Loading...
Loading...
Found 41 Skills
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.
End-to-end Dynatrace alerting lifecycle — anomaly detector setup and model selection (static threshold, adaptive baseline, seasonal baseline), alert event storage in Grail, problem grouping and denoising by root cause analysis, and workflow-based notification routing (email, Slack, ServiceNow, webhook). Use when configuring alerting, choosing between detector types, querying alert event history, understanding why alerts merged into a problem, or setting up problem-triggered notifications.
Generate a monthly financial summary with metrics, trends, and anomalies.
Huawei Cloud CCE Metric analysis skill using the Python dispatcher with hcloud-backed cloud service queries. Use this skill when the user wants to: (1) query Pod/Node/CoreDNS/nginx-ingress/autoscaler/control-plane CPU, memory, disk, QPS, latency, request, connection, certificate, scaling, or error-rate metrics, (2) get resource usage TopN rankings, (3) query ECS/ELB/EIP/NAT cloud resource metrics, (4) aggregate cluster monitoring data with anomaly detection, (5) detect threshold-based resource anomalies. Trigger: user mentions "metric analysis", "指标分析", "CCE metrics", "CCE 指标", "AOM metrics", "AOM 指标", "CoreDNS metrics", "CoreDNS 指标", "nginx ingress metrics", "nginx-ingress 指标", "autoscaler metrics", "autoscaler 指标", "HPA metrics", "HPA 指标", "apiserver metrics", "etcd metrics", "controller manager metrics", "scheduler metrics", "control plane metrics", "控制面指标", "certificate expiration", "证书过期", "resource metrics", "资源指标", "CPU usage", "CPU 使用率", "memory usage", "内存使用率", "performance monitoring", "性能监控", "TopN", "resource ranking", "资源排名"
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.
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.
Focused Signals scout for PostHog projects using logs. Watches for volume bursts, severity-distribution shifts, service silence, fresh message patterns, and trace-correlated bursts via the logs ingestion pipeline. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
Analyzes system and application logs to detect anomalies and security threats in blue-team operations.