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
Tools
Tools
No tool list yet. Remote servers get one after the first health check.
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
Does Skill Seekers need an API key?
No required environment variable is listed on this record.
What transport does Skill Seekers use?
Skill Seekers runs locally over stdio.
When was Skill Seekers last checked?
Last checked 2026-11-08. 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
🧠 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.
# 1. Install
pip install skill-seekers
# 2. Create a skill from any source
skill-seekers create https://docs.djangoproject.com/
# Optional: preview how a source will be detected without creating anything
skill-seekers detect https://docs.djangoproject.com/ --json
# 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:
Not sure what you need? Run the wizard: skill-seekers-setup
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.
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.
Every AI call runs through one transport, in API mode (Anthropic, Google Gemini, OpenAI, Moonshot/Kimi, MiniMax) or LOCAL mode (Claude Code, Kimi Code, Codex, Copilot, OpenCode, custom agents — no API costs). Control depth with --enhance-level 0-3 and pick an agent with --agent.
One config can pull documentation, GitHub, PDFs, videos, and more into a single knowledge asset, with conflict detection and pairwise synthesis across sources.
skill-seekers create <source> --index adds a stdlib-only scripts/search.py and a SQLite FTS5 index over the generated references, so an agent can find the right file#anchor before reading a large Markdown file wholesale. Off by default; the Markdown is untouched.
YouTube, Vimeo, and local files. Three-tier transcript fallback (subtitles → YouTube transcript API → local Whisper), plus optional visual extraction that OCRs on-screen code from sampled frames.
Quality scoring with a gate (skill-seekers quality output/react/ --threshold 7) or JSON stdout (skill-seekers quality output/react/ --json), provisional English readability metrics (informational — they never affect the score), doc-change detection with scheduled re-scrapes and notifications, streaming ingestion for very large doc sets, and incremental updates.
graph LR
A[Documentation Website] --> B[Skill Seekers]
B --> C[Scraper]
B --> D[AI Enhancement]
B --> E[Packager]
C --> F[Organized References]
D --> F
F --> E
E --> G[AI Skill .zip]
G --> H[Upload to AI Platform]
Scrape — extract every page (checking llms.txt first)
Categorize — organize content into topics (API, guides, tutorials, …)
Enhance — AI writes a comprehensive SKILL.md with examples
HTML parser fallback for broken markup (#96) — severely malformed pages no longer scrape as empty; well-formed pages are byte-identical.
Transient-failure retries — the doc scraper (#97) and MCP fetch_config (#92) now retry connection blips and 5xx with backoff; 4xx still fails fast.
Whisper transcription fallback (#420) — local videos without subtitles finally get a real transcript.
MiniMax image OCR + registry-driven multimodal providers (#423) — providers declare their wire protocol and image capability; China-issued keys work against the right endpoint.
Token-lean GitHub issue defaults (#169) — GitHub skills no longer bundle full closed-issue history by default.
Env-driven CORS across all three servers (#422, #424) — no more wildcard origins with credentials.
skill-seekers doctor # diagnose installation & environment
skill-seekers doctor --json # machine-readable diagnostics for CI and agents
skill-seekers sync-config # detect config drift
