by ratel-ai · MCP Server · ★ 550
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx skills add ratel-ai/skills --all
pnpm add @ratel-ai/sdkTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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 | 550 |
| Forks | 32 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65/100 |
| Open Issues | 15 |
| Last Updated | 2026-11-04 |
| Created | 2025-11-12 |
| Platforms | claude-code, mcp, rust |
| Est. Tokens | ~25k |
Ratel Context engineering for AI agents — engineer the context your agent actually needs, on every turn. Docs • Skills • Roadmap • Discord Most agents stuff every tool, skill, and memory into the context window each turn — burning tokens, drifting on the long tail. Ratel sits between the agent and its catalog, and resolves only what matters for this turn.
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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: