Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add meltedinhex/analyst-ai-pack --skill reverse-engineering-binaries-with-ghidragit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/reverse-engineering-binaries-with-ghidra)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/reverse-engineering-binaries-with-ghidra"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/reverse-engineering-binaries-with-ghidra/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/reverse-engineering-binaries-with-ghidra"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/reverse-engineering-binaries-with-ghidra.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.00886 |
| Opus 5 | $0.00040 | $0.00443 |
| Sonnet 5 | $0.00016 | $0.00177 |
| Haiku 4.5 | $0.00008 | $0.00089 |
Grade A, and why
reverse-engineering-binaries-with-ghidra scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse Engineering Binaries with Ghidra
When to Use
- Static/dynamic triage has identified functionality that needs code-level understanding.
- You need to reverse a C2 protocol, decryption routine, or custom obfuscation.
- You want to extract hardcoded config (C2, keys, campaign IDs) from compiled code, or automate extraction across many samples via headless scripts.
Do not use Ghidra as a first step for unknown samples — triage statically and behaviorally first, and unpack before decompiling a packed binary.
Prerequisites
- Ghidra 11.x with JDK 17+.
- An isolated analysis host.
- Familiarity with x86/x64 (or the target arch) and OS API conventions.
- The sample unpacked (decompiling packed code wastes effort).
Safety & Handling
- Ghidra performs static analysis and does not run the sample, but still work in the lab.
- Treat any dumped/derived payloads as live samples.
Workflow
Step 1: Import and auto-analyze
Create a project, import the binary (Ghidra detects PE/ELF/Mach-O and arch), and run auto-analysis with default analyzers.
Step 2: Navigate to interesting code
Start from imports (Symbol Tree) and strings (Defined Strings). Cross-reference (X) from a
suspicious API or string to its callers to reach the relevant function quickly.
VirtualAlloc/WriteProcessMemory -> injection logic
InternetOpen/WinHttp* -> C2
CryptEncrypt / custom XOR loops -> crypto/config decode
Step 3: Annotate the decompiler output
Retype variables, rename functions, and add comments as you understand the code. Good naming turns the decompiler into readable pseudo-C and compounds across a session.
Step 4: Automate with headless scripts
For repeatable extraction, run Ghidra headless with a post-script. The bundled Python uses
analyzeHeadless to export functions/strings to JSON:
python scripts/analyst.py headless --ghidra /opt/ghidra --bin sample.bin --out out.json
Step 5: Extract the target artifact
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 107 lines · 80 tokens per session scan A 3d3b4e4989b9
reverse-engineering-binaries-with-ghidra is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 886 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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