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Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection

MCP Servers14.1k stars1.5k forksPythonMITUpdated yesterday
Editor's note

Skill Seekers is a Python MCP server and CLI tool that ingests documentation websites, GitHub repositories, PDFs, videos, Jupyter notebooks, and over ten other source types, then converts them into structured knowledge assets exportable to multiple AI targets. Running a single `skill-seekers create` command against a URL or local path scrapes and parses the source material, while `skill-seekers package` exports the result to formats including Claude Skills (ZIP plus YAML for Claude Code and the Claude API), LangChain Documents, LlamaIndex TextNodes, Pinecone-ready Markdown, and local vector databases such as ChromaDB and FAISS. The MCP integration exposes 40 tools, enabling Claude Desktop and Claude Code users to trigger ingestion and packaging directly from a conversation. A built-in conflict detection system flags overlapping or contradictory content across sources before packaging. Developers building RAG pipelines, AI coding assistants, or Claude Skills benefit most, and a companion community config repository at skillseekersweb.com offers 24-plus preset configurations for common documentation sources.

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Last scanned: 6/11/2026
Install in Claude Code / Claude Desktop
Method: pip / Python · skill-seekers
Claude Code CLI
claude mcp add skill-seekers -- python -m skill-seekers
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "skill-seekers": {
      "command": "python",
      "args": ["-m", "skill-seekers"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Install first: pip install skill-seekers
Use cases

MCP Servers overview

<p align="center">
  <img src="docs/assets/logo.png" alt="Skill Seekers" width="200"/>
</p>

# Skill Seekers

English | [简体中文](README.zh-CN.md) | [日本語](README.ja.md) | [한국어](README.ko.md) | [Español](README.es.md) | [Français](README.fr.md) | [Deutsch](README.de.md) | [Português](README.pt-BR.md) | [Türkçe](README.tr.md) | [العربية](README.ar.md) | [हिन्दी](README.hi.md) | [Русский](README.ru.md)

[![Version](https://img.shields.io/badge/version-3.7.0-blue.svg)](https://github.com/yusufkaraaslan/Skill_Seekers/releases)
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[![Website](https://img.shields.io/badge/Website-skillseekersweb.com-blue.svg)](https://skillseekersweb.com/)
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<a href="https://trendshift.io/repositories/18329" target="_blank"><img src="https://trendshift.io/api/badge/repositories/18329" alt="yusufkaraaslan%2FSkill_Seekers | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>

**🧠 The data layer for AI systems.** Skill Seekers turns documentation sites, GitHub repos, PDFs, videos, notebooks, wikis, and 10+ more source types into structured knowledge assets—ready to power AI Skills (Claude, Gemini, OpenAI), RAG pipelines (LangChain, LlamaIndex, Pinecone), and AI coding assistants (Cursor, Windsurf, Cline) in minutes, not hours.

> 🌐 **[Visit SkillSeekersWeb.com](https://skillseekersweb.com/)** - Browse 24+ preset configs, share your configs, and access complete documentation!

> 📋 **[View Development Roadmap & Tasks](https://github.com/users/yusufkaraaslan/projects/2)** - 134 tasks across 10 categories, pick any to contribute!

## 🌐 Ecosystem

Skill Seekers is a multi-repo project. Here's where everything lives:

| Repository | Description | Links |
|-----------|-------------|-------|
| **[Skill_Seekers](https://github.com/yusufkaraaslan/Skill_Seekers)** | Core CLI & MCP server (this repo) | [PyPI](https://pypi.org/project/skill-seekers/) |
| **[skillseekersweb](https://github.com/yusufkaraaslan/skillseekersweb)** | Website & documentation | [Live](https://skillseekersweb.com/) |
| **[skill-seekers-configs](https://github.com/yusufkaraaslan/skill-seekers-configs)** | Community config repository | |
| **[skill-seekers-action](https://github.com/yusufkaraaslan/skill-seekers-action)** | GitHub Action for CI/CD | |
| **[skill-seekers-plugin](https://github.com/yusufkaraaslan/skill-seekers-plugin)** | Claude Code plugin | |
| **[homebrew-skill-seekers](https://github.com/yusufkaraaslan/homebrew-skill-seekers)** | Homebrew tap for macOS | |

> **Want to contribute?** The website and configs repos are great starting points for new contributors!

## 🧠 The Data Layer for AI Systems

**Skill Seekers is the universal preprocessing layer** that sits between raw documentation and every AI system that consumes it. Whether you are building Claude skills, a LangChain RAG pipeline, or a Cursor `.cursorrules` file — the data preparation is identical. You do it once, and export to all targets.

```bash
# One command → structured knowledge asset
skill-seekers create https://docs.react.dev/
# or: skill-seekers create facebook/react
# or: skill-seekers create ./my-project

# Export to any AI system
skill-seekers package output/react --target claude      # → Claude AI Skill (ZIP)
skill-seekers package output/react --target langchain   # → LangChain Documents
skill-seekers package output/react --target llama-index # → LlamaIndex TextNodes
skill-seekers package output/react --target cursor      # → .cursorrules
skill-seekers package output/react --target ibm-bob     # → IBM Bob skill directory
```

### What gets built

| Output | Target | What it powers |
|--------|--------|---------------|
| **Claude Skill** (ZIP + YAML) | `--target claude` | Claude Code, Claude API |
| **Gemini Skill** (tar.gz) | `--target gemini` | Google Gemini |
| **OpenAI / Custom GPT** (ZIP) | `--target openai` | GPT-4o, custom assistants |
| **LangChain Documents** | `--target langchain` | QA chains, agents, retrievers |
| **LlamaIndex TextNodes** | `--target llama-index` | Query engines, chat engines |
| **Haystack Documents** | `--target haystack` | Enterprise RAG pipelines |
| **Pinecone-ready** (Markdown) | `--target markdown` | Vector upsert |
| **ChromaDB / FAISS / Qdrant** | `--target chroma/faiss/qdrant` | Local vector DBs |
| **IBM Bob Skill** (directory) | `--target ibm-bob` | IBM Bob project/global skills |
| **Cursor** `.cursorrules` | `--target markdown` → copy SKILL.md | Cursor IDE `.cursorrules` |
| **Windsurf / Cline / Continue** | `--target claude` → copy | VS Code, IntelliJ, Vim |

### Why it matters

- ⚡ **99% faster** — Days of manual data prep → 15–45 minutes
- 🎯 **AI Skill quality** — 500+ line SKILL.md files with examples, patterns, and guides
- 📊 **RAG-ready chunks** — Smart chunking preserves code blocks and maintains context
- 🎬 **Videos** — Extract code, transcripts, and structured knowledge from YouTube and local videos
- 🔄 **Multi-source** — Combine 18 source types (docs, GitHub, PDFs, videos, notebooks, wikis, and more) into one knowledge asset
- 🌐 **One prep, every target** — Export the same asset to 21 platforms without re-scraping
- ✅ **Battle-tested** — 3,700+ tests, 24+ framework presets, production-ready

## 🚀 Quick Start (3 Commands)

```bash
# 1. Install
pip install skill-seekers

# 2. Create skill from any source
skill-seekers create https://docs.django.com/

# 3. Package for your AI platform
skill-seekers package output/django --target claude
```

**That's it!** You now have `output/django-claude.zip` ready to use.

```bash
# Use a different AI agent for enhancement (default: claude)
skill-seekers create https://docs.django.com/ --agent kimi
skill-seekers create https://docs.django.com/ --agent codex
skill-seekers create https://docs.django.com/ --agent-cmd "my-custom-agent run"
```

### 🛰️ AI-driven project scan (new)

Point `scan` at any project and an AI agent reads its manifests, README,
Dockerfile/CI and sampled source imports — then emits one config per detected
framework plus a `<project>-codebase.json` for your own code. Pins the
detected version so re-running reports bumps:

```bash
skill-seekers scan ./my-react-app --out ./configs/scanned/
# → react.json, vite.json, tailwind.json, jest.json, my-react-app-codebase.json

# Then build any of them
skill-seekers create ./configs/scanned/react.json
```

If a detection has no existing preset, the AI generates a fresh config; on
exit you can optionally publish it back to the [community registry](https://github.com/yusufkaraaslan/skill-seekers-configs).

### Other Sources (18 Supported)

```bash
# GitHub repository
skill-seekers create facebook/react

# Local project
skill-seekers create ./my-project

# PDF document
skill-seekers create manual.pdf

# Word document
skill-seekers create report.docx

# EPUB e-book
skill-seekers create book.epub

# Jupyter Notebook
skill-seekers create notebook.ipynb

# OpenAPI spec
skill-seekers create openapi.yaml

# PowerPoint presentation
skill-seekers create presentation.pptx

# AsciiDoc document
skill-seekers create guide.adoc

# Local HTML file (auto-detected by extension)
skill-seekers create page.html

# Whole directory of HTML files (auto-detected for HTML-dominant dirs)
skill-seekers create ./mirror_output/site/

# Force HTML mode on a mixed/code-heavy directory
skill-seekers create ./repo/ --html-path ./repo/docs/build/html/

# RSS/Atom feed
skill-seekers create feed.rss

# Man page
skill-seekers create curl.1

# Video (YouTube, Vimeo, or local file — requires skill-seekers[video])
skill-seekers create --video-url https://www.youtube.com/watch?v=... --name mytutorial
# First time? Auto-install GPU-aware visual deps:
skill-seekers create --setup

# Confluence wiki
skill-seekers create --space-key TEAM --name wiki

# Notion pages
skill-seekers create --database-id ... --name docs

# Slack/Discord chat export
skill-seekers create --chat-export-path ./slack-export --name team-chat
```

### Export Everywhere

```bash
# Package for multiple platforms
for platform in claude gemini openai langchain; do
  skill-seekers package output/django --target $platform
done
```

## What is Skill Seekers?

Skill Seekers is the **data layer for AI systems**. It transforms 18 source types—documentation websites, GitHub repositories, PDFs, videos, Jupyter Notebooks, Word/EPUB/AsciiDoc documents, OpenAPI specs, PowerPoint presentations, RSS feeds, man pages, Confluence wikis, Notion pages, Slack/Discord exports, and more—into structured knowledge assets for every AI target:

| Use Case | What you get | Examples |
|----------|-------------|---------|
| **AI Skills** | Comprehensive SKILL.md + references | Claude Code, Gemini, 
ai-toolsast-parserautomationclaude-aiclaude-skillscode-analysisconflict-detectiondocumentationdocumentation-generatorgithubgithub-scrapermcpmcp-servermulti-sourceocrpdfpythonweb-scraping

What people ask about Skill_Seekers

What is yusufkaraaslan/Skill_Seekers?

+

yusufkaraaslan/Skill_Seekers is mcp servers for the Claude AI ecosystem. Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection It has 14.1k GitHub stars and was last updated yesterday.

How do I install Skill_Seekers?

+

You can install Skill_Seekers by cloning the repository (https://github.com/yusufkaraaslan/Skill_Seekers) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is yusufkaraaslan/Skill_Seekers safe to use?

+

Our security agent has analyzed yusufkaraaslan/Skill_Seekers and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.

Who maintains yusufkaraaslan/Skill_Seekers?

+

yusufkaraaslan/Skill_Seekers is maintained by yusufkaraaslan. The last recorded GitHub activity is from yesterday, with 102 open issues.

Are there alternatives to Skill_Seekers?

+

Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.

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