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 →
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
| Stars | 100 |
| Forks | 3 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.2050039991145/100 |
| Open Issues | 8 |
| Last Updated | 2026-09-15 |
| Created | 2026-09-07 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~17k |
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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.
Awesome-OKF is primarily written in Python. It covers topics such as agent-memory, agent-skills, ai-agents.
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.
Awesome-OKF is released under the MIT license, making it free to use and modify according to the license terms.
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.
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: