by ucsandman · MCP Server · ★ 314
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm i -g @dashclaw/cli
npm install dashclaw # Node 18+Taken 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 | 314 |
| Forks | 52 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63/100 |
| Open Issues | 27 |
| Last Updated | 2026-10-31 |
| Created | 2026-02-08 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~20k |
DashClaw Govern AI agents before they act. DashClaw is the governance layer for AI agents that touch real systems. It sits between agents and the world, evaluates policy on every risky action, routes human approval where it is required, records verifiable evidence, and tracks terminal outcomes so a retried agent never silently double-executes.
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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: