Skill Seekers CLI
A CLI and MCP server that turns doc sites, repos, PDFs and videos into skills and RAG-ready data
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- Last commit
- MIT
Extended access
Why this level
- Scrapes external sites and repositories
- Uses provider API keys for enhancement and uploads
- Writes skill files into several agents' directories
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add cli/skill-seekersDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- Scrapes external sites and repositories
- Uses provider API keys for enhancement and uploads
- Writes skill files into several agents' directories
This is third-party code. Review the repository files before installing.
What it does
Skill Seekers gathers knowledge from 18 source types: documentation sites, GitHub repos, local code, PDF, DOCX, EPUB, Jupyter, OpenAPI, video, Confluence, Notion and Slack or Discord exports. Output is packaged for 22 targets: skills for Claude, Gemini and OpenAI, LangChain and LlamaIndex documents, and data for Pinecone, Chroma, Qdrant and more. scan inspects a project and emits configs for detected frameworks, and install-agent places a skill into 19 coding agents. It also ships a 40-tool MCP server.
Who it is for. Developers who want to give agents knowledge of a framework or their own codebase, and teams building RAG.
Good fit when
- You need a skill for a framework's docs that no existing set covers
- You want a skill built from your own codebase
- You are preparing documentation data for a vector database
- You want to install a skill into several agents at once
Not a fit when
- An official skill from the library authors already exists
- The source's terms of use do not allow scraping
Example request
Build a skill from the Django documentation and install it into Claude CodeLimitations
Requires Python 3.10+ and Git; some sources need extras such as skill-seekers[video] or skill-seekers[notion]. AI enhancement needs an agent or provider API key, and uploading to Claude needs ANTHROPIC_API_KEY. Visual video extraction needs GPU dependencies.
How to disable. Remove the MCP server entry from your client config and the installed skills from agent directories; uninstall the package with pip.
FAQ
How do I start the MCP server?
Install skill-seekers[mcp] and run python -m skill_seekers.mcp.server_fastmcp for stdio, or add --transport http --port 8765 for HTTP.
Can I choose the enhancement agent?
Yes, with --agent, for example --agent kimi, or a custom command via --agent-cmd.
Discussion
No questions or reviews yet.
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README in short
Skill Seekers presents itself as a data layer for AI systems, turning documentation and other sources into structured knowledge assets. It installs from PyPI, and the quick start is three commands: install, create and package. The README lists source types, export targets, codebase analysis with pattern detection, MCP integration and a table of agent install paths. The project reports 3,900+ tests and has docs at skillseekersweb.com. MIT licensed.
README badge
Original SKILL.md
--- name: skill-builder description: Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources. --- # Skill Builder You have access to the Skill Seekers MCP server which provides 40 tools for converting knowledge sources into AI-ready skills. ## When to Use This Skill Use this skill when the user: - Wants to create an AI skill from a documentation site, GitHub repo, PDF, video, or other source - Needs to convert documentation into a format suitable for LLM consumption - Wants to update or sync existing skills with their source documentation - Needs to export skills to vector databases (Weaviate, Chroma, FAISS, Qdrant) - Asks about scraping, converting, or packaging documentation for AI ## Source Type Detection Automatically detect the source type from user input: | Input Pattern | Source Type | Tool to Use | |---------------|-------------|-------------| | `https://...` (not GitHub/YouTube) | Documentation | `scrape_docs` | | `owner/repo` or `github.com/...` | GitHub | `scrape_github` | | `*.pdf` | PDF | `scrape_pdf` | | YouTube/Vimeo URL or video file | Video | `scrape_video` | | Local directory path | Codebase | `scrape_codebase` | | `*.ipynb`, `*.html`, `*.yaml` (OpenAPI), `*.adoc`, `*.pptx`, `*.rss`, `*.1`-`.8` | Various | `scrape_generic` | | JSON config file | Unified | Use config with `scrape_docs` | ## Recommended Workflow 1. **Detect source type** from the user's input 2. **Generate or fetch config** using `generate_config` or `fetch_config` if needed 3. **Estimate scope** with `estimate_pages` for documentation sites 4. **Scrape the source** using the appropriate scraping tool 5. **Enhance** with `enhance_skill` if the user wants AI-powered improvements 6. **Package** with `package_skill` for the target platform 7. **Export to vector DB** if requested using `export_to_*` tools ## Available MCP Tools ### Config Management - `generate_config` — Generate a scraping config from a URL - `list_configs` — List available preset configs - `validate_config` — Validate a config file ### Scraping (use based on source type) - `scrape_docs` — Documentation sites - `scrape_github` — GitHub repositories - `scrape_pdf` — PDF files - `scrape_video` — Video transcripts - `scrape_codebase` — Local code analysis - `scrape_generic` — Jupyter, HTML, OpenAPI, AsciiDoc, PPTX, RSS, manpage, Confluence, Notion, chat ### Post-processing - `enhance_skill` — AI-powered skill enhancement - `package_skill` — Package for target platform