r0crawl_skills: Massive reverse-engineering skill arsenal for web, Android, iOS, Frida, native binaries, protocols, malware

by manyuegong33 · AI Tool · ★ 326

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

Security audit verdict: SAFE · quality 58/100

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

Stars326
Forks116
LanguageHTML
CategoryAI Tool
Quality Score58/100
Open Issues1
Last Updated2026-09-20
Created2026-08-06
Platformsbrowser
Est. Tokens~16k

About r0crawl_skills

r0crawlskills 一套面向 Codex 风格 AI 代理的、对新手友好的全谱系逆向工程技能合集。 r0crawlskills 是一个"路由器 + 大量专用模块"的技能库。它对新手从简单入口开始，再按目标类型路由到更深的工作流：Web 逆向、Android 脱壳、Frida 追踪、原生二进制分析、协议还原、恶意软件分诊、固件分析、CTF 任务、可复现一致性测试。 亮点 196 个专用逆向工程模块 默认新手模式 全中文说明 证据优先的案件工作流 UTF-8 干净 Markdown 文件 覆盖 Web、Android、iOS、原生、Frida、脱壳、反分析、协议、恶意软件、固件、CTF、模糊测试、云/API、游戏逆向 快速开始 把本目录复制到 Codex 技能目录： 然后直接问： 新手工作流 r0crawlskills 从四个问题开始： 我们要分析什么？ 哪个动作最关键？...

Compatible Skills

These tools work well together with r0crawl_skills for enhanced workflows:

  • disrobe — semantic(0.24)+complementary+shared_fw(react)+similar_pop+shared_platform (56%)
  • open-claude-in-chrome — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)
  • claude-skill-web-clone — semantic(0.19)+complementary+shared_fw(react)+similar_pop+shared_platform (55%)
  • website-rebuild-skill — semantic(0.17)+complementary+shared_fw(react)+similar_pop+shared_platform (54%)
  • kuri — semantic(0.16)+complementary+shared_fw(react)+similar_pop+shared_platform (54%)

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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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