New to Claude Skills? Learn how to install them →

mukul975 on GitHub

Analyzing Threat Intelligence Feeds

Free

Transform threat data into actionable insights.

Get this skill

Free · Opens the source repo

What Analyzing Threat Intelligence Feeds does

The Analyzing Threat Intelligence Feeds skill is designed for cybersecurity professionals who need to process and analyze diverse threat intelligence feeds. This skill allows users to ingest both structured and unstructured data from various sources, such as commercial threat feeds and open-source intelligence (OSINT). By normalizing these feeds into a unified STIX 2.1 format, it helps streamline the process of extracting actionable indicators and understanding adversary tactics and campaigns. This is particularly useful for organizations looking to enhance their threat detection capabilities and improve their incident response strategies.

The skill operates through a well-defined workflow that begins with enumerating and prioritizing available threat feeds. Users can categorize feeds based on their type—commercial, government, or OSINT—and score them on criteria such as update frequency and historical accuracy. Once the feeds are prioritized, users can ingest them using TAXII 2.1 or REST API methods, ensuring that the data is current and relevant to their specific threat landscape.

Normalization is a key feature of this skill, as it converts indicators of compromise (IOCs) into the STIX 2.1 schema, making it easier to analyze and correlate data. The skill also allows for deduplication of IOCs against an existing threat intelligence platform (TIP) database, which is essential for maintaining a clean and efficient dataset. Additionally, it enriches IOCs with contextual information from various sources, enhancing the overall quality of the intelligence being analyzed.

Finally, the skill enables the distribution of enriched indicators to various consuming systems, such as SIEMs and firewalls. This ensures that the threat intelligence is actionable and can be integrated into existing security workflows. Overall, this skill is ideal for organizations looking to leverage threat intelligence effectively to bolster their cybersecurity posture.

When to use it

Use this skill when you need to assess and normalize threat intelligence feeds for better security decision-making.

When not to use it

This skill is not suitable for real-time incident response or raw packet capture analysis without prior threat intelligence context.

What you can build with it

Ingesting New Threat Feeds

Use this skill to efficiently ingest and assess new commercial or OSINT threat feeds, ensuring they are relevant to your organization's threat landscape.

Normalizing IOC Formats

When dealing with multiple IOC formats, this skill allows you to normalize them into STIX 2.1, simplifying analysis and integration into your security systems.

Building Automated Enrichment Pipelines

Leverage this skill to create automated pipelines that enrich IOCs with contextual data, enhancing your threat detection and response capabilities.

How to install Analyzing Threat Intelligence Feeds

View source

1. Install with the skills CLI

npx skills add mukul975/anthropic-cybersecurity-skills/analyzing-threat-intelligence-feeds --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by mukul975

Analyzing Threat Intelligence Feeds

When to Use

Use this skill when:

  • Ingesting new commercial or OSINT threat feeds and assessing their signal-to-noise ratio
  • Normalizing heterogeneous IOC formats (STIX 2.1, OpenIOC, YARA, Sigma) into a unified schema
  • Evaluating feed freshness, fidelity, and relevance to the organization's threat profile
  • Building automated enrichment pipelines that correlate IOCs against SIEM events

Do not use this skill for raw packet capture analysis or live incident triage without first establishing a CTI baseline.

Prerequisites

  • Access to a Threat Intelligence Platform (TIP) such as ThreatConnect, MISP, or OpenCTI
  • API keys for at least one commercial feed (Recorded Future, Mandiant Advantage, or VirusTotal Enterprise)
  • TAXII 2.1 client library (taxii2-client Python package or equivalent)
  • Role with read/write permissions to the TIP's indicator database

Workflow

Step 1: Enumerate and Prioritize Feed Sources

List all available feeds categorized by type (commercial, government, ISAC, OSINT):

  • Commercial: Recorded Future, Mandiant Advantage, CrowdStrike Falcon Intelligence
  • Government: CISA AIS (Automated Indicator Sharing), FBI InfraGard, MS-ISAC
  • OSINT: AlienVault OTX, Abuse.ch, PhishTank, Emerging Threats

Score each feed on: update frequency, historical accuracy rate, coverage of your sector, and attribution depth. Use a weighted scoring matrix with criteria from NIST SP 800-150 (Guide to Cyber Threat Information Sharing).

Step 2: Ingest via TAXII 2.1 or API

For TAXII-enabled feeds:

taxii2-client discover https://feed.example.com/taxii/
taxii2-client get-collection --collection-id <id> --since 2024-01-01

For REST API feeds (e.g., Recorded Future):

  • Query /v2/indicator/search with risk_score_min=65 to filter low-confidence IOCs
  • Apply rate limiting and exponential backoff for API resilience

Step 3: Normalize to STIX 2.1

Convert each IOC to STIX 2.1 objects using the OASIS standard schema:

  • IP address → indicator object with pattern: "[ipv4-addr:value = '...']"
  • Domain → indicator with pattern: "[domain-name:value = '...']"
  • File hash → indicator with pattern: "[file:hashes.SHA-256 = '...']"

Attach relationship objects linking indicators to threat-actor or malware objects. Use confidence field (0–100) based on source fidelity rating.

Step 4: Deduplicate and Enrich

Run deduplication against existing TIP database using normalized value + type as composite key. Enrich surviving IOCs:

  • VirusTotal: detection ratio, sandbox behavior reports
  • PassiveTotal (RiskIQ): WHOIS history, passive DNS, SSL certificate chains
  • Shodan: banner data, open ports, geographic location

Step 5: Distribute to Consuming Systems

Export enriched indicators via TAXII 2.1 push to SIEM (Splunk, Microsoft Sentinel), firewalls (Palo Alto XSOAR playbooks), and EDR platforms. Set TTL (time-to-live) per indicator type: IP addresses 30 days, domains 90 days, file hashes 1 year.

Key Concepts

TermDefinition
STIX 2.1Structured Threat Information Expression — OASIS standard JSON schema for CTI objects including indicators, threat actors, campaigns, and relationships
TAXII 2.1Trusted Automated eXchange of Intelligence Information — HTTPS-based protocol for sharing STIX content between servers and clients
IOCIndicator of Compromise — observable artifact (IP, domain, hash, URL) that indicates a system may have been breached
TLPTraffic Light Protocol — color-coded classification (RED/AMBER/GREEN/WHITE) defining sharing restrictions for CTI
Confidence ScoreNumeric value (0–100 in STIX) reflecting the producer's certainty about an indicator's malicious attribution
Feed FidelityHistorical accuracy rate of a feed measured by true positive rate in production detections

Tools & Systems

  • ThreatConnect TC Exchange: Aggregates 100+ commercial and OSINT feeds; provides automated playbooks for IOC enrichment
  • MISP (Malware Information Sharing Platform): Open-source TIP supporting STIX/TAXII; widely used by ISACs and government CERTs
  • OpenCTI: Open-source platform with native MITRE ATT&CK integration and graph-based relationship visualization
  • Recorded Future: Commercial feed with AI-powered risk scoring and real-time dark web monitoring
  • taxii2-client: Python library for TAXII 2.0/2.1 client operations (pip install taxii2-client)
  • PyMISP: Python API for MISP feed management and IOC submission

Common Pitfalls

  • IOC age staleness: IP addresses and domains rotate frequently; applying 1-year-old IOCs generates false positives. Enforce TTL policies.
  • Missing context: Blocking an IOC without understanding the associated campaign or adversary can disrupt legitimate business traffic (e.g., CDN IPs shared with malicious actors).
  • Feed overlap without deduplication: Ingesting the same IOC from five feeds without deduplication inflates indicator counts and SIEM rule complexity.
  • TLP violation: Redistributing RED-classified intelligence outside authorized boundaries violates sharing agreements and trust relationships.
  • Over-blocking on low-confidence indicators: Indicators with confidence below 50 should trigger detection-only rules, not blocking, to avoid operational disruption.

Frequently asked questions about Analyzing Threat Intelligence Feeds

Similar skills