Awesome-OKF — security grade SAFE, quality 69/100

Security audit verdict: SAFE · quality 69/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by Albertchamberlain · MCP Server · ★ 100

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is Awesome-OKF safe to install? View the security audit →

About Awesome-OKF

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

agent-memoryagent-skillsai-agentsai-memoryawesomeawesome-listclaude-codeknowledge-baseknowledge-graphknowledge-management

Quick Facts

Stars100
Forks3
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score69.2050039991145/100
Open Issues8
Last Updated2026-09-15
Created2026-09-07
Platformsclaude-code, mcp, python
Est. Tokens~17k

Compatible Skills

These tools work well together with Awesome-OKF for enhanced workflows:

  • okf-knowledge — semantic(0.43)+complementary+same_lang+similar_pop+shared_platform (60%)

Awesome-OKF alternative? Top 6 similar tools

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.

  • okf-skills by scaccogatto · ⭐ 359

    The OKF toolkit for Claude Code — author, maintain, validate & visualize Open Knowledge Format bundles. Plugin

  • okf-gem by serradura · ⭐ 126

    Open Knowledge Format for coding agents. Author, validate, lint, search, and visualize portable Markdown knowl

  • remnic by joshuaswarren · ⭐ 206

    Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction

  • okf by serradura · ⭐ 154

    OKF (Open Knowledge Format): Durable, structured memory for AI agents. Author, validate, consume, and maintain

  • Ori-Mnemos by aayoawoyemi · ⭐ 324

    Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.

  • llm-wiki by Pratiyush · ⭐ 362

    LLM-powered knowledge base from your Claude Code, Codex CLI, Copilot, Cursor & Gemini sessions. Karpathy's LLM

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Frequently Asked Questions

What is Awesome-OKF?

Awesome-OKF is OKF (Open Knowledge Format) — curated catalog of tools, plugins, skills, proposals, and docs for agent-friendly knowledge. YAML-driven, agent-searchable, MCP-ready.. It is categorized as a MCP Server with 100 GitHub stars.

What programming language is Awesome-OKF written in?

Awesome-OKF is primarily written in Python. It covers topics such as agent-memory, agent-skills, ai-agents.

How do I install or use Awesome-OKF?

You can find installation instructions and usage details in the Awesome-OKF GitHub repository at github.com/Albertchamberlain/Awesome-OKF. The project has 100 stars and 3 forks, indicating an active community.

What license does Awesome-OKF use?

Awesome-OKF is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to Awesome-OKF?

The top alternatives to Awesome-OKF on Agent Skills Hub include okf-skills, okf-gem, remnic. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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:

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