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analyzing-threat-intelligence-feeds

'Analyzes structured and unstructured threat intelligence feeds to extract

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-threat-intelligence-feeds

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

91/100

Category

Security

Supported Platforms

Universal

Our assessment of analyzing-threat-intelligence-feeds

analyzing-threat-intelligence-feeds scores 91/100 on our quality scale, 231st of 544 Security skills we index (top 43%).

Its SKILL.md is 6.1 KB long, well organised into 12 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
17/20
Description
12/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so analyzing-threat-intelligence-feeds is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-26. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

analyzing-threat-intelligence-feeds compared with similar skills

All 4 of these similar skills score higher than analyzing-threat-intelligence-feeds; compare them before choosing.

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analyzing-threat-intelligence-feeds (this skill)by mukul9759133.3k25d agoSKILL.md
algorithmic-artby anthropics100177.9k3d agoSKILL.md
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Frequently asked questions

How do I install analyzing-threat-intelligence-feeds?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-threat-intelligence-feeds. The install tabs above show the steps for each supported agent.
Which AI agents does analyzing-threat-intelligence-feeds work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is analyzing-threat-intelligence-feeds safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is analyzing-threat-intelligence-feeds still maintained?
The repository was last updated 25 days ago, so analyzing-threat-intelligence-feeds is actively maintained.

name: analyzing-threat-intelligence-feeds description: 'Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.

' domain: cybersecurity subdomain: threat-intelligence tags:

  • STIX
  • TAXII
  • MITRE-ATT&CK
  • IOC
  • ThreatConnect
  • Recorded-Future
  • MISP
  • CTI
  • NIST-CSF version: 1.0.0 author: mahipal license: Apache-2.0 nist_csf:
  • ID.RA-01
  • ID.RA-05
  • DE.CM-01
  • DE.AE-02 mitre_attack:
  • T1071.001
  • T1566
  • T1568
  • T1583.001
  • T1102

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

| Term | Definition | |------|-----------| | STIX 2.1 | Structured Threat Information Expression — OASIS standard JSON schema for CTI objects including indicators, threat actors, campaigns, and relationships | | TAXII 2.1 | Trusted Automated eXchange of Intelligence Information — HTTPS-based protocol for sharing STIX content between servers and clients | | IOC | Indicator of Compromise — observable artifact (IP, domain, hash, URL) that indicates a system may have been breached | | TLP | Traffic Light Protocol — color-coded classification (RED/AMBER/GREEN/WHITE) defining sharing restrictions for CTI | | Confidence Score | Numeric value (0–100 in STIX) reflecting the producer's certainty about an indicator's malicious attribution | | Feed Fidelity | Historical 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.

Related Skills

View on GitHub
GitHub Stars33.3k
CategorySecurity
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions