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 hamzabellouch/agent-skills --skill malware-analysis-reversinggit clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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/hamzabellouch/agent-skills/malware-analysis-reversing)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/malware-analysis-reversing"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/malware-analysis-reversing/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/hamzabellouch/agent-skills/malware-analysis-reversing"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/malware-analysis-reversing.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 | $0.00039 | $0.01886 |
| Opus 5 | $0.00019 | $0.00943 |
| Sonnet 5 | $0.00008 | $0.00377 |
| Haiku 4.5 | $0.00004 | $0.00189 |
Grade A, and why
malware-analysis-reversing 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 8d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Malware Analysis & Reverse Engineering Architecture
Overview
This skill provides technical standards and defensive methodologies for malware analysis and reverse engineering. It covers isolated sandbox setup, static binary inspection (PE/ELF headers, imported functions, packed section detection), dynamic behavior monitoring, memory dump forensics, and authoring YARA rules for threat detection and incident response.
1. Malware Analysis Principles & Safety Protocols
- Strict Air-Gapped / Isolated Sandbox: Always perform malware execution and dynamic monitoring inside isolated virtual machines (REMnux, FLARE VM) with network isolation (INetSim / FakeNet-NG) to prevent lateral movement or C2 callbacks to live networks.
- Layered Analysis Methodology:
- Static Triage: File hashing (MD5, SHA256, ssdeep), PE/ELF header analysis, string extraction, and packer detection (PEiD / Detect It Easy).
- Dynamic Triage: API monitoring, registry/filesystem access tracking (ProcMon), network packet capturing (Wireshark).
- Code-Level Disassembly / Decompilation: Reverse engineering control flow and functions using Ghidra or IDA Pro.
- Memory Forensics: Extracting decrypted payloads or process injection artifacts from volatile memory dumps via Volatility 3.
- Automated Indicator Extraction (IOCs): Extract high-confidence Indicators of Compromise (IP addresses, domain names, file hashes, registry run keys, mutexes) for SIEM ingest.
- Defensive YARA Rule Generation: Write precise YARA detection rules targeting unique string signatures and byte patterns while minimizing false positives.
2. Reverse Engineering & Analysis Pipeline
[ Suspicious Binary Sample ]
│
├──▶ [ 1. Static Analysis ] ──(Hashes, PE Headers, Import Table, Strings)
│
├──▶ [ 2. Dynamic Sandbox ] ──(Process Trees, File/Registry Mutations, PCAP)
│
├──▶ [ 3. Code Reversing ] ───(Ghidra Decompiler, Disassembly Control Flow)
│
└──▶ [ 4. Memory Forensics ] ─(Volatility 3 Dump, Extracted Payloads)
│
▼
[ Threat Intelligence & YARA Detection Rules ]
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.
- 8d ago First seen · 176 lines · 39 tokens per session scan A 6cbdca86b0f3
malware-analysis-reversing is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 1,886 once invoked, about $0.0002 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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