by jayminwest · Agent Tool · ★ 335
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
bun install -g @os-eco/mulch-cli
npx @os-eco/mulch-cli --helpTaken 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 | 335 |
| Forks | 43 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 75/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-29 |
| Created | 2026-02-10 |
| Platforms | claude-code, node |
| Est. Tokens | ~21k |
Mulch Structured expertise management for AI agent workflows. Agents start every session from zero. The pattern your agent discovered yesterday is forgotten today. Mulch fixes this: agents call to write learnings, and to read them. Expertise compounds across sessions, domains, and teammates. Mulch is a passive layer. It does not contain an LLM. Agents use Mulch — Mulch does not use agents.
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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: