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

Skill Seekers

English | 简体中文 | 日本語 | 한국어 | Español | Français | Deutsch | Português | Türkçe | العربية | हिन्दी | Русский

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🧠 The data layer for AI systems. Skill Seekers turns documentation sites, GitHub repos, PDFs, videos, notebooks, wikis, and more — 18 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). Prepare once, export to 22 targets.

💛 Sponsors

<!-- SPONSORS:START -->

Launch Partner

<p align="center"> <a href="https://www.atlascloud.ai/"><img src="docs/assets/sponsors/atlas-cloud.png" alt="Atlas Cloud" width="200"></a><br/>_{<b>Launch Partner</b>} </p>

Atlas Cloud — A full-modal, OpenAI-compatible AI inference platform. Skill Seekers supports it as a packaging/enhancement target via --target atlas with ATLAS_API_KEY.

Silver Sponsors

<p align="center"> <a href="https://www.rapidproxy.io/?utm_source=skillseekers&utm_medium=sponsor"><img src="docs/assets/sponsors/rapidproxy.png" alt="RapidProxy" width="140"></a><br/>_{<b>Sponsor — Silver</b>} </p> <!-- SPONSORS:END -->

Become a sponsor · GitHub Sponsors

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🚀 Quick Start

# 1. Install
pip install skill-seekers

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

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

You now have output/django-claude.zip, ready to use.

# Pick a different AI agent for enhancement (default: claude)
skill-seekers create https://docs.djangoproject.com/ --agent kimi
skill-seekers create https://docs.djangoproject.com/ --agent-cmd "my-custom-agent run"

🛰️ AI-driven project scan

Point scan at a 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:

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

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.

All 18 source types

skill-seekers create facebook/react            # GitHub repository
skill-seekers create ./my-project              # Local codebase
skill-seekers create manual.pdf                # PDF
skill-seekers create report.docx               # Word
skill-seekers create book.epub                 # EPUB
skill-seekers create notebook.ipynb            # Jupyter
skill-seekers create openapi.yaml              # OpenAPI/Swagger
skill-seekers create presentation.pptx         # PowerPoint
skill-seekers create guide.adoc                # AsciiDoc
skill-seekers create page.html                 # Local HTML (or a whole dir)
skill-seekers create feed.rss                  # RSS/Atom
skill-seekers create curl.1                    # Man page

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

skill-seekers create --space-key TEAM --name wiki               # Confluence
skill-seekers create --database-id ... --name docs              # Notion
skill-seekers create --chat-export-path ./slack-export --name team-chat  # Slack/Discord

See the Scraping Guide for every source type and its options.

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📦 Installation

pip install skill-seekers              # Core: scraping, GitHub, PDF, packaging
pip install skill-seekers[all-llms]    # + every LLM platform
pip install skill-seekers[mcp]         # + MCP server
pip install skill-seekers[all]         # Everything

Not sure what you need? Run the wizard: skill-seekers-setup

<details> <summary><b>All installation extras</b></summary>

| Install | Adds | |---------|------| | skill-seekers[gemini] | Google Gemini support | | skill-seekers[openai] | OpenAI ChatGPT support | | skill-seekers[all-llms] | All LLM platforms | | skill-seekers[mcp] | MCP server for Claude Code, Cursor, etc. | | skill-seekers[video] | YouTube/Vimeo transcript & metadata extraction | | skill-seekers[video-full] | + Whisper transcription & visual frame extraction | | skill-seekers[jupyter] | Jupyter Notebook support | | skill-seekers[pptx] | PowerPoint support | | skill-seekers[confluence] | Confluence wiki support | | skill-seekers[notion] | Notion pages support | | skill-seekers[rss] | RSS/Atom feed support | | skill-seekers[chat] | Slack/Discord chat export support | | skill-seekers[asciidoc] | AsciiDoc support | | skill-seekers[all] | Everything |

Video visual deps (GPU-aware): after installing skill-seekers[video-full], run skill-seekers create --setup to auto-detect your GPU and install the matching PyTorch variant + easyocr.

</details>

Prerequisites: Python 3.10+, Git. New here? → Bulletproof Quick Start 🎯

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📚 Documentation

| I want to... | Read this | |--------------|-----------| | Get started quickly | Quick Start — 3 commands to your first skill | | Understand the concepts | Core Concepts | | Scrape sources | Scraping Guide — all 18 source types | | Enhance skills with AI | Enhancement Guide · Enhancement Modes | | Export skills | Packaging Guide | | Build workflows | Workflows | | Look up a command | CLI Reference — all 19 commands | | Configure | Config Format · Environment Variables | | Set up MCP | MCP Setup · MCP Reference | | Integrate with RAG / IDEs | LangChain · RAG Pipelines · Cursor · Windsurf · Cline | | Handle huge doc sets | Large Documentation — 10K–40K+ pages | | Understand the architecture | UML Architecture — 14 diagrams | | Fix a problem | Troubleshooting |

Complete documentation index: docs/README.md

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🎯 What you get

| Use case | Output | Powers | |----------|--------|--------| | AI Skills | Comprehensive SKILL.md + reference files | Claude Code, Gemini, GPT | | RAG pipelines | Chunked documents with rich metadata | LangChain, LlamaIndex, Haystack | | Vector databases | Pre-formatted data ready for upsert | Pinecone, Chroma, Weaviate, FAISS, Qdrant | | AI coding assistants | Context files your IDE AI reads automatically | Cursor, Windsurf, Cline, Continue.dev |

Export targets (22)

skill-seekers package output/react --target claude      # → Claude Skill (ZIP + YAML)
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 ibm-bob     # → IBM Bob skill directory

LLM platforms (12): claude · gemini · openai · minimax · opencode · kimi · deepseek · qwen · openrouter · together · fireworks · markdown RAG & vector (8): langchain · llama-index · haystack · chroma · faiss · weaviate · qdrant · pinecone Other (2): atlas · ibm-bob

See the Feature Matrix for per-platform support details.

Why it matters

• ⚡ 99% faster — days of manual data prep → 15–45 minutes
• 🎯 Real skill quality — 500+ line SKILL.md files with examples, patterns, and guides
• 📊 RAG-ready chunks — smart chunking preserves code blocks and context
• 🔄 Multi-source — combine docs + GitHub + PDFs + videos into one knowledge asset
• 🌐 One prep, every target — export to 22 targets without re-scraping
• ✅ Battle-tested — 3,900+ tests, 68 workflow presets, production-ready

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✨ Key capabilities

<details> <summary><b>Documentation scraping</b> — SPA discovery, llms.txt, smart categorization</summary>

Three-layer discovery for JavaScript SPA sites (sitemap.xml → llms.txt → headless browser rendering), automatic llms.txt detection (10× faster when present), smart topic categorization, and a lenient HTML parser fallback so broken markup still scrapes.

→ Scraping Guide · llms.txt Support

</details> <details> <summary><b>GitHub & codebase analysis (C3.x)</b> — AST parsing, pattern detection, how-to guides</summary>

Three-stream architecture: code analysis (AST, design patterns, tests), documentation (README, docs/, wiki), and community (issues, PRs, metadata). The C3.x pipeline adds 10 GoF pattern detectors across 9 languages, usage examples extracted from tests, AI-written how-to guides, config extraction, and architecture overviews.

skill-seekers create ./my-project --preset quick          # 1–2 min, surface level
skill-seekers create ./my-project --preset standard       # balanced (default)
skill-seekers create ./my-project --preset comprehensive  # deep, exhaustive

→ Pattern Detection · How-To Guides · Test Example Extraction

</details>