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 building-a-sample-management-workflowgit 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/building-a-sample-management-workflow)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/building-a-sample-management-workflow"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/building-a-sample-management-workflow/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/building-a-sample-management-workflow"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/building-a-sample-management-workflow.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 | $0.00072 | $0.00784 |
| Opus 5 | $0.00036 | $0.00392 |
| Sonnet 5 | $0.00014 | $0.00157 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
building-a-sample-management-workflow 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building a Sample Management Workflow
When to Use
- You are accumulating malware samples and need a repeatable, safe way to store and find them.
- You need metadata and provenance (source, date, case) attached to every sample.
- You want content-addressed storage so the same sample is never duplicated or misnamed.
Do not use original filenames or loose folders as the organizing principle — names are attacker-controlled and collide; address samples by hash.
Prerequisites
- The hashing skill for content addressing; encrypted/password-protected archive tooling.
- A defined storage location inside the isolated lab.
Safety & Handling
- Store every sample inside a password-protected archive (commonly
infected) so it cannot auto-execute or be scanned/quarantined by host AV. - Strip the executable bit / neutralize the extension at rest; restore only inside the lab.
Workflow
Step 1: Content-address on intake
On receipt, compute SHA-256 and store the sample under a path derived from its hash
(e.g., samples/ab/cd/<sha256>), preventing duplicates and name collisions.
python scripts/analyst.py intake sample.bin --source "phishing-case-42" --store ./samples
Step 2: Record metadata
Write a metadata record per sample: hashes, original name, source, intake date, case ID, file type, and analyst — kept next to the sample or in an index.
Step 3: Archive safely
Wrap the sample in a password-protected archive; keep the inert copy out of host AV's reach and the metadata in plaintext for searchability.
Step 4: Maintain chain of custody
Append-only log each access/action (who, when, what) so the sample's handling is auditable.
Step 5: Index and search
Build a searchable index over metadata so samples can be found by hash, family, source, or case without touching the raw bytes.
Validation
- The same sample always lands at the same hash-derived path (idempotent intake).
- Every sample has a complete metadata record and an access log entry.
- Samples are stored password-protected; metadata is searchable in plaintext.
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 · 95 lines · 72 tokens per session scan A fb84ed3e1781
building-a-sample-management-workflow is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 784 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 2026-08-30.
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-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.
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.
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.