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Automated content similarity and grouping analysis. Groups related documents by topic, purpose, or content similarity.
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Based on SOC occupation classification
Perform exhaustive analysis of a critical IOC. Use when an IOC needs Tier 2+ investigation beyond basic enrichment - includes GTI pivoting, deep SIEM searches, correlation with related entities, and threat attribution. For escalated IOCs requiring comprehensive investigation.
Complete Tier 1 triage workflow. Orchestrates the full alert triage process: check-duplicates, triage-alert, enrich-ioc for each entity, and either close (FP/BTP) or escalate (TP/Suspicious). Use for end-to-end alert processing.
Complete Tier 2 investigation workflow. Orchestrates deep investigation of escalated cases: deep-dive-ioc, correlate-ioc, specialized triage (malware/login), pivot-on-ioc, and generate comprehensive report. Use for escalated cases requiring thorough analysis.
Check for duplicate or similar cases. Use before deep analysis to avoid investigating the same incident twice. Takes a CASE_ID and returns list of similar cases.
Close a case or alert with proper reason and documentation. Use when triage determines an alert is FP/BTP or investigation is complete. Requires artifact ID, type, closure reason, and root cause.
Check for existing SIEM alerts and case management entries related to IOCs. Use to understand if an indicator has triggered previous alerts or is part of ongoing investigations. Takes IOC list and returns related alerts and cases.
| name | cluster-documents |
| description | Automated content similarity and grouping analysis. Groups related documents by topic, purpose, or content similarity. |
| required_roles | {"scribe":"roles/scribe.viewer"} |
| personas | ["information-architect","data-analyst","researcher"] |
Analyze a repository of documents to group them based on content similarity, topic, or purpose. This skill helps organize large collections, identify redundancies, and discover relationships.
PATH - The repository to analyze (e.g., "/repository")SIMILARITY_THRESHOLD - (Optional) Float (0.0-1.0), threshold for grouping (default: 0.8)VISUALIZATION - (Optional) Boolean, whether to generate a visual representation (default: false)Ingest documents from PATH.
Apply clustering algorithms (e.g., K-Means, DBSCAN) to the document vectors.
SIMILARITY_THRESHOLD.Analyze the centroid or representative terms of each cluster to assign a meaningful label (Topic).
Generate the clustering report.
VISUALIZATION is true, create a scatter plot or dendrogram data.A CLUSTERING_REPORT object containing: