🔍 Read the full analysis: Start Exploring Someone Else’s Software With These AI Prompts on ThorstenMeyerAI.com
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TL;DR
A ThorstenMeyerAI.com article published Oct. 19, 2026, explains how to use the open-source Reverse Engineer Anything (REA) toolkit with AI coding agents to investigate existing software. It describes focused prompts, setup checks and ways to test an agent’s answers, while warning that analysis results may be sent to the model provider and that REA is not a security sandbox.
The guide presents REA as a way to connect agents including Claude Code, Codex, Cursor and Gemini CLI to software-analysis tools. It describes three components: a local MCP server that agents can call, a command-line tool for setup and analysis, and an agent skill that lays out an investigation process. MCP is the interface through which an agent can use external tools.
The article recommends starting with one behavior and the decision the developer needs to make. Its example prompt asks an agent to investigate how a program handles a partial invoice payment, identify the stored data and calculation, and describe conditions that change the result. It also advises asking the agent to label findings as observed, inferred or unknown, and to work through a complete example with inputs and outputs.
Before relying on results, the guide says to confirm REA is installed and the agent can actually call its tools. The project’s setup checks files and prerequisites, but the guide says that does not prove a live connection. It also warns that although analysis runs locally, results are passed to the agent and its model provider under that provider’s data policy. The article quotes the project’s warning: “This is not a sandbox.”
Infographic 01 · Reverse Engineer Anything
Five places to point REA first, and how to tell when the answer is wrong.
REA gives coding agents such as Claude Code and Codex tools to inspect programs, apps and browser sessions. Each investigation needs a finishable question and a check.
npx rea-agents@latest setupThe five investigations
What to ask, and the check that catches a wrong answer.
- 1
Ask a question you can finish
One behaviour, one decision it informs. Example: how a partial payment changes an invoice balance.
It’s wrong ifThe agent can't walk through one concrete example with real inputs and outputs. - 2
Get REA connected and confirmed
Run the guided setup, then doctor, then call binary_session inside the agent before any real target.
It’s wrong ifAnswers arrive without a single REA tool call: you're reading the model's guesses. - 3
Turn a business rule into a spec
Inputs, stored fields, formula, edge cases, plus test cases checked against the original's own output.
It’s wrong ifYour version and the original disagree on a test case, even by one cent. - 4
See what has to survive a copy
Find what an app writes to the clipboard and what it keeps locally, then design your own copy and paste.
It’s wrong ifThe explanation doesn't predict what lands in a plain text editor, an email and your app. - 5
Your own app, your equipment, an old program
Start with software you own or rely on. Ask what it sends, stores or calculates when you do one thing.
It’s wrong ifIt only works for the example you watched. Test with a new file or a read-only command first.
Sources: REA on GitHub (stars via GitHub API, 19 Oct 2026); REA installation guide; REA security policy.
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From Software Behavior to Working Specifications
The guide’s practical point is that software behavior matters more than interface resemblance when building a compatible or replacement application. A screen can show an invoice or report without revealing which records are stored, what arithmetic is applied, or how a particular condition changes the result. Those details determine whether an application behaves correctly in real use.
The source uses Ledger Reborn, a local accounting application, to illustrate the potential value of this method. Its author says the application’s profit-and-loss statement, balance sheet and trial balance matched QuickBooks Desktop’s official sample company account by account, to the cent. That is a claim reported by the article, not independent verification presented here. If accurate, the example shows how investigating rules can inform implementation and testing—not merely imitation of a product’s appearance.
For developers, the suggested prompts also set a standard for judging an AI answer: the agent should identify evidence, explain a concrete case and state what it cannot establish. That can help expose unsupported model guesses before they become design decisions. It does not, by itself, guarantee that an analysis is correct or that a rebuilt program will match the original in every situation.
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How REA Connects Agents to Analysis Tools
The source describes REA as an MIT-licensed GitHub project at morluto/rea. As of the article’s October 19 publication, it reported about 325,000 stars and 82,000 forks; it also said version 6.7.0 had been released the previous day. These are figures and release details supplied by the source, rather than independently checked metrics.
REA is presented as supporting a range of targets, including native programs, Electron and JavaScript apps, .NET and Android software, firmware, saved network traffic, WebAssembly, SQLite databases and older DOS code. The tools available depend on the target and setup; native analysis may require software such as Ghidra, Hopper or IDA. For websites, the guide says REA can observe activity through a Chrome-family browser controlled by the user.
The article’s example centers on an investigation of QuickBooks Desktop for Mac 2024. It says the author used REA 6.4.0 and Ghidra to examine inventory calculations, budget signs, reconciliation balances and cash-basis report logic. The source points readers to a published Ledger Reborn case study for the details. This is one reported project, not evidence that the same process will reproduce other applications or cover every edge case.
““This is not a sandbox.””
— Reverse Engineer Anything project, as quoted in the ThorstenMeyerAI.com guide
software reverse engineering toolkit
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Limits, Data Handling and Verification
The source does not provide an independent technical audit of REA, its reported popularity figures, or the Ledger Reborn comparison. It also does not establish how complete or accurate an agent’s findings will be across different programs. The accounting example is attributed to the project’s author and should not be read as a general performance guarantee.
Data handling depends partly on the model provider. The guide says REA analyzes targets locally, but the agent receives analysis results and the provider processes them under its own data policy. It does not specify what information a particular provider retains or how every configuration handles it. Users should check the relevant policies before examining sensitive software or data.
Setup checks have limits, too: according to the article, REA’s doctor command verifies files and prerequisites but not the live agent connection. The guide recommends confirming tool use inside the agent. It remains unclear how well results transfer to proprietary, heavily protected or unusual software, and what additional checks a developer should use for each target.
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Verify the Setup Before Investigating
The guide’s next step for readers is to install REA using its guided setup, review the proposed changes, and check prerequisites such as Node.js 22.19 or newer. After restarting the coding agent, users are advised to run the project’s doctor command and then confirm inside the agent that an REA tool can be called. The article recommends beginning with a harmless application the user owns before moving to a more complex target.
For the investigation itself, the suggested sequence is to choose one behavior, say what decision the answer will inform, and request stored data, calculations and conditions. The agent should then explain one example and mark which parts are observed, inferred or unknown. If it cannot provide a concrete walkthrough—or answers without calling REA—the guide says not to treat the response as an analysis result.
The source does not announce a new release beyond version 6.7.0, nor does it report a planned product milestone. Further developments, including changes to supported agents or analysis tools, will depend on updates from the REA project. Developers considering the workflow should consult its current documentation and security guidance before use.
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Key Questions
What is REA?
Reverse Engineer Anything is an open-source toolkit described in the guide as a local MCP server, command-line tool and agent skill for examining existing software with compatible coding agents.
What kinds of software can REA inspect?
The source lists native applications, Electron and JavaScript apps, .NET and Android software, firmware, saved network traffic, WebAssembly, SQLite databases and DOS code. The required analysis tools vary by target.
Does REA keep analysis data entirely on the user’s computer?
The guide says REA performs analysis locally, but results are sent to the coding agent and the model provider. Their handling is subject to the provider’s data policy, so local analysis does not mean all information stays on the machine.
How can a developer tell whether an AI answer is grounded in REA?
Confirm the agent actually called an REA tool, ask for a concrete example with inputs and outputs, and have it label findings as observed, inferred or unknown. The source says REA’s doctor command alone does not test the live agent connection.
Source: ThorstenMeyerAI.com
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