dapr-observability-setup
Configure OpenTelemetry tracing, metrics, and structured logging for DAPR applications. Integrates with Azure Monitor, Jaeger, Prometheus, and other observability backends.
Configure OpenTelemetry tracing, metrics, and structured logging for DAPR applications. Integrates with Azure Monitor, Jaeger, Prometheus, and other observability backends.
Customize and test Grok parsing for USP, Cloud Sensor, and External adapters. Helps generate parsing rules from sample logs, validate against test data, and deploy configurations. Use when setting up new log sources, troubleshooting parsing issues, or modifying field extraction for adapters.
Enforces project server-side Sentry monitoring conventions when implementing error tracking, performance monitoring, and breadcrumb logging in server actions, facades, middleware, and API routes. This skill ensures consistent patterns for context setting, breadcrumb categories, error capture, and performance spans.
Enterprise Application Monitoring with AI-powered observability architecture, Context7 integration, and intelligent performance orchestration for scalable modern applications
Structured logging, metrics, distributed tracing, and alerting strategies
Deploy serverless workloads with Knative Serving for scale-to-zero and autoscaling. Use for creating Knative Services, configuring autoscaling, traffic splitting, and revisions. Triggers on "knative service", "scale-to-zero", "serverless deployment", "ksvc", "knative autoscaling", "traffic splitting", or when deploying agents as serverless workloads.
Implement OpenTelemetry-based observability for traces, metrics, and logs. Use for distributed tracing, Prometheus metrics, structured logging, and agent monitoring. Triggers on "OpenTelemetry", "OTEL", "tracing", "metrics", "observability", "Prometheus", "Grafana", "distributed tracing", or when implementing spec/007-observability.md.
Observability stack with Prometheus, Grafana, and alerting.
Use when adding logging to features - structured logging with LoggerService categories
Use when adding logging, error monitoring, metrics, Sentry, debugging production issues, or improving observability
Mandatory incident fix verification with observables. Invoke after: applying production fixes, before declaring incidents resolved, when someone says 'I think that fixed it'. Requires log entries, metric changes, and database state confirmation.
Wrap errors with context using fmt.Errorf %w pattern
Manage production incidents with structured response, debugging, and post-mortem documentation
Define package-level sentinel errors using errors.New
Production observability with tracing, OpenTelemetry, and Prometheus metrics including structured logging, instrumented functions, distributed tracing, health checks, and request correlation. Use when adding logging, metrics, tracing, or health endpoints to Rust services.
Display routine measurement dashboard with metrics, costs, trends, and visualization
Use when implementing Go error handling with error wrapping, sentinel errors, and custom error types. Use when handling errors in Go applications.
Posts metrics data to a reporting endpoint. Use this when users need to send metrics data that has been collected and stored in temporary files to a reporting API.
Automatically validate DAPR configuration files (dapr.yaml, component YAML files) when they are created or modified. Checks for schema compliance, missing required fields, invalid values, and common misconfigurations. Use when validating DAPR configs, reviewing component setup, or before deployment.
Enterprise Go for systems and network programming Go 1.25.4, Fiber v3, gRPC, context patterns, goroutine orchestration, standard library mastery; activates for REST APIs, microservices, concurrent systems, backend infrastructure, and performance-critical code.
Analyze percentile metrics (tdigest type) using OPAL for latency analysis and SLO tracking. Use when calculating p50, p95, p99 from pre-aggregated duration or latency metrics. Covers the critical double-combine pattern with align + m_tdigest() + tdigest_combine + aggregate. For simple metrics (counts, averages), see aggregating-gauge-metrics skill.
Grafana dashboard management, visualization, and alerting