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REA-Argonaut

Zero-G Reverse Engineering Anything & Verified Safe Rust Reconstruction for Gemini 4 Argon + Google Antigravity 2.0 (4-Mount Recon Kits, Whole-Subgraph Extraction, 10,000-Vector Differential Parity Referee) -- (Actively maintained / Rolling updates)

Install / Use

claude mcp add PNWcoastie -- npx -y github:PNWcoastie/REA-Argonaut

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

84/100

Category

Security

Supported Platforms

Claude Code
Claude Desktop
Gemini CLI

Our assessment of REA-Argonaut

REA-Argonaut scores 84/100 on our quality scale, 853rd of 1,118 Security skills we index.

Its MCP Server is 14 KB long, well organised into 21 sections with 5 code examples: a thorough specification that gives an agent plenty to work with.

It has 10 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
4/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated today, so REA-Argonaut is actively maintained.
  • Our last check on 2026-10-18 found the source still online.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 97/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-10-18. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

REA-Argonaut compared with similar skills

All 4 of these similar skills score higher than REA-Argonaut; compare them before choosing.

SkillScoreStarsUpdatedFormat
REA-Argonaut (this skill)by PNWcoastie8410todayMCP Server
Agent-Reachby Panniantong10098.9k3d agoCLAUDE.md
headroomby headroomlabs-ai10075.7ktodayCLAUDE.md
CowAgentby zhayujie10047.4ktodayCLAUDE.md
Scraplingby D4Vinci10088.4ktodayMCP Server

Frequently asked questions

How do I install REA-Argonaut?
Run claude mcp add PNWcoastie -- npx -y github:PNWcoastie/REA-Argonaut. The install tabs above show the steps for each supported agent.
Which AI agents does REA-Argonaut work with?
It is written for Claude Code, Claude Desktop and Gemini CLI, as a MCP Server file. Other agents that read the same format can often use it too.
Is REA-Argonaut safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 97/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is REA-Argonaut still maintained?
The repository was last updated today, so REA-Argonaut is actively maintained.

REA-Argonaut (ArgonZeroG)

Status Last Commit

Zero-G Reverse Engineering Anything & Sandboxed Closed-Loop Rust Parity Verifier for Gemini 4 Argon + Google Antigravity 2.0

REA-Argonaut (package: rea-argonaut, CLI: argonaut, MCP server: rea-argonaut) is a cross-platform (linux, darwin, win32) reverse-engineering and verified Rust reconstruction suite, engineered as a first-class Google Antigravity 2.0 Plugin and Model Context Protocol (MCP) server.

Built as a full superset of morluto/rea (Reverse Engineer Anything), REA-Argonaut preserves all 35 original REA reverse-engineering domains (187 tools) while introducing the ArgonZeroG architecture and 7 new high-throughput forensic, recon-kit & verification tools (194 total MCP tools across 39 contract domains) designed for long-context frontier reasoning models.

[!IMPORTANT] Active Development & Rolling Updates: REA-Argonaut is continuously updated with new forensic capabilities, architectural refinements, and parity harness enhancements. Check Discussions Announcements or click Watch in the top-right corner for the latest drops.

[!TIP] New to Reverse Engineering or Vibe Coding? Start with the Visual User Guide: Read USER_GUIDE.md — The Zero-to-Hero Visual Field Manual & Decoder Ring for a plain-English walkthrough of the 3-Stage Division of Labor, what each of the 4 Mounts extracts, how to choose between the 4 Engineering Harnesses when Antigravity prompts you via ask_question, and copy-paste starter prompts. REA-Argonaut 3-Stage Division of Labor Map

The Vibe-Coder & Beginner Decoder Ring — 4 Mounts to 4 Harnesses


REA-Argonaut (ArgonZeroG) on AI Agents Listing

Why REA-Argonaut? (ArgonZeroG Architecture)

Legacy MCP reverse-engineering servers were architected for small-context models, enforcing tiny pagination limits (64-character string previews, 50-line pseudocode slices, 20-row table pages) that force frontier agents into hundreds of sequential RPC round-trips and destroy KV context-cache reuse.

