by Albertchamberlain · MCP Server · ★ 100
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pipx install awesome-okfTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 100 |
| Forks | 7 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69/100 |
| Open Issues | 11 |
| Last Updated | 2026-10-09 |
| Created | 2026-09-07 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~17k |
Awesome OKF The curated catalog of Open Knowledge Format resources. YAML-driven. Agent-searchable. Community-curated. English 한국어 Catalog  ·  Connect an Agent  ·  CLI  ·  Contributing What is OKF Open Knowledge Format (OKF) is an open specification by Google Cloud — define knowledge as a directory of Markdown files with YAML frontmatter and a sm
These tools work well together with Awesome-OKF for enhanced workflows:
Looking for a Awesome-OKF alternative? If you're comparing Awesome-OKF with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
The OKF toolkit for Claude Code — author, maintain, validate & visualize Open Knowledge Format bundles. Plugin
Open Knowledge Format for coding agents. Author, validate, lint, search, and visualize portable Markdown knowl
Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction
OKF (Open Knowledge Format): Durable, structured memory for AI agents. Author, validate, consume, and maintain
Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.
LLM-powered knowledge base from your Claude Code, Codex CLI, Copilot, Cursor & Gemini sessions. Karpathy's LLM
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: