Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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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.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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 |
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Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
中文简介正在准备中。当前显示原文说明。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
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Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
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Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
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