enzyme-skill: Agent Skill for exploring Obsidian vaults with Enzyme — self-contained, cross-agent compatible

by byenzyme · Agent Tool · ★ 65

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How to install enzyme-skill

git clone https://github.com/useenzyme/enzyme-skill.git ~/.hermes/enzyme-skill-repo
git clone https://github.com/useenzyme/enzyme-skill.git ~/.openclaw/skills/enzyme

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 62/100

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.

Quick Facts

Stars65
Forks3
LanguagePython
CategoryAgent Tool
Quality Score62/100
Open Issues1
Last Updated2026-10-19
Created2026-03-08
Platformspython
Est. Tokens~17k

About enzyme-skill

Enzyme — Compile-time semantic memory for agent workspaces Enzyme is a Hermes plugin and OpenClaw skill that gives agents structural memory over Obsidian vaults, markdown knowledge bases, and any accumulated document corpus. It compiles your workspace into a concept graph with 8ms local semantic search — before the conversation starts. Every agent session starts from scratch.

Compatible Skills

These tools work well together with enzyme-skill for enhanced workflows:

  • magic-slide — semantic(0.30)+complementary+same_lang+similar_pop+shared_platform (60%)
  • yaml-to-html-skill — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (59%)
  • agent-skills — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • agent-session-resume — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (58%)

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How this security grade is produced

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:

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