tachi: Threat modeling and AI-reasoning vulnerability detection harness for Claude Code — STRIDE + AI + MAESTRO

by davidmatousek · Claude Skill · ★ 95

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How to install tachi

git clone https://github.com/davidmatousek/tachi.git ~/Projects/tachi

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 69/100

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.

agentic-securityai-securityattack-treesclaude-codecybersecuritydevsecopsllm-securitysarifsecuritystride

Quick Facts

Stars95
Forks21
LanguagePython
CategoryClaude Skill
LicenseApache-2.0
Quality Score69/100
Open Issues34
Last Updated2026-10-18
Created2026-03-21
Platformsclaude-code, python
Est. Tokens~20k

About tachi

tachi Threat Modeling and Vulnerability Detection Harness for Claude Code. AI-Reasoning Scanner — STRIDE + AI + MAESTRO. Get started: Quick Start | Developer Guide (full walkthrough with worked examples) OWASP Coverage 50/50 across five frameworks — every catalogued threat in each framework has a tachi detection agent. [ML 202

Compatible Skills

These tools work well together with tachi for enhanced workflows:

  • perseus — semantic(0.32)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • claude-warden — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)

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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 Claude Skill tools