Reverse Engineer Anything

REA is a local reverse-engineering tool for AI agents, distributed as a CLI and MCP server. It connects agents to analysis providers such as Hopper and Ghidra, and can inspect native binaries, macOS applications, JavaScript and Electron applications, .NET assemblies, websites, packages, and controlled runtime behavior. Its investigation workflow decompiles or maps a target, follows strings, symbols, calls, references, and control flow, records evidence and limitations, and uses the findings to help reconstruct features in another project without claiming to recover original source code or automatically clone an application. Analysis runs on the local host; REA configures agent integrations and can optionally install Hopper, while Ghidra and Java must be installed separately. The project is distributed under the MIT license.

Reverse Skill is an open-source, client-neutral cybersecurity skills router for AI coding agents performing reverse engineering, authorized penetration testing, security research, and CTF work. It processes a task through structured routing rules and a primary master-routing workflow, initializes authorization and network scope before action, selects a scenario-specific skill, checks available tools, MCP servers, and scripts, and records a timeline, evidence-to-finding path, report, and field journal rather than relying on guessed commands. Supported scenarios include APK and mobile analysis, binary and .NET reverse engineering, frontend JavaScript and encrypted-parameter analysis, DSL-VM reverse engineering, HTTP capture and request replay, malware and YARA analysis, penetration testing, attack-chain orchestration, case review, CTFs, firmware and IoT security, patch-diff analysis, exploit development, EDR bypass research, API and GraphQL security, supply-chain and SBOM security, and LLM security. Its documented tool ecosystem includes JADX, Apktool, Frida, IDA Pro, radare2, Ghidra, BurpSuite, and YARA. The repository provides platform-specific setup and tool-index refresh scripts for Windows, Linux, macOS, and Kali Linux, with prerequisites including a JDK, Node.js, and Python. It supports Claude Code, Codex, Cursor, OpenCode, and other compatible clients while keeping client adapters separate from the routing core, and includes cross-platform CI, routing regression tests, structure and supply-chain checks, and generated skill-navigation indexes.

decomp.wtf is a locally running AI-assisted game decompilation tool for games the user owns. It accepts ROMs, disc images, or program files; identifies functions and data with tools such as splat, spimdisasm, decomp-toolkit, and ds-decomp; and gives each function to an AI agent with its assembly, related data, call context, look-alike code, and a draft implementation. Each agent attempt is compiled with the game's original or project-specific compiler, linked at the function's original address, and compared byte for byte with the source game. Mismatches return an instruction diff for another attempt, while matches are saved and shared with other agents. The tool also supports parallel helper agents, background permutation of near misses, public decompilation projects, rebuildable exports, and community rooms where participants work from their own copies of the game. It supports Linux and Windows through a WSL 2 distribution; macOS is not supported. GameCube, Wii, and Nintendo 64 are the recommended platforms, while PlayStation, PlayStation 2, Game Boy Advance, and Nintendo DS support is described as beta. The application runs locally without an account, telemetry, or uploading game data, and is released under the MIT license.

Reverify is an AI-agent verification toolkit for reverse engineering and related code analysis. It places deterministic tools between an agent and its claims: the model proposes hypotheses about a binary or candidate implementation, while parsers, disassemblers, pattern scanners, emulators, equivalence checks, and optional analysis engines compare them with ground truth and return evidence-backed VERIFIED, REFUTED, INCONCLUSIVE, OBSERVED, or INVALIDATED results. Its pure-Python core handles PE, ELF, and Mach-O parsing, x86/x64 and ARM/ARM64 disassembly, byte and pattern inspection, CPU micro-emulation, Protobuf/TLV dissection, and Frida hook generation; optional installations add Capstone, Unicorn, LIEF, Z3, or angr for deeper analysis. The project exposes the verification loop through a command-line interface and an MCP server that agents such as Claude Code and Cursor can call. It also records verified, observed, proved, and refuted results in a content-keyed local ledger, allowing grounded facts and negative findings to survive context resets, compaction, and new sessions. A rollover and orchestration system hands sessions off through files rather than model-written summaries, keeping verified facts separate from unverified notes. Reverify includes claim types for bytes, typed reads, instructions, patterns, emulation, behavioral or formal equivalence, imports, exports, sections, and—when angr is installed—functions, calls, references, and reachability. It is distributed from PyPI and licensed under the MIT License. The repository states that it is intended for authorized reverse engineering, malware analysis, CTF work, interoperability research, and software the user owns or is permitted to analyze.

leanrea is an 8 KB skill file for coding agents that guides reverse engineering of compiled applications using ordinary command-line tools instead of an MCP server. It routes targets such as Electron and ASAR files, Mach-O binaries, native disassembly, Go binaries, Android APKs, WebAssembly, SQLite databases, network captures, firmware, and web applications to appropriate first commands; its procedures use strings, imports, extracted files, and call sequences to narrow the investigation. The skill also instructs agents to work on copies, batch shell probes, search hidden extracted directories, and label claims as seen, inferred, or unknown. It can be installed for supported agents with the skills CLI or manually by copying its plain-text SKILL.md file, and is MIT licensed. The repository reports a benchmark in which leanrea matched REA's accuracy with lower wall time, cost, turns, and token usage, while noting that the comparison used one model, one machine, and three repetitions per cell.