Reverse Engineer Anything: 5 Use Cases for GitHub's #1 Trending Skill

0:00The #1 trending repo on GitHub lets Claude reverse-engineer any app or website in a way it couldn't before. Most people use it for one thing — copying a feature from another app — but AI LABS tried several and found five genuinely useful ways. Two stand out in their telling: one caught a pricing bug that reading the code alone would have missed (critical enough to cost customers), and one surfaced the mistakes agents make that put a whole app at risk.

What REA is

0:53Reverse Engineer Anything (REA) is a tool for agents like Claude Code and Codex: an MCP server you connect to any agent, with roughly 170 tools, each handling a different part of the process. The gap it fills is behavior: Claude can already open a site and screenshot it, and it gets the look right — but it usually gets the behavior wrong (what happens on click, drag, delete). A normal browser tool sees what appears when you click; REA follows that click back to the exact piece of code that sends the request to the server, captures what was sent and returned at every step, and turns it into a written explanation. It can also reverse-engineer some desktop apps — apps built like websites work the same way, and apps that keep program files locally can be read directly — though it can't do much with apps that load everything from the internet.

Seen / guessed / unknown

2:09The design detail worth knowing: every finding comes with proof. Each captured request or executed code snippet is saved as evidence with its own ID, and the skill makes Claude tie every finding back to that evidence — labeling each one seen (backed by something REA captured), guessed (worked out from clues), or unknown (nothing backing it). A guess can't pass as a fact, because every claim has to show where it came from. The honest limit: that proof only covers what reaches the browser — anything on the company's own servers stays hidden from REA.

Setup

3:27Install by copying the command from the GitHub repo. It detects your coding tools, lets you pick which to install for (arrow keys), and optionally offers Hopper for binary analysis (needed only for apps that aren't built like websites). The command installs the reverse-engineering skill into your main Claude folder — not a single project — so every Claude Code session can use it, along with references on handling different app types. (Sponsor break at ~6:25 — Firecrawl, the "context API for AI agents.")

Use case 1 — copy a feature you like

4:24The popular one, done properly. Their demo: an Obsidian/Notion-style app that lacked a graph view. They had Claude use REA on Obsidian (no MCP connected, so it read the app's code directly) to learn how it builds its graph, and on Notion (MCP connected, so it went through that) for its board view — then write a behavior list file. The list captured details screenshots never show: how Obsidian decides where each node goes, how it draws lines, how strongly linked pages pull toward each other, and what happens when you drag a node. It also split what REA actually saw from what it couldn't pin down — and caught a subtle one: Obsidian leaves broken links behind when you delete a note, while their app turned them back into plain text. That's the kind of behavioral difference that "changes how the graph feels to use."

Use case 2 — research all competitors at once

7:22Tell Claude what you're building, have it list competitors, pick the top five, and run REA on each in depth. Their A/B: without REA, the research on a demo meeting-notetaker covered the basics (pricing, how each records meetings). With REA, it dug through the code each competitor's sign-in page loads before login — and found features the websites never mention. Those deeper insights feed your own product plan.

Use case 3 — find bugs in your own app

8:13Run REA on your own feature with a key constraint: tell it not to read the code — work everything out from the live site. It writes test scenarios to a folder and runs them one by one (headless by default; ask it to turn that off if you want to watch). Their result was the pricing bug: prices shown on screen didn't match prices charged. The pricing code itself looked fine, so code review would never have found it — REA found it by checking the billing page against their pricing rules. Most accounts were priced correctly, but two saw one price and got charged another (one half the real total, one zero). It traced the bug to the very last step where the total is added up, and it also caught smaller issues — the server accepting a negative number of seats, overselling a plan, and an account leaving no billing-log record. And where it couldn't prove something, it said so: it guessed a settings list was causing the two wrong prices, and marked it as a guess, not a fact.

Use case 4 — performance checkup

10:19Ask the agent to run the whole app and check speed, what slows the site down, broken links, and overall behavior — it writes and runs the same kind of browser tests across the whole site and returns a report. Their demo found a real mobile issue: the dashboard header ran past the edge of the screen on phones, which it then fixed. Slow pages and broken layouts are why people leave an app, so this is the recurring check.

Use case 5 — security audit before you ship

10:55The one that matters most before publishing: AI-built apps often leak things that should never be public, and nothing on screen tells you. Ask Claude to run the app and use REA to check for exposed keys and passwords. Their test app had deliberate issues: REA found the easy ones (OpenAI and Stripe API keys accessible without much effort) and a harder logic issue that let people access customer data without backend access. The report sorts findings by danger and gives a fix for each.

Claims checked

Sources & further reading