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-feedsInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
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.
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 foundOur 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.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| analyzing-threat-intelligence-feeds (this skill)by mukul975 | 91 | 33.3k | 25d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 3d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 3d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 4d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 130.2k | 4d ago | SKILL.md |
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.
Skill content
View source on GitHubname: 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/searchwithrisk_score_min=65to 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 →
indicatorobject withpattern: "[ipv4-addr:value = '...']" - Domain →
indicatorwithpattern: "[domain-name:value = '...']" - File hash →
indicatorwithpattern: "[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.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
