dynatrace/dynatrace-for-ai
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npx skills add https://github.com/dynatrace/dynatrace-for-aiSkills in this repo
1Dt Dql EssentialsThe dt-dql-essentials skill is designed for >-. DQL Essentials Skill DQL is a pipeline-based query language. Queries chain commands with | to filter, transform, and aggregate data. Invoke when the user asks about dt dql essentials or related SKILL.md workflows.1.6kinstalls2Dt App DashboardsThe dt-app-dashboards skill is designed for work with Dynatrace dashboards - create, modify, query, and analyze dashboard JSON including tiles, layouts, DQL queries, variables, and visualizations. Dynatrace Dashboard Skill Overview Dynatrace dashboards are JSON documents stored in the Document Store containing tiles (content/visualizations), layouts (grid positioning), and variables (dynamic query parameters). When to use: Creating, modifying, querying, or analyzing dashboards. Invoke when the user asks about dt app dashboards or related SKILL.md workflows.1.6kinstalls3Dt Obs LogsThe dt-obs-logs skill >- # Log Analysis Skill Query, filter, and analyze Dynatrace log data using DQL for troubleshooting and monitoring. ## What This Skill Covers - Fetching and filtering logs by severity, content, and entity - Searching log messages using pattern matching - Calculating error rates and statistics - Analyzing log patterns and trends - Grouping and aggregating log data by dimensions > **Cross-source join required:** If the query must combine logs with host attributes > (OS type, hostname, IP address, cloud provider) → also read > `dt-dql-essentials/references/smartscape-topology-navigation.md` before writing the query. Log Searching Find specific log entries by time, severity, and content. Filter by severity (optional) 3. Search content for keywords 4. Sort and limit results **Example**: ```dql fetch logs, from:now() - 1h | filter status == "ERROR" | fields timestamp, content, process_group = dt.process_group.detected_name | sort timestamp desc | limit 100 ``` ### 2. Log Filtering Narrow down logs using multiple criteria (severity, entity, content). Fetch logs with time range 2.1.5kinstalls4Dt Obs ProblemsThe dt-obs-problems skill >- # Problem Analysis Skill Analyze Dynatrace AI-detected problems including root cause identification, impact assessment, and correlation with logs and metrics. Active Problem Triage - **Goal:** List and prioritize currently active problems - **Trigger:** "active problems", "what problems are open", "current issues", "availability issues" - **Done:** Prioritized list of active problems with category, user impact, and display IDs ### 2. Root Cause Investigation - **Goal:** Identify the root cause entity for a specific problem - **Trigger:** "root cause of P-12345", "what caused this problem", "which entity is the root cause" - **Done:** Root cause entity identified with affected entity list and blast radius ### 3. Problem Trending - **Goal:** Analyze problem patterns over time to identify recurring issues - **Trigger:** "recurring problems", "problem history", "problem trends last 30 days" - **Done:** Trend data showing problem frequency, recurring root causes, and resolution times --- ## Overview Dynatrace automatically detects anomalies, performance degradations, and failures across your environment, creating **problems** that aggregate related alert, w.1.5kinstalls5Dt App NotebooksThe dt-app-notebooks skill work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations. # Dynatrace Notebook Skill ## Overview Dynatrace notebooks are JSON documents stored in the Document Store containing an ordered array of **sections** - markdown blocks for narrative and `dql` blocks for DQL queries with visualizations. Sections render top-to-bottom in array order. **When to use:** Creating, modifying, querying, or analyzing notebooks. ## Notebook JSON Structure ```json { "name": "My Notebook", "type": "notebook", "content": { "version": "7", "defaultTimeframe": { "from": "now()-2h", "to": "now()" }, "sections": [ { "id": "1", "type": "markdown", "markdown": "# Title" }, { "id": "2", "type": "dql", "title": "Query Section", "showInput": true, "state": { "input": { "value": "fetch logs | summarize count()" }, "visualization": "table", "visualizationSettings": { "autoSelectVisualization": true, "chartSettings": {} }, "querySettings": { "maxResultRecords": 1000, "defaultScanLimitGbytes": 500, "maxResultMegaBytes": 1, "defaultSamplingRatio": 10, "enableSampling": false } } } ] } } ``` - Sections render in ar.1.5kinstalls6Dt Obs ServicesThe dt-obs-services skill Application Services Skill Monitor application service performance health and runtime-specific metrics using DQL Service Performance RED Metrics Monitor service Rate Errors Duration using metrics-based timeseries queries Key Metrics dt