHub with:
  • Clear title and description
  • Steps to reproduce
  • Expected vs actual behavior
  • Environment details (OS, Python version)
  • Error messages and stack traces

Environment Variables Reference

This document is maintained for AI coding agents. For human contributors, see README.md and CONTRIBUTING.md.

Last updated: 2026-02-08