cerberus-re-skill: cerberus-re is a local Apple-focused reverse-engineering workbench for building a repeatable three-headed

by OwenPawl · AI Tool · ★ 219

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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.

Quick Facts

Stars219
Forks21
LanguagePython
CategoryAI Tool
LicenseApache-2.0
Quality Score67/100
Open Issues1
Last Updated2026-08-30
Created2026-03-31
Platformspython
Est. Tokens~15k

About cerberus-re-skill

Cerberus RE is a local Apple-focused reverse-engineering workbench for building a repeatable three-headed static/dynamic/instrumentation loop around Ghidra, LLDB, and Frida. It is designed for analysts and coding agents that need durable artifacts instead of terminal-only notes: Ghidra import, headless export, decompilation, xrefs, and metadata recovery.

Compatible Skills

These tools work well together with cerberus-re-skill for enhanced workflows:

  • ai-reverse-engineering — semantic(0.47)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (75%)
  • auto-re-agent — semantic(0.37)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (71%)
  • OGhidra — semantic(0.33)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (70%)
  • ablation — semantic(0.32)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (69%)
  • kernagent — semantic(0.28)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (68%)

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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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