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Found 29 Skills
Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization.
Guides structured security log analysis across authentication, network, endpoint, and cloud audit log sources. Auto-invoked when the user shares log data, asks about suspicious events, needs help interpreting Windows Event IDs or Linux auth logs, or is establishing baselines for anomaly detection. Produces log source taxonomy, anomaly identification, baseline recommendations, and correlation findings mapped to MITRE ATT&CK v16 techniques.
Expert in data forensics, anomaly detection, audit trail analysis, fraud detection, and breach investigation
Analyzes system and application logs to detect anomalies and security threats in blue-team operations.
Aggregate and display system metrics with anomaly detection for a time period
Apply PyGraphistry graph ML/AI workflows such as UMAP, DBSCAN, embedding-based anomaly analysis, and fit/transform pipelines on nodes or edges. Use for feature-driven exploration, clustering, anomaly triage, and graph-AI notebook workflows.
Elastic ML anomaly detection skill — investigation/RCA, score explanation, job operations (create, datafeed, start/stop, results), and troubleshooting (missing docs, memory limits, datafeed health, lifecycle). Operates against Kibana Agent Builder MCP tools (`ad_*`) on `.ml-anomalies-*`, `.ml-config`, `.ml-notifications-*`, `.ml-annotations-*`. Use when answering "what broke?"/"which entity?"/RCA, "why is score high/low?"/renormalization, "datafeed stopped"/"memory limit", or any request to set up or configure an ML anomaly detection job.
Create comprehensive forensic timelines from multiple data sources. Use when reconstructing event sequences, correlating activities across sources, or visualizing incident progression. Supports super timeline creation and analysis.
Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.
Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
Track and analyze e-commerce sales performance across platforms. Set up KPI dashboards, trend analysis, and performance alerts to catch issues and opportunities early.