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 26zl/cybersec-toolkit --skill automating-ioc-enrichmentgit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/automating-ioc-enrichment)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/automating-ioc-enrichment"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/automating-ioc-enrichment/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/26zl/cybersec-toolkit/automating-ioc-enrichment"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/automating-ioc-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 109 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00101 | $0.02106 |
| Opus 5 | $0.00051 | $0.01053 |
| Sonnet 5 | $0.00020 | $0.00421 |
| Haiku 4.5 | $0.00010 | $0.00211 |
Grade A, and why
automating-ioc-enrichment 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 9d 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.
vt_resp = requests.get( Copies of this mod
2 near-identical copies found in the catalogue:
- automating-ioc-enrichment — 100% identical, 0 lines differ
- automating-ioc-enrichment — 95% identical, 38 lines differ
How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automating IOC Enrichment
When to Use
Use this skill when:
- Building a SOAR playbook that automatically enriches SIEM alerts with threat intelligence context before routing to analysts
- Creating a Python pipeline for bulk IOC enrichment from phishing email submissions
- Reducing analyst mean time to triage (MTTT) by pre-populating alert context with VT, Shodan, and MISP data
Do not use this skill for fully automated blocking decisions without human review — enrichment automation should inform decisions, not execute blocks autonomously for high-impact actions.
Prerequisites
- SOAR platform (Cortex XSOAR, Splunk SOAR, Tines, or n8n) or Python 3.9+ environment
- API keys: VirusTotal, AbuseIPDB, Shodan, and at minimum one TIP (MISP or OpenCTI)
- SIEM integration endpoint for alert consumption
- Rate limit budgets documented per API (VT: 4/min free, 500/min enterprise)
Workflow
Step 1: Design Enrichment Pipeline Architecture
Define the enrichment flow for each IOC type:
SIEM Alert → Extract IOCs → Classify Type → Route to enrichment functions
IP Address → AbuseIPDB + Shodan + VirusTotal IP + MISP
Domain → VirusTotal Domain + PassiveTotal + Shodan + MISP
URL → URLScan.io + VirusTotal URL + Google Safe Browse
File Hash → VirusTotal Files + MalwareBazaar + MISP
→ Aggregate results → Calculate confidence score → Update alert → Notify analyst
Step 2: Implement Python Enrichment Functions
import requests
import time
from dataclasses import dataclass, field
from typing import Optional
RATE_LIMIT_DELAY = 0.25 # 4 requests/second for VT free tier
@dataclass
class EnrichmentResult:
ioc_value: str
ioc_type: str
vt_malicious: int = 0
vt_total: int = 0
abuse_confidence: int = 0
shodan_ports: list = field(default_factory=list)
misp_events: list = field(default_factory=list)
confidence_score: int = 0
def enrich_ip(ip: str, vt_key: str, abuse_key: str, shodan_key: str) -> EnrichmentResult:
result = EnrichmentResult(ip, "ip")
# VirusTotal IP lookup
vt_resp = requests.get(
f"https://www.virustotal.com/api/v3/ip_addresses/{ip}",
headers={"x-apikey": vt_key}
)
if vt_resp.status_code == 200:
stats = vt_resp.json()["data"]["attributes"]["last_analysis_stats"]
result.vt_malicious = stats.get("malicious", 0)
result.vt_total = sum(stats.values())
time.sleep(RATE_LIMIT_DELAY)
# AbuseIPDB
abuse_resp = requests.get(
"https://api.abuseipdb.com/api/v2/check",
headers={"Key": abuse_key, "Accept": "application/json"},
params={"ipAddress": ip, "maxAgeInDays": 90}
)
if abuse_resp.status_code == 200:
result.abuse_confidence = abuse_resp.json()["data"]["abuseConfidenceScore"]
# Calculate composite confidence score
result.confidence_score = min(
(result.vt_malicious / max(result.vt_total, 1)) * 60 +
(result.abuse_confidence / 100) * 40, 100
)
return result
def enrich_hash(sha256: str, vt_key: str) -> EnrichmentResult:
result = EnrichmentResult(sha256, "sha256")
vt_resp = requests.get(
f"https://www.virustotal.com/api/v3/files/{sha256}",
headers={"x-apikey": vt_key}
)
if vt_resp.status_code == 200:
stats = vt_resp.json()["data"]["attributes"]["last_analysis_stats"]
result.vt_malicious = stats.get("malicious", 0)
result.vt_total = sum(stats.values())
result.confidence_score = int((result.vt_malicious / max(result.vt_total, 1)) * 100)
return result
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.
- 9d ago First seen · 219 lines · 101 tokens per session scan A 185037aedd5f
automating-ioc-enrichment is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), licensed MIT. It adds 101 tokens to every session and 2,106 once invoked, about $0.0005 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 2026-09-03.
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