malware-classification

A method for examining unknown or suspicious program files, identifying malware families, and collecting useful signs of compromise. Malware is software designed to harm systems, steal information, or provide unauthorized access.

In plain words
What is it for?
Use it to triage binaries, classify ransomware, remote-access tools, loaders, information stealers, banking trojans, and botnet software, and extract indicators for detection.
Why use it?
It provides a systematic way to move from an unfamiliar file to a clearer assessment of what it is doing and how defenders can recognize it.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ogrodev/fsociety/malware-classification
Any agent
npx skills add ogrodev/fsociety --skill malware-classification
Clone the repo
git clone --depth 1 https://github.com/ogrodev/fsociety

Made for: Claude Code, Codex.

Per session 449 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,710 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00449 $0.03710
Opus 5 $0.00225 $0.01855
Sonnet 5 $0.00090 $0.00742
Haiku 4.5 $0.00045 $0.00371

Measured 2d ago against content hash dd8d68905ef2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

malware-classification scanned grade A with 1 finding 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -F "file=@<sample>" http://<cape-host>:8000/apiv2/tasks/create/file/
romero/skills/malware-classification/SKILL.md · 366 lines

How it starts

The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Malware Classification

Systematic methodology for triaging unknown binaries, classifying malware families, extracting indicators of compromise, and producing actionable intelligence. Covers the full pipeline from initial sample receipt through final classification report.

Classification Taxonomy

Know what you are looking for. Every sample maps to one of these categories:

Category Subcategories Key Behavioral Signals
Ransomware Crypto-locker, locker, wiper-ransom File enumeration, crypto API calls, ransom note drops, shadow copy deletion
RAT Full RAT, lightweight backdoor Reverse shell, command dispatch, screenshot capture, keylogging, file exfil
Loader/Dropper Stage-1 loader, dropper, downloader Downloads next stage, writes to disk or injects, minimal own functionality
Stealer/Infostealer Browser stealer, credential harvester Reads browser DBs, credential stores, clipboard, crypto wallets
Banker Trojan Web inject, form grabber, overlay Targets banking URLs, injects into browser, form hooking
Botnet Agent DDoS bot, spam bot, proxy bot C2 check-in loop, command polling, peer-to-peer comms
Rootkit Kernel rootkit, userland rootkit, bootkit Driver loading, SSDT hooks, DKOM, MBR/VBR modification
Cryptominer CPU miner, GPU miner High CPU usage, stratum protocol, mining pool connections
Wiper Destructive wiper, MBR wiper Overwrites MBR, mass file deletion, no recovery mechanism
Spyware Keylogger, screen capture, audio capture Input hooks, periodic screenshots, mic access
Adware/PUP Ad injector, browser hijacker Browser extension install, DNS hijack, ad network callbacks

Triage Workflow

Follow this sequence. Each phase feeds the next — do not skip steps.

Phase 1 — Sample Receipt and Hashing

Every analysis begins with positive identification and deduplication.

# Compute all hashes for the sample
sha256sum <sample>
md5sum <sample>
ssdeep <sample>

# Check file type
file <sample>

# Track in romero analysis DB
node ${CLAUDE_PLUGIN_ROOT}/scripts/binary-hasher.js hash <sample>
node ${CLAUDE_PLUGIN_ROOT}/scripts/analysis-tracker.js add <sha256> classification pending

Read the full file on GitHub · 366 lines

Files

What ships with it

5 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.

Changes

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.

  1. 2d ago First seen · 366 lines · 449 tokens per session scan A dd8d68905ef2

Subscribe to this mod's changes

malware-classification is a skill published in the GitHub repository ogrodev/fsociety (20 stars, last pushed 5mo ago), licensed MIT. It adds 449 tokens to every session and 3,710 once invoked, about $0.0022 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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