iOS-reverse-engineering-claude-skill: Claude Code skill that enables Claude to extract, analyze, reverse engineer iOS apps

by incogbyte · Claude Skill · ★ 86

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

Security audit verdict: SAFE · quality 74/100

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-agentsclaudeios-appreverse-engineeringsecurity

Quick Facts

Stars86
Forks18
LanguageShell
CategoryClaude Skill
LicenseUnlicense
Quality Score74/100
Last Updated2026-07-01
Created2026-03-28
Platformsclaude-code, cli
Est. Tokens~11k

About iOS-reverse-engineering-claude-skill

iOS Reverse Engineering Skill for Claude Code A comprehensive Claude Code skill that enables Claude to extract, analyze, and reverse engineer iOS applications. It processes IPA files, .app bundles, Mach-O binaries, dynamic libraries, and frameworks — producing structured documentation of APIs, security findings, embedded secrets, SDK inventories, and protection assessments.

Compatible Skills

These tools work well together with iOS-reverse-engineering-claude-skill for enhanced workflows:

  • ios-marketing-capture — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)
  • ContextKit — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (53%)
  • xc-mcp — semantic(0.19)+complementary+rare_topics+similar_pop+shared_platform (51%)

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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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