failproofai: Observability and enforcement for AI agent harnesses

How to install failproofai

npm install -g failproofai

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

Security audit verdict: SAFE · quality 64/100

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

About failproofai

Translations: 简体中文 · 日本語 · 한국어 · Español · Português · Deutsch · Français · Русский · हिन्दी · Türkçe · Tiếng Việt · [Italiano](./docs/i18n/README.it.m

Compatible Skills

These tools work well together with failproofai for enhanced workflows:

• failproofai — semantic(1.00)+rare_topics+same_lang+similar_pop+shared_platform (70%)
• octogent — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
• pi-mcp-adapter — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (59%)
• claw-orchestrator — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (57%)
• code-container — semantic(0.20)+complementary+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:

• SlowMist agent-security taxonomy
• Liu et al., An Empirical Study of Agent Skill Security (2026)
• Our census dataset (DOI 10.5281/zenodo.21292799) · Hugging Face mirror
• How we grade: all 42 rules, the source tiers and known limits
• Full methodology & who runs this