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 analyzing-elf-binaries-on-linuxgit 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/analyzing-elf-binaries-on-linux)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-elf-binaries-on-linux"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-elf-binaries-on-linux/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/analyzing-elf-binaries-on-linux"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-elf-binaries-on-linux.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 | $0.00059 | $0.00800 |
| Opus 5 | $0.00030 | $0.00400 |
| Sonnet 5 | $0.00012 | $0.00160 |
| Haiku 4.5 | $0.00006 | $0.00080 |
Grade A, and why
analyzing-elf-binaries-on-linux 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 10d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing ELF Binaries on Linux
When to Use
- You have a Linux executable or shared object and need to assess it statically.
- You want to read the ELF header, segments, dynamic symbols, and strings to infer behavior.
- You are checking for static linking, stripped symbols, or packing.
Do not use symbol absence as proof of nothing — stripped or statically linked Go/Rust binaries hide structure; switch to disassembly when static metadata is thin.
Prerequisites
readelf/nm/strings(binutils) orpyelftools(pip install pyelftools).- The sample in neutralized form inside the lab.
Safety & Handling
- Static only: parse the file; do not set it executable or run it.
Workflow
Step 1: Read the ELF header
python scripts/analyst.py analyze sample.elf
Note class (ELF32/64), endianness, type (EXEC/DYN/REL), machine (x86-64, ARM, MIPS), and whether it is stripped.
Step 2: Examine segments and section permissions
Writable+executable segments, or a single large segment, suggest packing or self-modifying
code. Compare PT_LOAD permissions against expectations.
Step 3: Inspect dynamic symbols and needed libraries
Imported functions hint at capability: socket/connect (network), ptrace (anti-debug or
injection), fork/execve (process control), crypt/EVP_* (encryption).
Step 4: Detect static linking and packing
Static binaries lack a dynamic symbol table and NEEDED entries. High whole-file entropy and
a tiny section table suggest a packer (e.g. UPX leaves UPX! markers).
Step 5: Skim strings and constructors
Check .init_array/constructors (code before main), and strings for paths, URLs, and shell
commands.
Validation
- Header
typeand segment permissions are consistent with the inferred behavior. - Imported symbols correspond to plausible capabilities.
- Packing call (entropy + missing sections) matches the strings observed.
Pitfalls
- Assuming dynamic symbols are complete — they only cover imported/exported names, not internal functions.
- Treating a stripped Go binary as "empty"; its structure lives in runtime metadata, not the symbol table.
- Ignoring constructors/
.init_array, which run beforemain.
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.
- 10d ago First seen · 100 lines · 59 tokens per session scan A 6e61bd93d475
analyzing-elf-binaries-on-linux is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 800 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 2026-08-30.
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