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Found 42 Skills
Generate a Dynatrace Gen 3 **KPI dashboard** (15–20 business KPIs, required map tile, branded section dividers) and a matching 30‑minute BizEvents injector for a named company, then deploy both via `dtctl`. Use this skill ONLY when the user explicitly asks for a Dynatrace KPI dashboard, business-event KPI demo, BizEvents injector, or a "KPI dashboard for <company>" — do NOT use for generic Dynatrace dashboards (SRE, infra, k8s, services, RUM) or for editing existing non-KPI dashboards. Triggers include phrases like "generate a KPI dashboard", "build a BizEvents demo for <company>", "spin up a KPI dashboard + injector", "/generate-kpi-dashboard". Requires `dtctl` authenticated to a Dynatrace Gen 3 tenant.
Work with Dynatrace dashboards - create, modify, query, and analyze dashboard JSON including tiles, layouts, DQL queries, variables, and visualizations. Supports dashboard creation, updates, data extraction, structure analysis, and best practices.
Migrate Dynatrace classic and Gen2 entity-based DQL, topology navigation, and classic entity selectors to Smartscape equivalents. Use this skill when users want to convert classic entities to Smartscape nodes, rewrite entityName, entityAttr, or classicEntitySelector patterns, or map old relationships to Smartscape traversal.
Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, visualizations, markdown documentation, and analytics workflows. Supports notebook creation from scratch, section-based updates, data extraction from Document Store, structure analysis, investigation workflows, and collaborative documentation.
Investigate incidents, debug performance issues, analyze logs, and manage observability resources in Dynatrace using the dtctl CLI. Use this skill whenever the user asks about error rates, latency spikes, service health, crash-looping pods, web vitals, SLO status, open problems, root cause analysis, log patterns, trace analysis, or building dashboards — even if they don't mention Dynatrace by name. Also covers DQL queries, workflow management, notebook and dashboard creation, settings configuration, and any operations against a Dynatrace environment.
Dynatrace API integration. Manage Organizations. Use when the user wants to interact with Dynatrace API data.
Suggest and validate semantic dictionary (SD) mappings for audit log integrations using raw vendor log payloads or live ingested events. Use when: mapping a vendor audit log feed, authentication logs, user activity logs to the Dynatrace SD; checking required semantic fields; proposing OpenPipeline processor extraction rules based on DQL; running runtime validation (fetches live logs by log.source, then applies static validation).
Problem entities, root cause analysis (RCA), impact assessment, and problem correlation. Query and analyze Dynatrace-detected problems and incidents.
Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
Suggest and validate semantic dictionary (SD) mappings for new security integrations using vendor API samples or live events. Use when: mapping a new security vendor data to Dynatrace SD; checking required fields; validating namespaces; highlighting discrepancies vs the semantic dictionary; proposing mapping improvements; running runtime validation against live tenant data.
Instruments an existing Android project (Kotlin or Java) with the Dynatrace Mobile Agent for basic monitoring. Covers zero-to-first-event setup only: Gradle plugin, agent config, and user privacy opt-in. Do not use for advanced Dynatrace configuration beyond initial instrumentation.
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 off, or wants to find anomalies, score novelty, or correlate its metrics. The trigger is a dashboard or notebook as the data source, not a general DQL question. This skill extracts timeseries queries efficiently without reading the full raw document JSON, then optionally runs Davis analyzers on the extracted metrics. Trigger phrases: "what's wrong on this dashboard", "analyze this notebook", "find anomalies", "novelty score", "correlate metrics", "extract DQL from dashboard", "dashboard URL", "tile", "run-analyzer", "timeseries extraction", "Davis analyzer".