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 triaging-an-unknown-samplegit 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/triaging-an-unknown-sample)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/triaging-an-unknown-sample"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/triaging-an-unknown-sample/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/triaging-an-unknown-sample"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/triaging-an-unknown-sample.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 | $0.00064 | $0.00962 |
| Opus 5 | $0.00032 | $0.00481 |
| Sonnet 5 | $0.00013 | $0.00192 |
| Haiku 4.5 | $0.00006 | $0.00096 |
Grade A, and why
triaging-an-unknown-sample 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 9d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Triaging an Unknown Sample
When to Use
- A suspicious file has arrived and you need to quickly decide whether it warrants deep static/dynamic analysis or can be dismissed.
- You want a structured first-pass record (type, hashes, packing, notable strings) before committing analyst time.
- You are batch-triaging many files and need to rank them by suspicion.
Do not use triage results as a final verdict. A clean string review or unknown reputation does not mean benign; it means proceed to deeper analysis.
Prerequisites
file(libmagic) for type identification, or the bundled magic-based detection.- Optional VirusTotal API key for reputation (hash lookups only — never upload someone else's data without authorization).
- The sample in a neutralized, non-executable form inside the lab.
Safety & Handling
- Triage is static: read bytes, never execute. Keep the sample with a neutralized extension.
- Submit only the hash to reputation services unless you have authorization to upload the file itself; uploads can expose sensitive or attributable data.
Workflow
Step 1: Identify the true file type
Do not trust the extension. Identify by magic bytes:
python scripts/analyst.py triage sample.bin
# reports: detected type, magic, hashes, entropy, suspicious strings
A .pdf that is really a PE, or a .jpg that is a script, is itself a finding.
Step 2: Hash and check reputation
Compute the SHA-256 and look it up (hash-only) to see if it is known:
python scripts/analyst.py reputation --sha256 <sha256> --vt-key $VT_API_KEY
Known-bad with many detections → escalate. Unknown → continue triage; absence of detections is not exoneration.
Step 3: Estimate packing via entropy
High, uniform entropy across the whole file or a code section suggests packing or encryption:
entropy ~7.9 / 8.0 over most of the file -> likely packed/encrypted
mixed entropy with readable strings -> likely unpacked
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.
- 9d ago First seen · 120 lines · 64 tokens per session scan A b616173581db
triaging-an-unknown-sample is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 962 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.
Other skills, from other repositories
analyzing-golang-malware-with-ghidra
Reverse engineer Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a…
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF…
analyzing-linux-elf-malware
Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and reverse engineering of x8664 and ARM samples. Use when investigating Linux malware, triaging a suspicious ELF binary…
dynamic-malware-analysis
Execute and analyze malware samples within a highly controlled, instrumented sandbox environment to observe their true behavior, network communications, file system modifications, and registry changes in real-time.
analyzing-malicious-pdf-with-peepdf
A Chinese-language skill for examining suspicious PDF files with peepdf, pdfid, and pdf-parser. It is intended for static malware analysis, which studies a file without running it.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.