SKILLS/analyzing-threat-intelligence-feeds/SKILL.md
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.
npx skillsauth add pinkpixel-dev/skills-collection-1 analyzing-threat-intelligence-feedsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
Do not use this skill for raw packet capture analysis or live incident triage without first establishing a CTI baseline.
List all available feeds categorized by type (commercial, government, ISAC, OSINT):
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).
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):
/v2/indicator/search with risk_score_min=65 to filter low-confidence IOCsConvert each IOC to STIX 2.1 objects using the OASIS standard schema:
indicator object with pattern: "[ipv4-addr:value = '...']"indicator with pattern: "[domain-name:value = '...']"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.
Run deduplication against existing TIP database using normalized value + type as composite key. Enrich surviving IOCs:
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.
| 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 |
testing
When the user wants a full ASO health audit, review their App Store listing quality, or diagnose why their app isn't ranking. Also use when the user mentions "ASO audit", "ASO score", "why am I not ranking", "listing review", or "optimize my app store page". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
testing
Clarify requirements before implementing. Use when serious doubts arise.
tools
Complete reference and build guide for ASI:One (ASI1) — the AI platform by Fetch.ai built for agentic, Web3-native applications. Use this skill IMMEDIATELY and ALWAYS when the user mentions ASI1, ASI:One, Fetch.ai AI API, building with ASI1, integrating ASI:One, asking about ASI1 models, tool calling with ASI1, ASI1 image generation, ASI1 agentic LLM, Agentverse, uagents, Agent Chat Protocol, structured output with ASI1, or OpenAI-compatible wrappers for ASI1. Also trigger when the user says things like "use ASI1 instead of OpenAI", "build an app with ASI:One", "ASI1 API", or references docs.asi1.ai. This skill covers everything needed to build production apps - setup, all models, all API features, tool calling, image gen, agentic orchestration, structured data, session management, streaming, LangChain integration, uagents / Agent Chat Protocol, and TypeScript/Node.js patterns.
data-ai
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.