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 diffing-malware-samples-to-find-changesgit 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/diffing-malware-samples-to-find-changes)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/diffing-malware-samples-to-find-changes"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/diffing-malware-samples-to-find-changes.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.00640 |
| Opus 5 | $0.00030 | $0.00320 |
| Sonnet 5 | $0.00012 | $0.00128 |
| Haiku 4.5 | $0.00006 | $0.00064 |
Grade A, and why
diffing-malware-samples-to-find-changes 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 7d 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.
What it actually says
Diffing Malware Samples to Find Changes
When to Use
- You have two related samples (suspected variants of one family) and want a fast, structural diff before a full BinDiff.
- You need to quantify similarity and pinpoint changed sections/imports for variant tracking.
Do not use this as authoritative function-level diffing — that requires BinDiff/Diaphora on disassembly. This skill does structural/statistical diffing and executes nothing.
Prerequisites
- Two sample files (read inertly). Optional:
ssdeep/tlshfor fuzzy scores (degrade gracefully).
Workflow
Step 1: Structural diff
python scripts/analyst.py diff a.bin b.bin
Compares file size, per-section SHA-256 and entropy, and the import name sets, reporting added/ removed/changed sections and imports.
Step 2: Similarity score
Reports a byte-level similarity ratio and, if available, ssdeep/tlsh fuzzy-hash comparison scores.
Step 3: Prioritize changed regions
Changed sections (same name, different hash) and new imports are the high-value targets for deeper disassembly diffing.
Step 4: Document
Record what changed and the similarity score to support variant/lineage tracking.
Validation
- Section comparison keys on section name; identical sections report equal hashes.
- Import diff lists are accurate (added vs removed vs common).
- Fuzzy-hash scores are reported only when the library is available, else clearly omitted.
Pitfalls
- Recompilation shifting addresses makes raw byte diff noisy — rely on section/import structure.
- Packers making both samples look similar (packed) while the payloads differ — unpack first.
- Treating a high byte-similarity as proof of same author without corroboration.
References
- See
references/api-reference.mdfor the differ. - PE format and ssdeep references (linked in frontmatter).
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.
- 7d ago First seen · 85 lines · 60 tokens per session scan A cc843252d582
diffing-malware-samples-to-find-changes is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 640 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.
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.