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 ratel-ai · MCP Server · ★ 434
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ratel safe to install? View the security audit →
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. Integrate Ratel in 60 seconds The fastest way to get Ratel into your agent is the Ratel skills suite — five Claude Code / Cursor / Codex skills that integrate Ratel, plan observability, d
| Stars | 434 |
| Forks | 21 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.5058933905015/100 |
| Open Issues | 14 |
| Last Updated | 2026-09-07 |
| Created | 2025-11-12 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~17k |
These tools work well together with ratel for enhanced workflows:
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ratel is Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.. It is categorized as a MCP Server with 434 GitHub stars.
ratel is primarily written in TypeScript. It covers topics such as accuracy, agents, claude-skills.
You can find installation instructions and usage details in the ratel GitHub repository at github.com/ratel-ai/ratel. The project has 434 stars and 21 forks, indicating an active community.
ratel is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ratel on Agent Skills Hub include mcp-memory-service, mcp, pg-aiguide. 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: