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 hashing-and-fingerprinting-filesgit 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/hashing-and-fingerprinting-files)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/hashing-and-fingerprinting-files"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hashing-and-fingerprinting-files/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/hashing-and-fingerprinting-files"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hashing-and-fingerprinting-files.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 | $0.00080 | $0.00741 |
| Opus 5 | $0.00040 | $0.00370 |
| Sonnet 5 | $0.00016 | $0.00148 |
| Haiku 4.5 | $0.00008 | $0.00074 |
Grade A, and why
hashing-and-fingerprinting-files 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hashing and Fingerprinting Files
When to Use
- You need a stable identity for a sample to look up, deduplicate, or share as an IOC.
- You want to cluster related variants that differ slightly using fuzzy/similarity hashes.
- You are computing imphash or section hashes to pivot across a malware family.
Do not use cryptographic hashes alone to judge similarity — a single byte change yields a completely different SHA-256; use fuzzy/import hashing for relatedness.
Prerequisites
- Python with
hashlib(stdlib); optionallyssdeep/python-tlshfor fuzzy hashes andpefilefor imphash. - Samples handled in the isolated lab per the safe-handling skill.
Safety & Handling
- Treat every sample as live: never execute it during hashing; operate on the inert file only.
- Store samples encrypted/password-protected and reference them by hash, not by original name.
Workflow
Step 1: Compute cryptographic hashes
Generate MD5, SHA-1, and SHA-256. SHA-256 is the canonical identity for sharing; MD5/SHA-1 aid lookups in legacy feeds.
python scripts/analyst.py hash sample.bin
Step 2: Compute structural hashes (PE)
For PE files, compute imphash (hash of the import table) and per-section hashes to pivot across samples built from the same toolchain.
Step 3: Compute fuzzy/similarity hashes
Generate ssdeep and/or TLSH digests so near-duplicates can be matched even when bytes differ.
Step 4: Record and cross-reference
Store all digests with the sample metadata; query threat-intel feeds by SHA-256 and cluster by imphash/fuzzy hash.
Validation
- The same input always yields identical cryptographic hashes (deterministic).
- Imphash matches across known-related samples; fuzzy hashes score high similarity for variants.
- Digests are recorded alongside sample metadata for later pivoting.
Pitfalls
- Treating MD5 collisions as identity proof; use SHA-256 as canonical.
- Assuming different cryptographic hashes mean unrelated files; check fuzzy/import hashes.
- Computing imphash on packed samples (imports are stubbed) and over-trusting the result.
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 · 91 lines · 80 tokens per session scan A ad541fe9c665
hashing-and-fingerprinting-files is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 741 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 2026-09-03.
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