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 adriannoes/awesome-agentic-ai --skill extracting-iocs-from-malware-samplesgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples/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/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/extracting-iocs-from-malware-samples.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.03823 |
| Opus 5.5 | $0.00033 | $0.01529 |
| Sonnet 5.5 | $0.00016 | $0.00765 |
| Haiku 4.5 | $0.00008 | $0.00382 |
Grade A, and why
extracting-iocs-from-malware-samples 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 2mo 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.
resp = requests.get(f"https://www.virustotal.com/api/v3/ip_addresses/{ip}", How it starts
The opening of the file, as written. The whole thing — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extracting IOCs from Malware Samples
When to Use
- A malware analysis (static or dynamic) is complete and actionable indicators need to be extracted for defense teams
- Building blocklists for firewalls, proxies, and DNS sinkholes from analyzed samples
- Creating YARA rules, Snort/Suricata signatures, or SIEM detection content from malware artifacts
- Contributing to threat intelligence sharing platforms (MISP, OTX, ThreatConnect)
- Tracking malware campaigns by correlating IOCs across multiple samples
Do not use for IOCs from unverified sources without validation; false positives in blocklists can disrupt legitimate business operations.
Prerequisites
- Python 3.8+ with
iocextract,pefile,yara-pythonlibraries installed - Completed malware analysis report (static analysis, dynamic analysis, or reverse engineering)
- Access to PCAP files, memory dumps, or sandbox reports from the analysis
- MISP instance or STIX/TAXII server for structured IOC sharing
- VirusTotal API key for IOC enrichment and validation
- CyberChef for decoding obfuscated indicators
Workflow
Step 1: Extract File-Based IOCs
Compute hashes and identify file metadata indicators:
# Generate all standard hashes
md5sum malware_sample.exe
sha1sum malware_sample.exe
sha256sum malware_sample.exe
# Generate ssdeep fuzzy hash for similarity matching
ssdeep malware_sample.exe
# Generate imphash (import hash) for PE files
python3 -c "
import pefile
pe = pefile.PE('malware_sample.exe')
print(f'Imphash: {pe.get_imphash()}')
"
# Generate TLSH (Trend Micro Locality Sensitive Hash)
python3 -c "
import tlsh
with open('malware_sample.exe', 'rb') as f:
h = tlsh.hash(f.read())
print(f'TLSH: {h}')
"
# Compile file metadata IOCs
python3 << 'PYEOF'
import pefile
import os
import hashlib
import datetime
pe = pefile.PE("malware_sample.exe")
print("FILE IOCs:")
with open("malware_sample.exe", "rb") as f:
data = f.read()
print(f" MD5: {hashlib.md5(data).hexdigest()}")
print(f" SHA-1: {hashlib.sha1(data).hexdigest()}")
print(f" SHA-256: {hashlib.sha256(data).hexdigest()}")
print(f" File Size: {len(data)} bytes")
ts = pe.FILE_HEADER.TimeDateStamp
print(f" Compile: {datetime.datetime.utcfromtimestamp(ts)} UTC")
print(f" Imphash: {pe.get_imphash()}")
PYEOF
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.
- 2mo ago First seen · 399 lines · 82 tokens per session scan A 41eec5442e40
extracting-iocs-from-malware-samples is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (93 stars, last pushed 9d ago), licensed MIT. It adds 82 tokens to every session and 3,823 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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extracting-iocs-from-malware-samples
Extracts indicators of compromise (IOCs) from malware samples including file hashes, network indicators (IPs, domains, URLs), host artifacts (file paths, registry keys, mutexes), and behavioral patterns for threat intelligence sharing and detection rule creation. Activates for requests involving IOC extraction, threat…
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Classify workspace mode.
brownfield-drift
Enforces architecture boundaries defined in PLAN.md. Use when a PR crosses module/service boundaries, when the dev asks "are we following the architecture?", or as a scheduled architecture health check. Not for querying what a module does — use brownfield-chat for that.
wednesday-git
Unified Git workflow. Manages the entire task lifecycle: branch creation (sprint), atomic commits (git-os), and PR opening (pr-create).