dt-obs-analytics

Cursor Skill

Analyze dashboards and notebooks using Davis analyzers — anomaly detection, novelty scoring, and correlation. Use when the user references a specific Dynatrace dashboard or notebook (by URL, UUID, or name) and asks what it shows, which DQL queries it runs, whether a tile looks of

Dynatrace observability skills. DQL query patterns, application and infrastructure monitoring, log analysis, problem investigation, and incident response workflows. Use with dtctl or the Dynatrace MCP server (https://docs.dynatrace.com/docs/shortlink/dynatrace-mcp-server) for live platform access.

Created by DynatraceVerified by CursorView Source

Skills30

dt-alertingEnd-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, Slac

dt-app-dashboardsWork with Dynatrace dashboards - create, modify, query, and analyze dashboard JSON including tiles, layouts, DQL queries, variables, and visualizations.

dt-app-notebooksWork with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations.

dt-dql-essentialsCore DQL syntax, pitfalls, query patterns, and query optimization. Load to write, build, fix, or OPTIMIZE a DQL query — prevents syntax errors and makes queries faster, more efficient, and cheaper (less data scanned = lower query consumption/cost per run). Covers fetch commands,

dt-js-runtimeDynatrace server-side JS runtime — function contract, runtime limits, Web APIs, Node.js modules, fetch with credential vault, and the @dynatrace-sdk/* catalog.

MCP1

dynatrace