REx-skill: REx@Skill - Agentic Reverse Engineering eXecution Skill for binary vulnerability discovery

by tihanyin · Agent Tool · ★ 111

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

How to install REx-skill

git clone https://github.com/tihanyin/REx-skill && cd REx-skill
git clone https://github.com/tihanyin/REx-skill 2>/dev/null || git -C REx-skill pull

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

Security audit verdict: SAFE · quality 61/100

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

Stars111
Forks17
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score61/100
Last Updated2026-09-23
Created2026-09-23
Platformspython
Est. Tokens~18k

About REx-skill

REx@Skill Reverse Engineering eXecution Skill Find the defects in a compiled binary — and prove them. Seven subagents, one evidence tree, and a pinned toolchain so two runs are comparable. <img src="images/tools/capa.png" width="38"

Compatible Skills

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

  • apktool-mcp-server — semantic(0.39)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (72%)
  • spectral — semantic(0.39)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (72%)
  • MCPwner — semantic(0.33)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (70%)
  • MCPwner — semantic(0.33)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (70%)
  • ghidra-re-skill — semantic(0.27)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (67%)

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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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