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 handling-malware-samples-safelygit 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/handling-malware-samples-safely)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/handling-malware-samples-safely"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/handling-malware-samples-safely/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/handling-malware-samples-safely"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/handling-malware-samples-safely.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 | $0.00067 | $0.01039 |
| Opus 5 | $0.00034 | $0.00519 |
| Sonnet 5 | $0.00013 | $0.00208 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
handling-malware-samples-safely 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 5d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Handling Malware Samples Safely
When to Use
- You are receiving, storing, or sharing a malicious file and need to prevent accidental execution or contamination.
- You need a consistent naming and identity scheme so a sample can be tracked across tools and tickets.
- You are packaging a sample to send to another analyst or a sandbox.
Do not use these conventions as a substitute for an isolated detonation environment; safe storage prevents accidents but analysis still requires an isolated lab.
Prerequisites
- An archive tool that supports password-protected, encrypted archives (7-Zip).
- A hashing utility (
sha256sum,certutil, or the bundled script). - A defined storage location with restricted access, separate from general file shares.
Safety & Handling
- Treat every sample as live. Never double-click; never leave it with its original executable extension on a working machine.
- Store samples inside an encrypted, password-protected archive with the conventional
password
infected. - Neutralize the extension on disk (e.g.
sample.exe→sample.exe.binorsample.exe_) so the OS will not execute it on a stray click. - Defang indicators in any text leaving the lab:
hxxp://,1.2.3[.]4,evil[.]com.
Workflow
Step 1: Identify the sample by hash
A sample's identity is its SHA-256, not its filename. Compute hashes immediately:
python scripts/analyst.py hash sample.exe
# md5, sha1, sha256, size, ssdeep-like size bucket
Rename the working copy to its SHA-256 so identity travels with the file:
mv sample.exe 9f86d0818...<sha256>.bin
Step 2: Store in an encrypted archive
7z a -p"infected" -mhe=on <sha256>.7z <sha256>.bin
# -mhe=on encrypts filenames too
Keep the loose, neutralized copy only inside the lab; the archive is the storage/transfer form.
Step 3: Record provenance
Capture where the sample came from, when, and who handled it. The script can emit a JSON metadata record:
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.
- 5d ago First seen · 123 lines · 67 tokens per session scan A dbebd014db63
handling-malware-samples-safely is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,039 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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