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Found 29 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.
Use when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
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.
Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
Agent skill for performance-monitor - invoke with $agent-performance-monitor
Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.
DTC Full-Funnel Diagnosis & Attribution Engine —— Full-funnel attribution analysis, key indicator anomaly detection, cross-business-line problem localization, root cause analysis, priority ranking. Use when user mentions: 诊断, diagnose, 归因, attribution, 问题分析, root cause, 指标下降, conversion rate decline, ROAS decline, CPA increase, 全链路, full-funnel, 异常检测, anomaly, 问题出在哪, why the decline.
Эксперт по CloudWatch алармам. Используй для настройки мониторинга AWS, метрик, порогов и уведомлений.
Real-time monitoring and detection of adversarial attacks and model drift in production
Expert knowledge for Azure AI Metrics Advisor development including decision making, security, configuration, and integrations & coding patterns. Use when configuring data feeds, tuning anomaly detection, managing alert hooks, or integrating the Metrics Advisor APIs, and other Azure AI Metrics Advisor related development tasks. Not for Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Monitor (use azure-monitor), Azure Machine Learning (use azure-machine-learning).
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
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.