REA-Argonaut eliminates token-scarcity bottlenecks across six architectural pillars:

1. decompile_subgraph (ArgonZeroG Subgraph Batch Engine)

  • Single-Call Call-Graph Extraction: Traverses a target binary's call graph (BFS or DFS, cycle-safe up to max_depth: 5 and max_nodes: 256) and returns an entire subsystem's decompiled C pseudocode, disassembly, cross-references (XREFs), referenced string literals (2048-char previews), P-Code inferred struct field offsets (inferred_struct_offsets), and latent binary hazards (latent_hazards) in one deterministic JSON payload.
  • 95%+ Context-Cache Reuse: Emits a topologically ordered, immutable subgraph representation so multi-turn agent reasoning and Rust reconstruction reuse cached KV prefixes instead of fragmenting context across dozens of micro-paginated tool calls.
  • Raised ArgonZeroG Limits Across Canonical Tools:
    • search_strings: max_preview_length raised to 2048 (default: 512), limit raised to 2000 (default: 250).
    • procedure_pseudocode / analyze_function: max_lines raised to 5000 (default: 1000, detail: "full" by default).
    • procedure_disassembly: max_instructions raised to 5000 (default: 1000).
    • list_procedures / search_procedures / search_symbols / binary_overview: limit raised to 2500 (default: 500).

2. export_recon_kit (Universal 4-Mount Recon Kit & Blueprint Exporter)

  • 4-Phase Recon-to-Argon Handoff: Materializes RECON_BLUEPRINT.md and the structured 4-Mount recon-kit/ workspace directory (Mount 1: native-binary, Mount 2: packaged-ui-archive, Mount 3: hardware-firmware-protocol, Mount 4: ai-model-data) in a single execution-free pass (mock_ui_concepts: false by default, producing zero unsolicited HTML files).
  • Native ask_question Harness Routing: Pairs with Antigravity's native ask_question decision control so users choose which subsystem and which of the 4 Harnesses (verified-safe-rust-port, clean-room-app-ui-rebuild, hardware-hal-protocol-replay, security-hazard-audit) to execute.

3. 2-Tier Cross-Platform Native Engine (linux, darwin, win32)

  • Tier 1 (Deep Decompiler Engine — Auto-Detected via provider: "auto"): Works directly with standard desktop installations of Ghidra (11.x and 12.x), IDA Pro (idat / idalib), Hopper Disassembler, and JEB. By default, REA-Argonaut drives their built-in headless runners in the background so the AI agent never blocks on GUI dialogs—while also exporting standard C headers (recon-kit/types/recovered_types.h) that you can import into your interactive Ghidra or IDA Pro desktop GUI (File -> Parse C Source) for side-by-side visual analysis.
  • Tier 2 (ZeroInstallNativeProvider Zero-Install Fallback): Out-of-the-box binary layout, symbol/section discovery, stripped C++ RTTI/VTable slot seeding (ClassName__vslot_N_<addr>), call-graph extraction, and disassembly when Ghidra/Hopper/IDA are not installed:
    • Linux (ELF): GNU binutils (objdump, readelf, nm) + native ELF header parser.
    • macOS (Mach-O): otool, nm, lipo, llvm-objdump + universal fat-binary slice parser.
    • Windows (PE/COFF): llvm-objdump, dumpbin + native PE/COFF header/import/export parser.
    • Python Layout & Crash Bridge (bridge/pwntools/layout_impl.py & recorded_crash_impl.py): Dual-path backend supporting pwntools when installed and a 100% Python stdlib fallback on clean hosts.

4. 100% Vanilla REA Superset (187 Retained Tools) + 7 ArgonZeroG Forensic Verticals (194 Total MCP Tools Across 39 Domains)

In addition to every original REA capability (JavaScript/Web bundles, Source Maps, Chrome DevTools/CDP, Frida, GDB/LLDB/WinDbg, Android APK/DEX, iOS IPA, JVM/JAR, .NET CIL, WebAssembly, Electron ASAR, Mach-O/ELF/PE, Hermes/React Native, Lua, Flash SWF, Archives, PCAP/HAR, EVM/Solidity, Firmware, Symbolic Execution, YARA/Capa), REA-Argonaut adds purpose-built forensic verticals:

  • inspect_rust_cpp_binary — Stripped Rust & Modern C++ Binary Forensics:
    • Recovers crate names and exact versions from .cargo/registry/src/... paths and core::panicking::Location tuples even in strip --strip-all binaries.
    • Native TypeScript Rust _RNv v0 and legacy _ZN symbol demangler + Itanium (_Z) and MSVC (?) C++ demangler.
    • Reconstructs C++ VTables and RTTI (typeinfo) across ELF (SHT_RELA PIE relocation resolution), Mach-O, and PE binaries without launching a heavy decompiler session.
  • inspect_python_frozen_binary — Execution-Free Frozen Python Unpacker:
    • Pure-TypeScript parser for PyInstaller (MEI\x0c\x0b\x0a\x0b\x0e) CArchive trailers, PYZ zlib-compressed embedded module tables, and PEP 552 .pyc code-object marshal streams.
    • Extracts Python version, entry-point scripts, bundled package manifests, co_names, co_consts, and string literals without executing untrusted bytecode.
  • inspect_ai_model_artifact — Zero-Copy Streaming AI Model Forensics:
    • Constant-memory (fs.open + bounded read()) streaming inspector for GGUF (v1–v3), SafeTensors, ONNX, and TFLite artifacts.
    • Extracts architecture hyperparameters, quantization distribution, tensor layouts, and embedded Jinja tokenizer.chat_template / system_prompt strings without loading multi-gigabyte weight buffers into RAM.
  • inspect_engine_archive — Game Engine Metadata & Asset Pack Inspector:
    • Parses Unity IL2CPP global-metadata.dat (0xFAB11BAF, v24–v31) to recover C# namespaces, class/type definitions, method names, and string literals from native-compiled Unity binaries.
    • Parses Godot Engine .pck (GDPC, v1–v3) and Electron .asar archives to enumerate packed .gd / .gdc scripts, .tscn scenes, package.json entrypoints, and native .node addons.

5. Closed-Loop Sandboxed Rust Parity Verifier (verify_rust_parity)

  • Automated Differential FFI Harness: Compiles a C reference implementation (or loads an existing .so / .dylib / .dll via dlopen/dlsym) alongside a candidate #![forbid(unsafe_code)] Rust reconstruction (rustc --crate-type cdylib) and runs 10,000+ SplitMix64 randomized + boundary test vectors (0, 1, -1, MIN, MAX, powers of two, bitmasks) in-process.
  • Deterministic Counterexample Shrinking: Automatically binary-searches any diverging input down to the minimal failing argument tuple (counterexample.shrunk_inputs, bit_diff_summary) so the agent can self-heal its Rust port in a closed loop.
  • Clean Cargo Crate Scaffolding (scaffold_output_dir): Scaffolds a 100% pure #![forbid(unsafe_code)] Rust Cargo crate (Cargo.toml, src/lib.rs, tests/parity_regression.rs) with zero HTML clutter by default (html_report_path / --html-report is strictly opt-in).
  • OS-Native Sandboxed Execution: Runs compiled harnesses inside isolated OS sandboxes:
    • Linux: bwrap (bubblewrap network-isolated namespace) + prlimit CPU/AS/NOFILE caps.
    • macOS: /usr/bin/sandbox-exec ((deny network*) Seatbelt profile) + ulimit.
    • Windows: Restricted Job Objects / timeout-bounded process groups.

6. Hardware-Adaptive Runtime Scaling (AdaptiveResourceProfile)

  • Dynamically inspects os.availableParallelism() and os.totalmem() at startup (src/config/AdaptiveResourceProfile.ts) to scale JavaScript AST worker pools, Headless Ghidra JVM heap (-XX:ActiveProcessorCount, -Xmx), and concurrent subprocess budgets across laptops, CI containers, and 128-core workstations with zero hardcoded CPU limits.

Quickstart

1. Install & Build

npm install
npm run build

2. Run the End-to-End ArgonZeroG Showcase

Compile a multi-function C cryptographic/compression pipeline (argon_cipher_pipeline), strip all symbols, extract the whole call-graph in one decompile_subgraph call, inspect stripped Rust/C++ panics & VTables, stream-inspect a GGUF AI model header, and prove bit-exact C-to-Rust parity across 10,000 FFI test inputs:

npm run showcase

3. CLI Usage (argonaut)

# Check host toolchains and adaptive hardware profile
node dist/cli.js doctor

# Materialize a Universal 4-Mount Recon Kit (RECON_BLUEPRINT.md + recon-kit/)
node dist/cli.js export-recon-kit /path/to/target --output-dir ./recon-output

# Single-call ArgonZeroG call-graph extraction
node dist/cli.js open /path/to/binary --alias target
node dist/cli.js decompile-subgraph --session target --entry 0x401120 --max-depth 3

# Standalone forensic inspectors (no binary session required)
node dist/cli.js inspect-rust-cpp-binary --path /path/to/stripped_rust_or_cpp_bin
node dist/cli.js inspect-python-frozen-binary --path /path/to/pyinstaller_binary
node dist/cli.js inspect-ai-model-artifact --path /path/to/model.gguf
node dist/cli.js inspect-engine-archive --path /path/to/global-metadata.dat

# Closed-loop C vs. Rust FFI differential parity verification
node dist/cli.js verify-rust-parity \
  --target-binary-path /path/to/libtarget.so \
  --symbol-name compute_tag \
  --arg-types u64,u32 \
  --return-type u64 \
  --rust-source-code '#[no_mangle] pub extern "C" fn compute_tag(a: u64, b: u32) -> u64 { ... }' \
  --iterations 10000

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars10
CategorySecurity
Updated19h ago
Forks0

Languages

TypeScript

Trust signals

97/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

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