advocaat: A small, type-safe client for asking AI questions about your data, powered by TypeSafe Jev

by pithings · Agent Tool · ★ 101

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How to install advocaat

npx skills add pithings/advocaat
npx nypm i advocaat

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 65/100

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.

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

Stars101
Forks2
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score65/100
Open Issues1
Last Updated2026-09-18
Created2026-09-16
Platformscli, node
Est. Tokens~17k

About advocaat

❓Advocaat A small, type-safe client for asking AI questions about your data, powered by TypeSafe Jev. Get probabilities, choices, and scores in one request. Agent skill teaches coding agents how to design questions and build with . Quick start Install the package: Set your TypeSafe API key: Using Vercel instead? See Vercel AI Gateway.

Compatible Skills

These tools work well together with advocaat for enhanced workflows:

  • blink — semantic(0.46)+complementary+same_lang+similar_pop+shared_platform (66%)
  • spiceflow — semantic(0.45)+complementary+same_lang+similar_pop+shared_platform (66%)
  • killmyidea — semantic(0.17)+complementary+shared_fw(vercel)+same_lang+similar_pop+shared_platform (64%)
  • jev-review — semantic(0.39)+complementary+same_lang+similar_pop+shared_platform (64%)

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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:

View on GitHub → Browse Agent Tool tools