classify

A malware-analysis command for examining a binary file, such as a program file. It hashes the file, measures data randomness, checks for packer signatures, and uses tools such as YARA, radare2, or pefile.

In plain words
What is it for?
Use it to identify a binary, inspect entropy by section, detect known packers such as UPX or Themida, and support a malware classification review.
Why use it?
It gathers several clues that can indicate whether a binary is compressed, obfuscated, encrypted, or packed. It also keeps generated output in the project directory according to its storage rule.

Command

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 commands/ogrodev/fsociety/classify
Clone the repo
git clone --depth 1 https://github.com/ogrodev/fsociety
Per session 14 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 792 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00014 $0.00792
Opus 5 $0.00007 $0.00396
Sonnet 5 $0.00003 $0.00158
Haiku 4.5 $0.00001 $0.00079

Measured 2d ago against content hash fdb0f3fcc8b7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

classify 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 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.

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.

romero/commands/classify.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Storage Policy: ALL output files MUST be saved in the project directory. NEVER write to /tmp/ or any system temporary directory.

Malware Classification

Parse $ARGUMENTS to get the binary path.

Step 1 — Hash & Identify

node "${CLAUDE_PLUGIN_ROOT}/scripts/binary-hasher.js" hash "$ARGUMENTS"

Step 2 — Entropy Analysis

Use radare2 for per-section entropy:

r2 -qc 'iS~entropy' <binary>

Or use Python pefile:

python3 -c "
import pefile, math
pe = pefile.PE('$ARGUMENTS')
for s in pe.sections:
    data = s.get_data()
    if len(data) == 0: continue
    entropy = 0
    for x in range(256):
        p = data.count(bytes([x])) / len(data)
        if p > 0: entropy -= p * math.log2(p)
    name = s.Name.decode().rstrip('\x00')
    print(f'{name}: entropy={entropy:.2f} size={len(data)}')
"

Interpretation:

  • 0-1: Empty/null data
  • 1-5: Normal code/data
  • 5-7: Compressed or obfuscated
  • 7-8: Encrypted or packed (HIGH confidence)

Step 3 — Packer Detection

Known packer signatures:

  • UPX: Section names UPX0, UPX1, or UPX! magic
  • ASPack: Section name .aspack or .adata
  • Themida: Section name .winlicense or .themida
  • VMProtect: Section name .vmp0, .vmp1
  • .NET Reactor: .reacto section
  • Enigma Protector: .enigma section

Check with file:

file <binary> | grep -i "packed\|UPX\|compressed"

Step 4 — YARA Scan

yara -r /usr/share/yara/rules/ <binary> 2>/dev/null
yara -r /opt/yara-rules/ <binary> 2>/dev/null

If no YARA rules are installed, note this and suggest installing community rulesets.

Step 5 — ssdeep Similarity

ssdeep <binary>

Compare against known samples if a reference database exists.

Step 6 — IOC Extraction

From strings and structure, extract:

  • C2 server addresses (URLs, IPs)
  • Mutex names (unique identifiers)
  • Dropped file paths
  • Registry persistence keys
  • Scheduled task names
  • Service names

Read the full file on GitHub · 105 lines

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 · 105 lines · 14 tokens per session scan A fdb0f3fcc8b7

Subscribe to this mod's changes

classify is a command published in the GitHub repository ogrodev/fsociety (20 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 792 once invoked, about $0.0001 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.