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claude-legal-skill
AI contract review skill — CUAD risk detection with lawyer-ready redlines.
★ 459 スターMIT
ガイドを見る →Skill_Seekersは、yusufkaraaslanが公開しているオープンソースの開発ツールプロジェクトです（GitHub: yusufkaraaslan/Skill_Seekers）。GitHubスターは15,118、フォークは1,540で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年9月30日です。
Convert documentation sites, GitHub repos and PDFs into Claude AI skills and RAG-ready knowledge assets.
Choose what the AI should learn: a documentation site, a GitHub repo, a local codebase, a PDF, a video, or a mix of several sources in one config.
Install skill-seekers from PyPI, adding extras such as [mcp] or [video] only if you need those source types.
Run skill-seekers create on your source; it scrapes, categorizes, optionally enhances with AI, and detects conflicts between sources.
Package the output for your target - Claude, LangChain, a vector database or one of 19 coding agents - and start using it.
Create knowledge from documentation sites, GitHub repos, local codebases, PDFs, Word, EPUB, Jupyter notebooks, OpenAPI specs, PowerPoint, videos, Confluence, Notion, RSS and more.
Package once and export to 12 LLM platforms (Claude, Gemini, OpenAI and others), 8 RAG/vector targets (LangChain, LlamaIndex, Chroma, FAISS, Qdrant, Pinecone and more) and other formats.
AST parsing across languages, design-pattern detection, usage examples extracted from tests, and AI-written how-to guides from a repo's code, docs and community signals.
Unified multi-source scraping detects conflicts between sources and synthesizes across them when docs, code and PDFs disagree.
Enhance generated skills through provider APIs or local agents such as Claude Code, Codex and Copilot with no API costs, with 68 workflow presets.
Ships an MCP server (stdio or HTTP) with dozens of tools so Claude Code, Cursor and other clients can create and package skills conversationally.
Clone Skill Seekers if you want the source; most users install from PyPI instead.
git clone https://github.com/yusufkaraaslan/Skill_Seekers.git
cd Skill_SeekersInstall the core CLI (scraping, GitHub, PDF and packaging) into your Python environment.
pip install skill-seekersTurn a documentation site into a skill, then package it as a Claude skill zip.
skill-seekers create https://docs.djangoproject.com/
skill-seekers package output/django --target claudeIt is developed by yusufkaraaslan with community contributors and sponsors.
Yes. The tool is MIT-licensed and free, including for commercial use.
Not necessarily: AI enhancement can run through local agents such as Claude Code with no API costs, or through provider APIs (Anthropic, Gemini, OpenAI and others) using your own key.
Skill Seekers automates the whole pipeline - scraping, categorization, enhancement, quality scoring and packaging for 22 targets - instead of hand-writing and maintaining skill files yourself.
Run pip install skill-seekers, then skill-seekers create <docs-url> and skill-seekers package to produce a ready-to-use skill zip.
AI contract review skill — CUAD risk detection with lawyer-ready redlines.
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Pack your whole repository into one AI-friendly file for Claude, ChatGPT, Gemini and more
LibreAIカタログに掲載された開発ツールプロジェクトの中で最もスターの多いもの — Skill_Seekersが合わなかった場合に検討したい、現実的な候補リストです。
The open source coding agent
The model-definition framework behind modern AI — thousands of pretrained transformers for text, vision, and audio.
Anthropic's agentic coding tool that lives in your terminal and works across your whole codebase
Cross-platform desktop all-in-one manager for Claude Code, Codex, OpenCode and other coding agents
OpenAI's lightweight coding agent that runs locally in your terminal
カタログの中でSkill_Seekersとスター数が近い開発ツールプロジェクト — 前後の棚もそのまま見て回れます。
Open-source terminal AI coding agent built for large projects and multi-step, real-world tasks.
Agentic IDE that runs 100+ parallel coding agents in isolated workspaces on your own subscriptions.
Bridge local AI coding agents (Claude Code, Cursor, Gemini CLI, Codex) to messaging platforms (Feishu/Lark, DingTalk, Slack, Telegram, Discord, LINE, WeChat …
Turn domain documents into high-quality LLM fine-tuning, RAG and evaluation datasets through a visual UI.
Fine-tune and deploy 600+ LLMs and 300+ multimodal models with PEFT or full-parameter training.
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