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Found 39 Skills
Rule-based anomaly detection for production systems with configurable thresholds, cooldown periods to prevent alert storms, and error pattern tracking for repeated failures.
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.
Detect patterns, anomalies, and trends in code and data. Use when identifying code smells, finding security vulnerabilities, or discovering recurring patterns. Handles regex patterns, AST analysis, and statistical anomaly detection.
Run forensic ratio and trend checks from SEC filing data to validate or challenge Shenanigans hypotheses. Use when users ask for quantitative red-flag checks, earnings quality diagnostics, or quarter-over-quarter anomaly detection.
DTC Data Dashboard & Health Check Engine — Full-link data analysis, KPI tracking, industry benchmarking, data health assessment, market trend monitoring. Use when user mentions: data health check, data audit, KPI, dashboard, metrics tracking, metrics, baseline, benchmark, data analysis, revenue report, channel data, advertising data, ROAS tracking.
Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when users ask for 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.
Configure Harness AI-powered operations (AIDA) via MCP. Set up predictive failure analysis with ML models for memory leaks, disk exhaustion, connection pool saturation, and latency degradation. Configure intelligent alert correlation and noise reduction to reduce alert volume. Use when asked to set up predictive failure analysis, configure AI-powered alerting, reduce alert noise, or enable ML-based anomaly detection. Do NOT use for pipeline debugging (use debug-pipeline instead) or SLO management (use manage-slos instead). Trigger phrases: AIDA, predictive failure, alert correlation, noise reduction, anomaly detection, AI ops, predictive analysis, alert fatigue, ML alerting, intelligent alerting.
Agent skill for performance-monitor - invoke with $agent-performance-monitor
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'.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.
Analyze system, application, and security logs for forensic investigation. Use when investigating security incidents, insider threats, system compromises, or any scenario requiring analysis of log data. Supports Windows Event Logs, Syslog, web server logs, and application-specific log formats.
Эксперт по CloudWatch алармам. Используй для настройки мониторинга AWS, метрик, порогов и уведомлений.