vault-operator: Real AI agent for your vault

by pssah4 · MCP Server · ★ 249

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install vault-operator

git clone https://github.com/pssah4/vault-operator.git

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

Security audit verdict: SAFE · quality 67/100

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.

aiautomationknowledge-managementllm-wikimemoryobsidian-pluginsecond-brain

Quick Facts

Stars249
Forks30
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score67/100
Open Issues13
Last Updated2026-09-15
Created2026-02-19
Platformsmcp, node
Est. Tokens~16k

About vault-operator

Vault Operator An autonomous AI agent inside your Obsidian vault. You describe a task, it plans, searches, reads, writes, and reports back. Every action is visible. Every write needs your approval. Every change is undoable in one click. Free. Open source. Local-first. Works with cloud models, with your existing ChatGPT or Copilot subscription, or fully offline with Ollama or LM Studio.

Compatible Skills

These tools work well together with vault-operator for enhanced workflows:

  • agentfiles — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • hypernovum — semantic(0.23)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • obsidian-ai-tagger — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)

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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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