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Found 30 Skills
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).
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.
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, detect outliers, find key drivers, or leverage generative AI capabilities in BigQuery.
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.
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.
Expert in data forensics, anomaly detection, audit trail analysis, fraud detection, and breach investigation
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.
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.
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
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.
Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.
Multi-year financial trend comparison, regression detection, and anomaly flagging for tax planning and audit risk assessment