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 jayminwest · Agent Tool · ★ 335
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mulch safe to install? View the security audit →
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. Install Or try without installing: Development Quick Start bash ml init # Create .mulch/ in your project ml add database # Add a domain ml record database --type convention "Use WAL mode for SQLite" ml record database --type failure \ --description "VACUUM inside a transaction causes silent corruption" \ --reso
| Stars | 335 |
| Forks | 42 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 75.1416964827792/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-02 |
| Created | 2026-02-10 |
| Platforms | claude-code, node |
| Est. Tokens | ~21k |
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mulch is Growing Expertise for Coding Agents — structured expertise files that accumulate over time, live in git, work with any agent. It is categorized as a Agent Tool with 335 GitHub stars.
mulch is primarily written in TypeScript. It covers topics such as ai-memory, ai-tools, claude-code.
You can find installation instructions and usage details in the mulch GitHub repository at github.com/jayminwest/mulch. The project has 335 stars and 42 forks, indicating an active community.
mulch is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mulch on Agent Skills Hub include task-orchestrator, openclaw-config, nocturne_memory. 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: