performing-ioc-enrichment-automation

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-ioc-enrichment-automation
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summary

Automates Indicator of Compromise (IOC) enrichment by orchestrating lookups across VirusTotal, AbuseIPDB, Shodan, MISP, and other intelligence sources to provide contextual scoring and disposition recommendations. Use when SOC analysts need rapid multi-source enrichment of IPs, domains, URLs, and file hashes during alert triage or incident investigation.

skill.md
name
performing-ioc-enrichment-automation
description
'Automates Indicator of Compromise (IOC) enrichment by orchestrating lookups across VirusTotal, AbuseIPDB, Shodan, MISP, and other intelligence sources to provide contextual scoring and disposition recommendations. Use when SOC analysts need rapid multi-source enrichment of IPs, domains, URLs, and file hashes during alert triage or incident investigation. '
domain
cybersecurity
subdomain
soc-operations
tags
- soc - ioc - enrichment - automation - virustotal - abuseipdb - shodan - threat-intelligence
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- DE.CM-01 - DE.AE-02 - RS.MA-01 - DE.AE-06

Performing IOC Enrichment Automation

When to Use

Use this skill when:

  • SOC analysts need to quickly enrich IOCs from multiple sources during alert triage
  • High alert volumes require automated enrichment to reduce manual lookup time
  • Incident investigations need comprehensive IOC context for scope assessment
  • SOAR playbooks require enrichment actions as part of automated triage workflows

Do not use for bulk blocking decisions without analyst review — enrichment provides context, not definitive malicious/benign determination.

Prerequisites

  • API keys: VirusTotal (free or premium), AbuseIPDB, Shodan, URLScan.io, GreyNoise
  • Python 3.8+ with requests, vt-py, shodan libraries
  • MISP instance or TIP for cross-referencing organizational intelligence
  • SOAR platform (optional) for workflow integration
  • Rate limit awareness: VT free (4 req/min), AbuseIPDB (1000/day), Shodan (1 req/sec)

Workflow

Step 1: Build Unified Enrichment Engine

Create a multi-source enrichment pipeline:

import requests
import vt
import shodan
import time
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class EnrichmentResult:
    ioc_value: str
    ioc_type: str
    virustotal: dict = field(default_factory=dict)
    abuseipdb: dict = field(default_factory=dict)
    shodan_data: dict = field(default_factory=dict)
    greynoise: dict = field(default_factory=dict)
    urlscan: dict = field(default_factory=dict)
    misp_matches: list = field(default_factory=list)
    risk_score: float = 0.0
    disposition: str = "Unknown"

class IOCEnrichmentEngine:
    def __init__(self, config):
        self.vt_client = vt.Client(config["virustotal_key"])
        self.shodan_api = shodan.Shodan(config["shodan_key"])
        self.abuseipdb_key = config["abuseipdb_key"]
        self.greynoise_key = config["greynoise_key"]
        self.urlscan_key = config["urlscan_key"]

    def enrich_ip(self, ip_address):
        result = EnrichmentResult(ioc_value=ip_address, ioc_type="ip")

        # VirusTotal
        try:
            vt_obj = self.vt_client.get_object(f"/ip_addresses/{ip_address}")
            result.virustotal = {
                "malicious": vt_obj.last_analysis_stats.get("malicious", 0),
                "suspicious": vt_obj.last_analysis_stats.get("suspicious", 0),
                "total_engines": sum(vt_obj.last_analysis_stats.values()),
                "reputation": vt_obj.reputation,
                "country": getattr(vt_obj, "country", "Unknown"),
                "as_owner": getattr(vt_obj, "as_owner", "Unknown")
            }
        except Exception as e:
            result.virustotal = {"error": str(e)}

        # AbuseIPDB
        try:
            response = requests.get(
                "https://api.abuseipdb.com/api/v2/check",
                headers={"Key": self.abuseipdb_key, "Accept": "application/json"},
                params={"ipAddress": ip_address, "maxAgeInDays": 90}
            )
            data = response.json()["data"]
            result.abuseipdb = {
                "confidence_score": data["abuseConfidenceScore"],
                "total_reports": data["totalReports"],
                "is_tor": data.get("isTor", False),
                "usage_type": data.get("usageType", "Unknown"),
                "isp": data.get("isp", "Unknown"),
                "domain": data.get("domain", "Unknown")
            }
        except Exception as e:
            result.abuseipdb = {"error": str(e)}

        # Shodan
        try:
            host = self.shodan_api.host(ip_address)
            result.shodan_data = {
                "ports": host.get("ports", []),
                "os": host.get("os", "Unknown"),
                "organization": host.get("org", "Unknown"),
                "isp": host.get("isp", "Unknown"),
                "vulns": host.get("vulns", []),
                "last_update": host.get("last_update", "Unknown")
            }
        except shodan.APIError:
            result.shodan_data = {"status": "Not found in Shodan"}

        # GreyNoise
        try:
            response = requests.get(
                f"https://api.greynoise.io/v3/community/{ip_address}",
                headers={"key": self.greynoise_key}
            )
            gn_data = response.json()
            result.greynoise = {
                "classification": gn_data.get("classification", "unknown"),
                "noise": gn_data.get("noise", False),
                "riot": gn_data.get("riot", False),
                "name": gn_data.get("name", "Unknown")
            }
        except Exception as e:
            result.greynoise = {"error": str(e)}

        # Calculate composite risk score
        result.risk_score = self._calculate_ip_risk(result)
        result.disposition = self._determine_disposition(result.risk_score)
        return result

    def enrich_domain(self, domain):
        result = EnrichmentResult(ioc_value=domain, ioc_type="domain")

        # VirusTotal
        try:
            vt_obj = self.vt_client.get_object(f"/domains/{domain}")
            result.virustotal = {
                "malicious": vt_obj.last_analysis_stats.get("malicious", 0),
                "suspicious": vt_obj.last_analysis_stats.get("suspicious", 0),
                "reputation": vt_obj.reputation,
                "creation_date": getattr(vt_obj, "creation_date", "Unknown"),
                "registrar": getattr(vt_obj, "registrar", "Unknown"),
                "categories": getattr(vt_obj, "categories", {})
            }
        except Exception as e:
            result.virustotal = {"error": str(e)}

        # URLScan.io
        try:
            response = requests.get(
                f"https://urlscan.io/api/v1/search/?q=domain:{domain}",
                headers={"API-Key": self.urlscan_key}
            )
            scans = response.json().get("results", [])
            result.urlscan = {
                "total_scans": len(scans),
                "verdicts": [s.get("verdicts", {}).get("overall", {}).get("malicious", False)
                            for s in scans[:5]],
                "last_scan": scans[0]["task"]["time"] if scans else "Never scanned"
            }
        except Exception as e:
            result.urlscan = {"error": str(e)}

        result.risk_score = self._calculate_domain_risk(result)
        result.disposition = self._determine_disposition(result.risk_score)
        return result

    def enrich_hash(self, file_hash):
        result = EnrichmentResult(ioc_value=file_hash, ioc_type="hash")

        # VirusTotal
        try:
            vt_obj = self.vt_client.get_object(f"/files/{file_hash}")
            result.virustotal = {
                "malicious": vt_obj.last_analysis_stats.get("malicious", 0),
                "suspicious": vt_obj.last_analysis_stats.get("suspicious", 0),
                "undetected": vt_obj.last_analysis_stats.get("undetected", 0),
                "total_engines": sum(vt_obj.last_analysis_stats.values()),
                "type_description": getattr(vt_obj, "type_description", "Unknown"),
                "popular_threat_name": getattr(vt_obj, "popular_threat_classification", {}).get(
                    "suggested_threat_label", "Unknown"
                ),
                "sandbox_verdicts": getattr(vt_obj, "sandbox_verdicts", {}),
                "first_seen": getattr(vt_obj, "first_submission_date", "Unknown")
            }
        except vt.APIError:
            result.virustotal = {"status": "Not found in VirusTotal"}

        # MalwareBazaar
        try:
            response = requests.post(
                "https://mb-api.abuse.ch/api/v1/",
                data={"query": "get_info", "hash": file_hash}
            )
            mb_data = response.json()
            if mb_data["query_status"] == "ok":
                entry = mb_data["data"][0]
                result.abuseipdb = {  # Reusing field for MalwareBazaar data
                    "malware_family": entry.get("signature", "Unknown"),
                    "tags": entry.get("tags", []),
                    "file_type": entry.get("file_type", "Unknown"),
                    "delivery_method": entry.get("delivery_method", "Unknown"),
                    "first_seen": entry.get("first_seen", "Unknown")
                }
        except Exception:
            pass

        result.risk_score = self._calculate_hash_risk(result)
        result.disposition = self._determine_disposition(result.risk_score)
        return result

    def _calculate_ip_risk(self, result):
        score = 0
        vt = result.virustotal
        abuse = result.abuseipdb
        gn = result.greynoise

        if isinstance(vt, dict) and "malicious" in vt:
            score += min(vt["malicious"] * 3, 30)
        if isinstance(abuse, dict) and "confidence_score" in abuse:
            score += abuse["confidence_score"] * 0.3
        if isinstance(gn, dict):
            if gn.get("classification") == "malicious":
                score += 20
            elif gn.get("riot"):
                score -= 20  # Known benign service
        return min(max(score, 0), 100)

    def _calculate_domain_risk(self, result):
        score = 0
        vt = result.virustotal
        if isinstance(vt, dict) and "malicious" in vt:
            score += min(vt["malicious"] * 4, 40)
            if vt.get("reputation", 0) < -5:
                score += 20
        return min(max(score, 0), 100)

    def _calculate_hash_risk(self, result):
        score = 0
        vt = result.virustotal
        if isinstance(vt, dict) and "malicious" in vt:
            total = vt.get("total_engines", 1)
            detection_rate = vt["malicious"] / total if total > 0 else 0
            score = detection_rate * 100
        return min(max(score, 0), 100)

    def _determine_disposition(self, risk_score):
        if risk_score >= 70:
            return "MALICIOUS — Block recommended"
        elif risk_score >= 40:
            return "SUSPICIOUS — Monitor and investigate"
        elif risk_score >= 10:
            return "LOW RISK — Likely benign, verify context"
        else:
            return "CLEAN — No indicators of malicious activity"

    def close(self):
        self.vt_client.close()

Step 2: Batch Enrichment for Incident Investigation

# Process multiple IOCs from an incident
iocs = [
    {"type": "ip", "value": "185.234.218.50"},
    {"type": "domain", "value": "evil-c2-server.com"},
    {"type": "hash", "value": "a1b2c3d4e5f6..."},
    {"type": "ip", "value": "45.33.32.156"},
]

config = {
    "virustotal_key": "YOUR_VT_KEY",
    "shodan_key": "YOUR_SHODAN_KEY",
    "abuseipdb_key": "YOUR_ABUSEIPDB_KEY",
    "greynoise_key": "YOUR_GREYNOISE_KEY",
    "urlscan_key": "YOUR_URLSCAN_KEY"
}

engine = IOCEnrichmentEngine(config)

results = []
for ioc in iocs:
    if ioc["type"] == "ip":
        result = engine.enrich_ip(ioc["value"])
    elif ioc["type"] == "domain":
        result = engine.enrich_domain(ioc["value"])
    elif ioc["type"] == "hash":
        result = engine.enrich_hash(ioc["value"])
    results.append(result)
    time.sleep(15)  # Rate limiting for free VT API

engine.close()

# Print summary
for r in results:
    print(f"{r.ioc_type}: {r.ioc_value}")
    print(f"  Risk Score: {r.risk_score}")
    print(f"  Disposition: {r.disposition}")
    print()

Step 3: Integrate with Splunk for Automated Enrichment

Create a Splunk custom search command for inline enrichment:

index=notable sourcetype="stash"
| table src_ip, dest_ip, file_hash, url
| lookup threat_intel_ip_lookup ip AS src_ip OUTPUT vt_score, abuse_score, disposition
| lookup threat_intel_hash_lookup hash AS file_hash OUTPUT vt_detections, malware_family
| eval combined_risk = coalesce(vt_score, 0) + coalesce(abuse_score, 0)
| where combined_risk > 50
| sort - combined_risk

Step 4: Generate Enrichment Report

def generate_enrichment_report(results):
    report = []
    report.append("IOC ENRICHMENT REPORT")
    report.append("=" * 60)

    for r in sorted(results, key=lambda x: x.risk_score, reverse=True):
        report.append(f"\n{r.ioc_type.upper()}: {r.ioc_value}")
        report.append(f"  Risk Score: {r.risk_score}/100")
        report.append(f"  Disposition: {r.disposition}")

        if r.virustotal and "malicious" in r.virustotal:
            report.append(f"  VirusTotal: {r.virustotal['malicious']}/{r.virustotal.get('total_engines', 'N/A')} malicious")
        if r.abuseipdb and "confidence_score" in r.abuseipdb:
            report.append(f"  AbuseIPDB: {r.abuseipdb['confidence_score']}% confidence, {r.abuseipdb['total_reports']} reports")
        if r.greynoise and "classification" in r.greynoise:
            report.append(f"  GreyNoise: {r.greynoise['classification']}")
        if r.shodan_data and "ports" in r.shodan_data:
            report.append(f"  Shodan: Ports {r.shodan_data['ports']}, Org: {r.shodan_data.get('organization', 'N/A')}")

    return "\n".join(report)

Key Concepts

TermDefinition
IOC EnrichmentProcess of adding contextual intelligence to raw indicators from multiple external sources
Composite Risk ScoreWeighted aggregate score combining multiple intelligence sources for disposition decisions
Rate LimitingAPI request restrictions requiring throttling (VT free: 4/min, AbuseIPDB: 1000/day)
GreyNoise RIOTRule It Out — GreyNoise dataset of known benign services to reduce false positives
Passive DNSHistorical DNS resolution data showing domain-to-IP mappings over time
DefangingModifying IOCs for safe handling in reports (evil.com becomes evil[.]com)

Tools & Systems

  • VirusTotal: Multi-engine malware scanner providing file, URL, IP, and domain analysis with 70+ AV engines
  • AbuseIPDB: Community IP reputation database with abuse confidence scoring and ISP attribution
  • Shodan: Internet-wide scanner providing open ports, banners, and vulnerability data for IP addresses
  • GreyNoise: Internet noise intelligence distinguishing targeted attacks from opportunistic scanning
  • URLScan.io: URL analysis platform capturing screenshots, DOM, and network requests for phishing detection

Common Scenarios

  • Alert Triage Enrichment: Auto-enrich all IPs in a notable event to determine if source is known malicious
  • Incident Scope Assessment: Batch-enrich all IOCs from a compromised host to identify C2 infrastructure
  • Threat Intel Validation: Enrich received IOC feed to validate quality before adding to blocking controls
  • Phishing URL Analysis: Enrich URLs from reported phishing emails with URLScan and VT before user notification
  • False Positive Investigation: Enrich flagged IP to determine if it belongs to CDN/cloud provider (legitimate)

Output Format

IOC ENRICHMENT REPORT — IR-2024-0450
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Enrichment Time: 2024-03-15 14:30 UTC
IOCs Processed:  4

IP: 185.234.218[.]50
  Risk Score:   87/100 — MALICIOUS
  VirusTotal:   14/90 engines flagged malicious
  AbuseIPDB:    92% confidence, 347 reports
  Shodan:       Ports [22, 80, 443, 4444], Org: BulletProof Hosting
  GreyNoise:    malicious — known C2 infrastructure
  Action:       BLOCK immediately

DOMAIN: evil-c2-server[.]com
  Risk Score:   73/100 — MALICIOUS
  VirusTotal:   8/90 engines flagged
  URLScan:      5 scans, 4 malicious verdicts
  WHOIS:        Registered 3 days ago via Namecheap
  Action:       BLOCK and add to DNS sinkhole

HASH: a1b2c3d4e5f6...
  Risk Score:   91/100 — MALICIOUS
  VirusTotal:   52/72 engines (Cobalt Strike Beacon)
  MalwareBazaar: Tags: cobalt-strike, beacon, c2
  Action:       BLOCK hash, quarantine affected endpoints

IP: 45.33.32[.]156
  Risk Score:   5/100 — CLEAN
  VirusTotal:   0/90 engines
  GreyNoise:    benign — Shodan scanner
  Action:       No action required (known scanner)
how to use performing-ioc-enrichment-automation

How to use performing-ioc-enrichment-automation on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add performing-ioc-enrichment-automation
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-ioc-enrichment-automation

The skills CLI fetches performing-ioc-enrichment-automation from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/performing-ioc-enrichment-automation

Reload or restart Cursor to activate performing-ioc-enrichment-automation. Access the skill through slash commands (e.g., /performing-ioc-enrichment-automation) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.846 reviews
  • Fatima Wang· Dec 28, 2024

    performing-ioc-enrichment-automation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Harper Abbas· Dec 4, 2024

    performing-ioc-enrichment-automation reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Advait Liu· Nov 23, 2024

    Registry listing for performing-ioc-enrichment-automation matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Henry Zhang· Nov 19, 2024

    I recommend performing-ioc-enrichment-automation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Olivia Verma· Nov 3, 2024

    Useful defaults in performing-ioc-enrichment-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Olivia Thomas· Oct 22, 2024

    Registry listing for performing-ioc-enrichment-automation matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Soo Khanna· Oct 18, 2024

    Keeps context tight: performing-ioc-enrichment-automation is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Anaya Dixit· Oct 14, 2024

    Useful defaults in performing-ioc-enrichment-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Henry Anderson· Oct 10, 2024

    Solid pick for teams standardizing on skills: performing-ioc-enrichment-automation is focused, and the summary matches what you get after install.

  • Soo Malhotra· Sep 21, 2024

    performing-ioc-enrichment-automation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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