building-threat-intelligence-feed-integration

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

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/building-threat-intelligence-feed-integration
0 commentsdiscussion
summary

Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

skill.md
name
building-threat-intelligence-feed-integration
description
'Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems. '
domain
cybersecurity
subdomain
soc-operations
tags
- soc - threat-intelligence - stix - taxii - misp - feeds - ioc - siem-integration
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- DE.CM-01 - DE.AE-02 - RS.MA-01 - DE.AE-06

Building Threat Intelligence Feed Integration

When to Use

Use this skill when:

  • SOC teams need automated ingestion of threat intelligence feeds into SIEM platforms
  • Multiple TI sources require normalization into a common format (STIX 2.1)
  • Detection systems need real-time IOC matching against network and endpoint telemetry
  • TI feed quality assessment and deduplication processes need to be established

Do not use for manual IOC lookup — use dedicated enrichment tools (VirusTotal, AbuseIPDB) for ad-hoc queries.

Prerequisites

  • MISP instance or Threat Intelligence Platform (TIP) for feed aggregation
  • STIX/TAXII client library (taxii2-client, stix2 Python packages)
  • SIEM platform (Splunk ES, Elastic Security, or Sentinel) with TI framework configured
  • API keys for commercial and open-source feeds (AlienVault OTX, Abuse.ch, CISA AIS)
  • Python 3.8+ for feed processing automation

Workflow

Step 1: Identify and Catalog Intelligence Sources

Map available feeds by type, format, and update frequency:

Feed SourceFormatIOC TypesUpdate FreqCost
AlienVault OTXSTIX/JSONIP, Domain, Hash, URLReal-timeFree
Abuse.ch URLhausCSV/JSONURL, DomainEvery 5 minFree
Abuse.ch MalwareBazaarJSON APIFile HashReal-timeFree
CISA AISSTIX/TAXII 2.1All typesDailyFree (US Gov)
CrowdStrike IntelSTIX/JSONAll types + Actor TTPReal-timeCommercial
Mandiant AdvantageSTIX 2.1All types + ReportsReal-timeCommercial

Step 2: Ingest STIX/TAXII Feeds

Connect to a TAXII 2.1 server and download indicators:

from taxii2client.v21 import Server, Collection
from stix2 import parse

# Connect to TAXII server (example: CISA AIS)
server = Server(
    "https://taxii.cisa.gov/taxii2/",
    user="your_username",
    password="your_password"
)

# List available collections
for api_root in server.api_roots:
    print(f"API Root: {api_root.title}")
    for collection in api_root.collections:
        print(f"  Collection: {collection.title} (ID: {collection.id})")

# Fetch indicators from a collection
collection = Collection(
    "https://taxii.cisa.gov/taxii2/collections/COLLECTION_ID/",
    user="your_username",
    password="your_password"
)

# Get indicators added in last 24 hours
from datetime import datetime, timedelta
added_after = (datetime.utcnow() - timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%S.000Z")

response = collection.get_objects(added_after=added_after, type=["indicator"])
for obj in response.get("objects", []):
    indicator = parse(obj)
    print(f"Type: {indicator.type}")
    print(f"Pattern: {indicator.pattern}")
    print(f"Valid Until: {indicator.valid_until}")
    print(f"Confidence: {indicator.confidence}")
    print("---")

Step 3: Ingest Open-Source Feeds

Abuse.ch URLhaus Feed:

import requests
import csv
from io import StringIO

# Download URLhaus recent URLs
response = requests.get("https://urlhaus.abuse.ch/downloads/csv_recent/")
reader = csv.reader(StringIO(response.text), delimiter=',')

indicators = []
for row in reader:
    if row[0].startswith("#"):
        continue
    indicators.append({
        "id": row[0],
        "dateadded": row[1],
        "url": row[2],
        "url_status": row[3],
        "threat": row[5],
        "tags": row[6]
    })

print(f"Ingested {len(indicators)} URLs from URLhaus")

# Filter for active threats only
active = [i for i in indicators if i["url_status"] == "online"]
print(f"Active threats: {len(active)}")

AlienVault OTX Pulse Feed:

from OTXv2 import OTXv2, IndicatorTypes

otx = OTXv2("YOUR_OTX_API_KEY")

# Get subscribed pulses (last 24 hours)
pulses = otx.getall(modified_since="2024-03-14T00:00:00")

for pulse in pulses:
    print(f"Pulse: {pulse['name']}")
    print(f"Tags: {pulse['tags']}")
    for indicator in pulse["indicators"]:
        print(f"  IOC: {indicator['indicator']} ({indicator['type']})")

Abuse.ch Feodo Tracker (C2 IPs):

response = requests.get("https://feodotracker.abuse.ch/downloads/ipblocklist_recommended.json")
c2_data = response.json()

for entry in c2_data:
    print(f"IP: {entry['ip_address']}:{entry['port']}")
    print(f"Malware: {entry['malware']}")
    print(f"First Seen: {entry['first_seen']}")
    print(f"Last Online: {entry['last_online']}")

Step 4: Normalize and Deduplicate

Convert all feeds to STIX 2.1 format for standardization:

from stix2 import Indicator, Bundle
import hashlib

def create_stix_indicator(ioc_value, ioc_type, source, confidence=50):
    """Convert raw IOC to STIX 2.1 indicator"""
    pattern_map = {
        "ipv4": f"[ipv4-addr:value = '{ioc_value}']",
        "domain": f"[domain-name:value = '{ioc_value}']",
        "url": f"[url:value = '{ioc_value}']",
        "sha256": f"[file:hashes.'SHA-256' = '{ioc_value}']",
        "md5": f"[file:hashes.MD5 = '{ioc_value}']",
    }

    return Indicator(
        name=f"{ioc_type}: {ioc_value}",
        pattern=pattern_map[ioc_type],
        pattern_type="stix",
        valid_from="2024-03-15T00:00:00Z",
        confidence=confidence,
        labels=[source],
        custom_properties={"x_source_feed": source}
    )

# Deduplicate across sources
seen_iocs = set()
unique_indicators = []

for ioc in all_collected_iocs:
    ioc_hash = hashlib.sha256(f"{ioc['type']}:{ioc['value']}".encode()).hexdigest()
    if ioc_hash not in seen_iocs:
        seen_iocs.add(ioc_hash)
        unique_indicators.append(
            create_stix_indicator(ioc["value"], ioc["type"], ioc["source"])
        )

bundle = Bundle(objects=unique_indicators)
print(f"Unique indicators: {len(unique_indicators)}")

Step 5: Push to SIEM Threat Intelligence Framework

Push to Splunk ES Threat Intelligence:

import requests

splunk_url = "https://splunk.company.com:8089"
headers = {"Authorization": f"Bearer {splunk_token}"}

for indicator in unique_indicators:
    # Extract IOC value from STIX pattern
    ioc_value = indicator.pattern.split("'")[1]

    # Upload to Splunk ES threat intel collection
    data = {
        "ip": ioc_value,
        "description": indicator.name,
        "weight": indicator.confidence // 10,
        "threat_key": indicator.id,
        "source_feed": indicator.get("x_source_feed", "unknown")
    }

    requests.post(
        f"{splunk_url}/services/data/threat_intel/item/ip_intel",
        headers=headers, data=data,
        verify=not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true",  # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
    )

Push to MISP for centralized management:

from pymisp import PyMISP, MISPEvent, MISPAttribute

misp = PyMISP("https://misp.company.com", "YOUR_MISP_API_KEY")

# Create event for feed batch
event = MISPEvent()
event.info = f"TI Feed Import - {datetime.now().strftime('%Y-%m-%d')}"
event.threat_level_id = 2  # Medium
event.analysis = 2  # Completed

# Add indicators as attributes
for ioc in unique_indicators:
    attr = MISPAttribute()
    attr.type = "ip-dst" if "ipv4" in ioc.pattern else "domain"
    attr.value = ioc.pattern.split("'")[1]
    attr.to_ids = True
    attr.comment = f"Source: {ioc.get('x_source_feed', 'mixed')}"
    event.add_attribute(**attr)

result = misp.add_event(event)
print(f"MISP Event created: {result['Event']['id']}")

Step 6: Monitor Feed Health and Quality

Track feed effectiveness metrics:

index=threat_intel sourcetype="threat_intel_manager"
| stats count AS total_iocs,
        dc(threat_key) AS unique_iocs,
        dc(source_feed) AS feed_count
  by source_feed
| join source_feed [
    search index=notable source="Threat Intelligence"
    | stats count AS matches by source_feed
  ]
| eval match_rate = round(matches / unique_iocs * 100, 2)
| sort - match_rate
| table source_feed, unique_iocs, matches, match_rate

Key Concepts

TermDefinition
STIX 2.1Structured Threat Information Expression — standardized JSON format for sharing threat intelligence objects
TAXIITrusted Automated eXchange of Indicator Information — transport protocol for sharing STIX data via REST API
TIPThreat Intelligence Platform — centralized system for aggregating, scoring, and distributing threat intelligence
IOC ScoringProcess of assigning confidence values to indicators based on source reliability and corroboration
Feed DeduplicationRemoving duplicate IOCs across multiple sources while preserving multi-source attribution
IOC ExpirationTime-to-live policy removing aged indicators (IP: 30 days, Domain: 90 days, Hash: 1 year)

Tools & Systems

  • MISP: Open-source threat intelligence platform for feed aggregation, correlation, and sharing
  • AlienVault OTX: Free threat intelligence sharing platform with community pulse feeds
  • Abuse.ch: Suite of free threat feeds (URLhaus, MalwareBazaar, Feodo Tracker, ThreatFox)
  • OpenCTI: Open-source cyber threat intelligence platform supporting STIX 2.1 native storage
  • TAXII2 Client: Python library for connecting to STIX/TAXII 2.1 servers for automated indicator retrieval

Common Scenarios

  • New Feed Onboarding: Evaluate feed quality, map fields to STIX, configure automated ingestion pipeline
  • Multi-SIEM Distribution: Push normalized IOCs from MISP to Splunk, Elastic, and Sentinel simultaneously
  • False Positive Reduction: Score IOCs by source count and age, expire stale indicators automatically
  • Feed Quality Audit: Compare detection match rates across feeds to identify highest-value sources
  • Incident IOC Sharing: Package investigation IOCs as STIX bundle and share with ISACs via TAXII

Output Format

THREAT INTEL FEED STATUS — Daily Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Date:         2024-03-15
Total IOCs:   45,892 active indicators

Feed Health:
  Feed                  IOCs    Matches  Match Rate  Status
  Abuse.ch URLhaus      12,340  47       0.38%       HEALTHY
  AlienVault OTX        18,567  23       0.12%       HEALTHY
  Abuse.ch Feodo        1,203   12       1.00%       HEALTHY
  CISA AIS              8,945   8        0.09%       HEALTHY
  CrowdStrike Intel     4,837   31       0.64%       HEALTHY

Actions Today:
  New IOCs ingested:    1,247
  IOCs expired:         892
  Duplicates removed:   156
  SIEM matches:         121 notable events generated
  False positives:      3 (CDN IPs removed from feed)
how to use building-threat-intelligence-feed-integration

How to use building-threat-intelligence-feed-integration 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 building-threat-intelligence-feed-integration
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/building-threat-intelligence-feed-integration

The skills CLI fetches building-threat-intelligence-feed-integration 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/building-threat-intelligence-feed-integration

Reload or restart Cursor to activate building-threat-intelligence-feed-integration. Access the skill through slash commands (e.g., /building-threat-intelligence-feed-integration) 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.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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

4.575 reviews
  • Hana Li· Dec 28, 2024

    Registry listing for building-threat-intelligence-feed-integration matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Omar Thomas· Dec 24, 2024

    Keeps context tight: building-threat-intelligence-feed-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Soo Tandon· Dec 20, 2024

    We added building-threat-intelligence-feed-integration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Soo Smith· Dec 16, 2024

    I recommend building-threat-intelligence-feed-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Shikha Mishra· Dec 12, 2024

    building-threat-intelligence-feed-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Emma Brown· Dec 4, 2024

    Solid pick for teams standardizing on skills: building-threat-intelligence-feed-integration is focused, and the summary matches what you get after install.

  • Emma Johnson· Dec 4, 2024

    building-threat-intelligence-feed-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ama Wang· Nov 23, 2024

    We added building-threat-intelligence-feed-integration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Mia Thompson· Nov 23, 2024

    building-threat-intelligence-feed-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Soo Choi· Nov 19, 2024

    Useful defaults in building-threat-intelligence-feed-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

showing 1-10 of 75

1 / 8