Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/yusufkaraaslan/skill_seekers/agents-mdgit clone --depth 1 https://github.com/yusufkaraaslan/Skill_SeekersWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.03471 | $0.03471 |
| Opus 5 | $0.01736 | $0.01736 |
| Sonnet 5 | $0.00694 | $0.00694 |
| Haiku 4.5 | $0.00347 | $0.00347 |
Grade A, and why
Skill_Seekers AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md - Skill Seekers
Comprehensive reference for AI coding agents. Skill Seekers is a Python CLI tool (v3.6.0) that converts documentation sites, GitHub repos, PDFs, videos, notebooks, wikis, and more into AI-ready skills for 21+ LLM platforms and RAG pipelines.
Project Overview
Skill Seekers is a universal preprocessing layer that transforms raw documentation and code into structured knowledge assets. It supports 17+ source types and exports to 21+ AI platforms including Claude, Gemini, OpenAI, LangChain, LlamaIndex, and various vector databases.
Key Capabilities
- Source Types (17): Documentation websites, GitHub repos, PDFs, Word docs, EPUBs, videos, local codebases, Jupyter notebooks, HTML, OpenAPI specs, AsciiDoc, PowerPoint, Confluence, Notion, RSS feeds, man pages, chat exports
- Export Targets (21): Claude, Gemini, OpenAI, MiniMax, OpenCode, Kimi, DeepSeek, Qwen, OpenRouter, Together AI, Fireworks AI, Markdown, LangChain, LlamaIndex, Haystack, Weaviate, ChromaDB, FAISS, Qdrant, Pinecone
- MCP Server: FastMCP-based Model Context Protocol server for AI assistant integration
Setup
# REQUIRED before running tests (src/ layout — tests hard-exit if package not installed)
pip install -e .
# With dev tools (pytest, ruff, mypy, coverage)
pip install -e ".[dev]"
# With specific LLM platform support
pip install -e ".[gemini]" # Google Gemini
pip install -e ".[openai]" # OpenAI ChatGPT
pip install -e ".[all-llms]" # All LLM platforms
# With all optional dependencies (except video-full)
pip install -e ".[all]"
# Full video processing (heavy dependencies)
pip install -e ".[video-full]"
Note: tests/conftest.py checks that skill_seekers is importable and calls sys.exit(1) if not. Always install in editable mode first.
Environment Variables
Create a .env file or export these variables:
ANTHROPIC_API_KEY # For Claude AI enhancement
GOOGLE_API_KEY # For Gemini support
OPENAI_API_KEY # For OpenAI support
GITHUB_TOKEN # For GitHub repo scraping (higher rate limits)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 304 lines · 3,471 tokens per session scan A 80d1396ad9ea
Skill_Seekers AGENTS.md is an instructions file published in the GitHub repository yusufkaraaslan/Skill_Seekers (14,860 stars, last pushed yesterday), licensed MIT. It adds 3,471 tokens to every session, about $0.0174 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
sparX CLAUDE.md
Instructions for Epistates/sparX, covering sparx — x post optimization system, how this project works, phoenix algorithm — essential context, the scoring formula and critical rules (violating these kills reach).
nanobrowser CLAUDE.md
Instructions for nanobrowser/nanobrowser, covering claude.md, project overview, development commands, workspace tips and architecture.
stealth-browser-mcp AGENTS.md
Instructions for vibheksoni/stealth-browser-mcp, covering agent instructions, start here, recommended proxy services, environment and validation.
octocode CLAUDE.md
Claude Code instructions for bgauryy/octocode: Read AGENTS.md. Discover repository skills under .agents/skills and activate only those matching the task.
ai-workflow AGENTS.md
Instructions for cunhaax/ai-workflow, covering ai workflow template, rules — non-negotiable, project overview, commands and architecture.
substack-mcp-plus CLAUDE.md
Instructions for ty13r/substack-mcp-plus, covering claude.md, quick command reference (use these!), always activate virtual environment first, run tests (use python3!) and code quality.