building-threat-intelligence-feed-integration▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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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.
| 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,stix2Python 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 Source | Format | IOC Types | Update Freq | Cost |
|---|---|---|---|---|
| AlienVault OTX | STIX/JSON | IP, Domain, Hash, URL | Real-time | Free |
| Abuse.ch URLhaus | CSV/JSON | URL, Domain | Every 5 min | Free |
| Abuse.ch MalwareBazaar | JSON API | File Hash | Real-time | Free |
| CISA AIS | STIX/TAXII 2.1 | All types | Daily | Free (US Gov) |
| CrowdStrike Intel | STIX/JSON | All types + Actor TTP | Real-time | Commercial |
| Mandiant Advantage | STIX 2.1 | All types + Reports | Real-time | Commercial |
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
| Term | Definition |
|---|---|
| STIX 2.1 | Structured Threat Information Expression — standardized JSON format for sharing threat intelligence objects |
| TAXII | Trusted Automated eXchange of Indicator Information — transport protocol for sharing STIX data via REST API |
| TIP | Threat Intelligence Platform — centralized system for aggregating, scoring, and distributing threat intelligence |
| IOC Scoring | Process of assigning confidence values to indicators based on source reliability and corroboration |
| Feed Deduplication | Removing duplicate IOCs across multiple sources while preserving multi-source attribution |
| IOC Expiration | Time-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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches building-threat-intelligence-feed-integration from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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.
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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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★75 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.
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