reverse-engineer-anything
An AI agent skill from morluto/rea, scored 56 out of 100 by code.
Code score by group
It loses the most points in Description (15), the group that checks whether the description says what the skill does and when to use it.
What the author says it does
Reverse engineer native, managed, Electron/JavaScript, packaged, firmware, and browser targets with REA, and build agent environments (AgentEnv bundles) of web applications from a live instance or a capture, and of Linux desktop apps with a task from a request. Use shipped-artifact or requested runtime evidence to explain features, compare versions, decompile code, or guide a reconstruction. Skip REA for ordinary source-repository architecture analysis.
This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.
- Repository
- morluto/rea
- Folder
- .agents/skills/reverse-engineer-anything
- License
- MIT
- GitHub stars
- 453,855
- SKILL.md lines
- 609
- Other files
- 8
Add reverse-engineer-anything to your agent
npx skills add morluto/rea --skill reverse-engineer-anything
The command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored.
More skills from morluto/rea
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rea-changelog-update
Prepare or rewrite REA release changelogs and GitHub release notes from pinned Git history, with verified contributor thanks and Release Please synchronization.
59C -
rea-analysis-optimization
Optimize REA providers and shared analysis workflows for agent context, latency, CPU, and memory while preserving correctness and Evidence. Use for analysis bottlenecks and cache…
57C -
rea-ci-tests-audit
Audit REA CI and test suites for correctness, fixture drift, false passes, and measured performance; use for suite or workflow investigations and efficiency changes, rather than o…
54D -
rea-tool-design
Audit, investigate, design, or change REA investigation tools, CLI/MCP contracts, provider capabilities, and Evidence semantics.
49D
Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.