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 autohandai/community-skills --skill automating-ioc-enrichmentgit clone --depth 1 https://github.com/autohandai/community-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/autohandai/community-skills/automating-ioc-enrichment)<a href="https://agentmods.dev/skills/autohandai/community-skills/automating-ioc-enrichment"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/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/autohandai/community-skills/automating-ioc-enrichment"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/automating-ioc-enrichment.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 | $0.00101 | $0.02027 |
| Opus 5 | $0.00051 | $0.01014 |
| Sonnet 5 | $0.00020 | $0.00405 |
| Haiku 4.5 | $0.00010 | $0.00203 |
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( This is a copy
95% identical to automating-ioc-enrichment — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 197 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 · 197 lines · 101 tokens per session scan A 40d3370d652f
automating-ioc-enrichment is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 101 tokens to every session and 2,027 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to automating-ioc-enrichment, differing in 38 lines, and is treated as a copy.
Other skills, from other repositories
automating-ioc-enrichment
Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email…
automating-ioc-enrichment
Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email…
automating-ioc-enrichment
Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email…
building-ioc-enrichment-pipeline-with-opencti
OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using O.
analyzing-indicators-of-compromise
Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…
building-ioc-enrichment-pipeline-with-opencti
Build an automated IOC enrichment pipeline on OpenCTI (STIX 2.1 native threat intel platform) using its internal enrichment connectors to pull context from VirusTotal, Shodan, AbuseIPDB, and GreyNoise, correlate indicators with known actors/campaigns, and score them for analyst prioritization. Use when deploying…