service request response_time Response time microseconds dt service request count Request count dt service request failure_count Failed request count Common Use Cases Response time monitoring avg p50 p95 p99 Error rate tracking and spike detection Traffic analysis throughput peaks growth Performance degradation detection Multi-cluster comparison Quick Example dql timeseries p95 percentile dt service request response_time 95 total_requests sum dt service request count failures sum dt service request failure_count by dt service name fieldsAdd p95_ms p95 1000 error_rate_pct failures 100 0 total_requests For detailed queries See references service-metrics md references service-metrics md 2 Advanced Service Analysis Span-based queries for complex scenarios requiring flexible filtering and custom aggregations Use Cases SLA compliance tracking with custom thresholds Service health scoring multi-dimensional Operation endpoint-level.1.5kinstalls7Dt Obs TracingThe dt-obs-tracing skill Application Tracing Skill Overview Distributed traces in Dynatrace consist of spans building blocks representing units of work With Traces in Grail every span is accessible via DQL with full-text searchability on all attributes This skill covers trace fundamentals common analysis patterns and span-type specific queries Investigate Slow Requests Goal Find and diagnose requests exceeding a latency threshold Trigger slow requests high latency p99 response time find traces over 5 seconds Done List of slow traces with duration endpoint service and trace IDs for drilldown 2 Analyze Request Failures Goal Identify failed requests failure reasons and exception patterns Trigger failed spans HTTP 500 errors exception analysis failure rate by service Done Failure breakdown by reason HTTP code exception gRPC status with exemplar traces 3 Map Service Dependencies Goal Understand service-to-service communication patterns and external API calls Trigger service dependencies what services does X call outgoing HTTP calls Done Dependency map showing call counts latency and error rates between services Core Concepts Understanding Traces and Spans Spans represent logical1.5kinstalls8Dt Obs HostsThe dt-obs-hosts skill >- # Infrastructure Hosts Skill Monitor and manage host and process infrastructure including CPU, memory, disk, network, and technology inventory. ## When to Use This Skill Use this skill when the user needs to: - **Inventory:** "Show me all Linux hosts in AWS us-east-1" - **Monitor:** "What hosts have high CPU usage?" - **Troubleshoot:** "Which processes are consuming the most memory?" - **Discover:** "What databases are running in production?" - **Plan:** "Track Kubernetes version distribution for upgrade planning" - **Cost:** "Calculate infrastructure costs by cost center" - **Security:** "Find all processes listening on port 22" - **Compliance:** "Identify hosts running EOL Java versions" - **Quality:** "Check data completeness for AWS hosts" - **Optimize:** "Find rightsizing candidates based on utilization" --- > **Cross-source join required:** If the query must combine host data with logs or other > telemetry sources (e.g. "show logs from Linux hosts with their IP addresses") → also read > `dt-dql-essentials/references/smartscape-topology-navigation.md` before writing the query. **Host Metrics** - `dt.host.cpu.*`, `dt.host.memory.*`, `dt.host.disk.*`,.1.5kinstalls9Dt Obs KubernetesThe dt-obs-kubernetes skill >- # Infrastructure Kubernetes Monitor and analyze Kubernetes infrastructure using Dynatrace DQL. Query cluster resources, monitor workload health, analyze pod placement, optimize costs, and assess security posture. ## When to Use This Skill - Monitoring Kubernetes cluster health and capacity - Analyzing pod and container resource utilization - Investigating pod failures, OOMKills, evictions, or crash loops - Debugging degraded deployments, stuck rollouts, or node pressure - Optimizing Kubernetes resource costs - Assessing security posture and compliance - Troubleshooting workload scheduling and placement - Auditing ingress routing and network policies ## Knowledge Base Structure ### Core Monitoring (Start Here) 1. **Cluster Inventory** → `references/cluster-inventory.md` - Clusters, namespaces, resource distribution 2. **Node Monitoring** - Node capacity, CPU/memory usage, pod density 3. **Pod Monitoring** - Pod CPU, memory, lifecycle events 4. **Workload Monitoring** - Deployment, StatefulSet, DaemonSet resources ### Advanced Topics 1. **Configuration Analysis** → `references/labels-annotations.md` - Parse k8s.object, labels, annotations 2. **Scheduli.1.5kinstalls10Dt Obs FrontendsThe dt-obs-frontends skill real User Monitoring (RUM) on Dynatrace - web, mobile, and hybrid frontends. Core Web Vitals, user sessions, page performance, mobile crashes, frontend errors, and trace correlation. Query via `user.events`, `user.sessions`, and `dt.frontend.