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.
Work with Dynatrace dashboards - create, modify, query, and analyze dashboard JSON including tiles, layouts, DQL queries, variables, and visualizations.
Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations.
Core 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, data models, field namespaces, time alignment, entity/smartscape patterns, metric discovery, and performance/cost optimization (filter early, bucket filters, short time ranges, field selection, sampling, cardinality). Trigger: "write/build/fix a DQL query", "DQL syntax", "query logs/spans/metrics", "create a timeseries", "optimize my DQL", "make my query faster/cheaper", "reduce DQL cost/consumption/scanned data", "keep DQL cost under control". Do NOT use to explain an existing query or answer product questions. For MONITORING a tenant's ACTUAL query consumption/billing (how much queries cost, who scanned most, cost trends) use dt-platform-costs — this tunes the query text, not billing data.
Real User Monitoring (RUM) on Dynatrace — web and mobile frontends. Core Web Vitals, user sessions, page performance, mobile crashes, frontend errors, and frontend-backend linking. Query via `user.events`, `user.sessions`, and `dt.frontend.*` metrics. Does NOT cover synthetic monitoring (HTTP/browser/network checks) — that's a separate domain.
Analyzes observability signals from customer GenAI applications with DQL. Reads OpenTelemetry GenAI spans and LLM evaluation bizevents. Use for: golden signals (traffic, errors, latency, saturation); LLM signals (model, provider, tokens); cost/token analytics, usage attribution, and prompt caching; agent signals (tool calls, steps, failures, loop detection, Smartscape topology); conversation/session analytics; guardrails (blocked/truncated responses); and evaluation signals (quality, pass/fail). Trigger: "LLM latency", "token usage by model", "cost by model and provider", "cost per conversation", "who is driving token spend", "do I have prompt caching", "failing agent tool calls", "find runaway agents", "responses truncated or blocked", "failed evaluations", "am I hitting rate limits", "token throughput / TPM", "provider throttling or 429s". Do NOT use for: Davis CoPilot/MCP telemetry (dt-platform), generic service metrics (dt-obs-services), logs (dt-obs-logs), or non-GenAI tracing (dt-obs-tracing).
Set up the Dynatrace iOS SDK (OneAgent) in an iOS project using Swift Package Manager. Automates adding the SPM dependency, creating a Dynatrace.plist configuration file, adding the import statement, adding privacy opt-in code, and building the project. Use this skill when the user wants to integrate, install, add, or set up Dynatrace monitoring in their iOS app. Keywords: Dynatrace, iOS, SDK, OneAgent, setup, install, integrate, SPM, Swift Package Manager, Info.plist, DTXApplicationID, DTXBeaconURL, mobile monitoring, RUM.
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).