AI导哈

前端开发

dt-obs-frontends

dynatrace/dynat…e-for-ai

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.

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安装命令

npx skills add https://github.com/dynatrace/dynatrace-for-ai --skill dt-obs-frontends

SKILL.md

Frontend Observability (RUM)

Monitor web and mobile frontends using Real User Monitoring with DQL. Targets RUM on the latest Dynatrace — do not use RUM Classic data.

Concepts and data model: https://docs.dynatrace.com/docs/observe/digital-experience/new-rum-experience/concepts

Data Model

Three data sources, each for a different question:

Source Use for Granularity
timeseries dt.frontend.* Trends, dashboards, alerting Aggregated metric
fetch user.events Root cause, individual page views / requests / clicks / errors Per-event
fetch user.sessions Bounce rate, session duration, session-level aggregates Per-session

Rule of thumb: start with metrics for the shape of the problem, drill into events for the why. Use sessions when the question is about user journeys, not individual interactions.

相关 Skills

dt-dql-essentialsdynatrace/dynat…e-for-aiCore 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.数据与研究2904dt-app-dashboardsdynatrace/dynat…e-for-aiWork with Dynatrace dashboards - create, modify, query, and analyze dashboard JSON including tiles, layouts, DQL queries, variables, and visualizations.其他2836dt-obs-logsdynatrace/dynat…e-for-aiLog querying, filtering, pattern analysis, and error rate calculation. Use when searching application or infrastructure logs, analyzing error patterns, or correlating log data. Trigger: "show error logs", "search logs for keyword", "log error rate", "recent errors", "logs from last hour", "find log entries", "top error messages", "log patterns", "parse JSON logs", "logs by process group", "log trends over time", "log entry counts per minute". Do NOT use for explaining existing queries, product documentation questions, distributed tracing or span analysis (use dt-obs-tracing).数据与研究2736dt-obs-problemsdynatrace/dynat…e-for-aiDAVIS problem analysis including root cause identification, impact assessment, and correlation with other telemetry. Use when querying or investigating detected problems. Trigger: "active problems", "root cause analysis", "problem impact", "affected users", "list problems", "P-12345 details", "recurring problems", "problem history", "problem trending", "blast radius", "which entity caused the problem", "problems affecting Kubernetes", "problems by service". Do NOT use for explaining existing queries, product documentation questions, generic log searching, distributed tracing, or host-level resource monitoring.部署与运维2733dt-app-notebooksdynatrace/dynat…e-for-aiWork with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations.其他2725dt-obs-tracingdynatrace/dynat…e-for-aiDistributed traces, spans, service dependencies, and request flow analysis. Use when investigating span-level details, failures, performance bottlenecks, or trace correlation. Trigger: "trace analysis", "slow requests", "failed spans", "service dependencies", "distributed trace", "span details", "HTTP status codes in traces", "database query spans", "messaging spans", "gRPC calls", "Lambda cold starts", "trace ID lookup", "exception analysis", "correlate logs and traces", "request attributes". Do NOT use for explaining existing queries, product documentation or configuration questions, service-level RED metrics (use dt-obs-services), log searching (use dt-obs-logs), or problem analysis (use dt-obs-problems).数据与研究2697