*` metrics. Does NOT cover synthetic monitoring (HTTP/browser/network checks) - that's a separate domain. # Frontend Observability (RUM) Monitor web, mobile, and hybrid frontends using Real User Monitoring with DQL. Targets the **new RUM experience only** - do not use classic RUM data. ## 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.1.4kinstalls11Dt Obs AwsThe dt-obs-aws skill monitors AWS infrastructure through Dynatrace Smartscape and DQL queries. It covers compute resources like EC2, Lambda, ECS, and EKS, networking with VPC, subnets, security groups, and load balancers, databases including RDS and DynamoDB, storage such as S3 and EBS volumes, and messaging with SQS and SNS. Common tasks include inventory by region, VPC topology mapping, unattached EBS cost savings, publicly accessible database detection, missing Environment tag compliance, subnet IP utilization, and correlating load balancers to target groups and instances. AWS entities expose fields like aws.account.id, aws.region, aws.arn, and aws.vpc.id for filtering. The skill routes problem analysis, workload context, and recent AWS events queries while directing host CPU metrics to dt-obs-hosts, tracing to dt-obs-tracing, and logs to dt-obs-logs. Trigger phrases include show EC2 instances, AWS cost optimization, and security group analysis.1.4kinstalls12Dt Migrationdt-migration is a Dynatrace-for-AI skill that automatically maps every ManagementZoneDataSourceMeAttribute key from auto_tagging_attributes.md to the most appropriate field in a semantic dictionary used by AI coding agents. The mapping handles recursive tag rules where *_TAGS conditions reference other tags, and accounts for tags imported from cloud vendor labels or Kubernetes attributes in monitored environments. Fields without direct semantic equivalents are marked explicitly so agents do not invent mappings. Developers reach for dt-migration when Dynatrace auto-tagging metadata must be normalized before AI agents can reason about environments, ownership, or deployment zones during incident response or infrastructure change workflows.1.3kinstalls13Dt Obs Predictive Analyticsdt-obs-predictive-analytics is an Apache-2.0 licensed skill from dynatrace-for-ai for predictive observability inside Dynatrace environments. It applies DQL queries and Dynatrace analyzer tools—including timeseries-forecast—for forecasting future metric values, detecting trend changes, and characterizing signal behavior across hosts, services, and infrastructure. The skill organizes work into disciplines such as forecast and prediction for capacity planning and proactive alerting, plus change detection when metrics shift unexpectedly. Operators reach for dt-obs-predictive-analytics when estimating cost from growth trends, planning headroom before saturation, or investigating anomalous production signals.1.1kinstalls14Dt Obs Azuredt-obs-azure is a Dynatrace agent skill for configuring observability on Microsoft Azure environments running AI workloads. The skill guides setup so teams trace latency, surface errors, and monitor dependency health across LLM endpoints, agent runtimes, and supporting Azure services like functions, containers, and managed databases. Developers reach for it when AI features are already deployed on Azure and blind spots appear—slow inference chains, failing tool calls, or unclear cross-service bottlenecks. It focuses on Dynatrace-specific instrumentation patterns for AI pipelines rather than generic infrastructure provisioning or model training workflows.966installs15Dt Obs Gcpdt-obs-gcp is a dynatrace-for-ai skill that configures Dynatrace observability across Google Cloud Platform workloads including GKE clusters, Cloud Run services, and native GCP integrations. Developers invoke it when production or staging environments on GCP need unified dashboards, service-level objectives, and AI-specific workload telemetry beyond default Cloud Monitoring. The skill guides Dynatrace agent deployment, GCP service instrumentation, and SLO configuration so engineering teams gain full-stack visibility into containerized and serverless AI pipelines running on Google Cloud.933installs16Dt AlertingCovers end-to-end Dynatrace alerting from anomaly detector setup and model choice through Grail event storage, problem grouping, and workflow-based notifications to Slack, email, ServiceNow, or webhook. A developer uses it when configuring alerts, choosing detector types, querying alert history, or reducing alert noise.622installs17Dt Js RuntimeDocuments the built-ins, Web APIs, and Node.js compatibility modules available in the Dynatrace server-side JS runtime, plus what is unsupported. A developer uses it when writing server-side JavaScript for Dynatrace workflows or functions.582installs