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-arm-binariesgit 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-arm-binaries)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/reverse-engineering-arm-binaries"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/reverse-engineering-arm-binaries.svg" alt="Measured on agentmods" 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 | $0.00069 | $0.00720 |
| Opus 5 | $0.00034 | $0.00360 |
| Sonnet 5 | $0.00014 | $0.00144 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
reverse-engineering-arm-binaries 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 4d 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.
What it actually says
Reverse Engineering ARM Binaries
When to Use
- You have an ARM (32-bit) or AArch64 (64-bit) binary — IoT/mobile/Linux malware — and need to identify the architecture, instruction-set state, and entry point before disassembly.
- You need to handle ARM/Thumb interworking correctly.
Do not use an x86 mindset for calling conventions/registers — ARM differs. This skill reads the binary statically and executes nothing.
Prerequisites
- The ARM binary (ELF or Mach-O), read inertly. Capstone optional for instruction decoding.
Safety & Handling
- Read bytes statically; analyze on an isolated host (and emulate via QEMU separately if needed).
Workflow
Step 1: Identify architecture and format
python scripts/analyst.py identify sample.bin
Parses ELF/Mach-O headers to report ARM vs AArch64, endianness, entry point, and (for ELF) whether
the entry is Thumb (low bit set in e_entry or $t mapping symbols).
Step 2: Set the correct disassembly mode
Disassemble AArch64 as A64; for 32-bit ARM, switch between ARM and Thumb per the entry/mapping symbols.
Step 3: Orient around the calling convention
Track arguments in r0-r3/x0-x7, return in r0/x0, and syscalls via svc with the syscall
number in r7/x8.
Step 4: Proceed with analysis
Identify functions, strings, and syscalls; pair with emulation if dynamic insight is needed.
Validation
- Architecture (ARM/AArch64) and endianness are read from the header.
- The entry point and ARM/Thumb state are reported.
- Disassembly mode matches the detected state.
Pitfalls
- Missing Thumb state and decoding Thumb as ARM (garbage output).
- Big-endian ARM (rare but real) mis-parsed as little-endian.
- Statically linked musl/uClibc inflating the function set on IoT samples.
References
- See
references/api-reference.mdfor the identifier. - Arm ARM and AAELF references (linked in frontmatter).
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.
- 4d ago First seen · 90 lines · 69 tokens per session scan A e899d74a7619
reverse-engineering-arm-binaries is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 69 tokens to every session and 720 once invoked, about $0.0003 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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