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Reverse Engineer Anything

CommunityPopular
morluto
reverse-engineer-anything

Reverse engineer native, managed, Electron/JavaScript, packaged, and browser applications with REA. Use shipped-artifact or approved runtime evidence to explain features, compare versions, decompile code, or guide a reconstruction. Skip REA for ordinary source-repository architecture analysis.

Overview

Publishermorluto
Repositoryrea
Skill namereverse-engineer-anything
Stars
3.1K
Forks
320
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by morluto on GitHub. Read the source before you install it.

Installation

Install the Reverse Engineer Anything AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/morluto/rea/tree/main/skills/reverse-engineer-anything
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/morluto/rea.git /tmp/rea
mkdir -p .claude/skills
cp -r /tmp/rea/skills/reverse-engineer-anything .claude/skills/reverse-engineer-anything
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reverse Engineer Anything in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reverse Engineer Anything on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reverse Engineer Anything is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

REA

Use REA when a claim depends on a shipped binary or package, decompilation, passive application runtime evidence, controlled replay, or comparison with behavior not established by available source. For ordinary analysis of a complete source repository, use normal repository tools and do not run REA readiness or provider commands.

Route the target first

Choose the first tool from the target the user supplied. Do not call open_binary unless the target is native or an analysis database.

  • ASAR or extracted JavaScript/Electron tree: analyze_javascript_application.
  • Archive, application package, ZIP/APK/IPA/MSIX/AppX, or DMG: open_binary with the supplied local path; use inspect_artifact when its graph and findings help answer the question.
  • Managed PE/CLI assembly: inspect_managed_artifact.
  • User-owned browser page already open: list_browser_targets.
  • User-owned Electron runtime already open: list_electron_targets.
  • Native executable, library, or analysis database: open_binary, then use focused analysis tools directly; call binary_overview when metadata or inventory context is useful.

If the app is missing, ask which app to inspect. Resolve a human-readable app name to one clear installed artifact when possible; ask only when matches are ambiguous. Never choose an example app on the user's behalf.

In a target-free session, use open_binary to bind any archive/package or native target whose analysis tool operates on the active target. Do not call a tool hidden from tools/list; inspect binary_session with detail: "capabilities" for the exact remediation when a desired capability is unavailable.

Work summary-first

Start with the default result and use its inline Evidence and graph context. Do not repeat an identical tool call. Make a focused follow-up only when the returned result leaves a specific question unanswered.

Every conclusion must distinguish observations, inferences, and unknowns. Cite Evidence IDs, preserve limitations and incomplete coverage, and never imply that static analysis observed execution. Ask for approval only where a tool or policy requires it; approval never broadens a different authority boundary.

Plan broader investigations

For requests that span multiple features or subsystems, use a staged workflow:

  1. Turn the request into a checklist of questions and the evidence each answer needs. Resolve target identity and constraints from the conversation and workspace before asking for information again.
  2. Inspect the current REA session, artifact identity, saved analysis database, bookmarks, and prior evidence. Reuse matching state; do not open duplicate sessions or repeat identical analysis.
  3. Start with the smallest useful overview or inventory. Follow each question from its entry point through relevant data and state changes to its result. Batch related operations around a specific hypothesis, then expand only when the returned evidence leaves a concrete gap.
  4. Inspect relevant packaged resources and configuration alongside code when they affect the question. Use format-aware inventory and parsers; do not infer behavior from filenames, strings, or layout alone.
  5. Corroborate a conclusion with the evidence type it requires. Use runtime observation or controlled replay only when static evidence cannot answer the question and the required authority is available.
  6. Decompose work into independent questions. When parallel workers are available and the questions do not depend on one another, assign distinct scopes, point workers to existing evidence, and ask them to return sources, conclusions, and unresolved gaps. Otherwise, work sequentially.
  7. Keep a concise finding ledger linking each conclusion to Evidence IDs, confidence/evidence type, search boundary, and remaining unknowns. Update the shared index or investigation report so later passes can reuse results.

Before finishing, revisit the original checklist. Mark each question as answered, partially answered, or unresolved based on its evidence; keep bounded negative searches bounded, and do not describe a broad investigation as complete while required questions remain open.

Read only the relevant guide

Readiness and setup

The readiness rule is conditional: when REA tools are available and their registration is not known to be stale, proceed directly; do not run doctor before every task. Run npx -y rea-agents@latest doctor when the MCP server or required provider is unavailable, registration is reported stale, or the user asks for an environment diagnosis. Propose npx -y rea-agents@latest setup only when doctor identifies an alignment or provider problem. Show the exact plan and obtain approval before setup writes configuration or installs Hopper. Restart the agent after MCP registration changes; direct CLI commands remain available immediately.

Finish the task

Explain findings in plain language and tie them to returned evidence. When the user asks to build something, use normal coding tools and separate observed behavior from design choices. Close an opened native session with close_binary when the investigation is complete.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Reverse Engineer Anything AI skill do?

Reverse engineer native, managed, Electron/JavaScript, packaged, and browser applications with REA. Use shipped-artifact or approved runtime evidence to explain features, compare versions, decompile code, or guide a reconstruction. Skip REA for ordinary source-repository architecture analysis.

Why use Reverse Engineer Anything on TypingMind?

Because you install it once and use it with any model. Reverse Engineer Anything is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reverse Engineer Anything in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/morluto/rea/tree/main/skills/reverse-engineer-anything. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Reverse Engineer Anything?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reverse Engineer Anything?

As many as you like. As long as a model supports skills, you can use Reverse Engineer Anything with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reverse Engineer Anything AI skill free?

Yes. It is published on GitHub by morluto under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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