sast-skills: Collection of agent skills that turn your AI coder into a SAST scanner

by utkusen · Agent Tool · ★ 1.3k

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

Security audit verdict: SAFE · quality 68/100

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.

ai-securityclaudeclaude-codesast

Quick Facts

Stars1,337
Forks65
CategoryAgent Tool
LicenseMIT
Quality Score68/100
Open Issues1
Last Updated2026-04-08
Created2026-03-30
Platformsclaude-code
Est. Tokens~405k

About sast-skills

LLM SAST Skills A collection of agent skills that turn your LLM coding assistant into a fully functional SAST scanner to find vulnerabilities in your codebase. Works natively with Claude Code, Codex, Opencode, Cursor and any other assistant that supports agent skills. No third-party tools required. Claude Code with Opus model is recommended.

Compatible Skills

These tools work well together with sast-skills for enhanced workflows:

  • aguara — semantic(0.31)+complementary+rare_topics+similar_pop+shared_platform (55%)
  • agent-security-scanner-mcp — semantic(0.23)+complementary+rare_topics+similar_pop+shared_platform (47%)
  • Auditor — semantic(0.22)+complementary+rare_topics+similar_pop+shared_platform (47%)

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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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