182-java-observability-metrics-micrometer
Use this skill when implementing or enhancing Java metrics instrumentation with Micrometer. Apply it to define service-level metrics, select appropriate meter types (Counter, Timer, Gauge, DistributionSummary), enforce low-cardinality tagging conventions, configure Prometheus or OpenTelemetry export, and validate metrics through testing. Triggers for requests like "Improve metrics," "Add metrics observability," or "Refactor Micrometer instrumentation" in Java applications requiring production-ready observability.
git clone --depth 1 https://github.com/jabrena/cursor-rules-java /tmp/182-java-observability-metrics-micrometer && cp -r /tmp/182-java-observability-metrics-micrometer/skills/182-java-observability-metrics-micrometer ~/.claude/skills/182-java-observability-metrics-micrometerSKILL.md
# Java Metrics Observability with Micrometer Implement effective Java metrics instrumentation with Micrometer by defining meaningful service-level metrics, controlling cardinality, selecting the right meter type, and exposing production-ready telemetry for dashboards and alerting. **What is covered in this Skill?** - Metrics-first observability with Micrometer in Java applications - Meter selection: Counter, Timer, DistributionSummary, Gauge, LongTaskTimer - Naming and tagging conventions with low-cardinality dimensions - Cardinality and meter lifecycle safeguards to prevent time-series explosion - Histogram/percentile strategy and SLO-oriented metrics design - Integration guidance for Actuator + Prometheus/OpenTelemetry pipelines - Testing and verification of metrics registration and values **Scope:** Application-level metrics design and instrumentation quality for Java services, with emphasis on operationally useful and cost-efficient telemetry. ## Constraints Metrics instrumentation must be operationally safe, low-cardinality, and validated. Poor tag design or excessive meter creation can degrade observability systems and increase costs. - **LOW CARDINALITY FIRST**: Never tag metrics with unbounded values (userId, UUID, raw URL, full exception message) - **RIGHT METER TYPE**: Use Counter for monotonically increasing events, Timer for latency, Gauge for point-in-time state, and DistributionSummary for sampled values - **BEFORE APPLYING**: Read the reference for good/bad instrumentation examples and anti-patterns - **VERIFY**: Run `./mvnw clean verify` or `mvn clean verify` after changes ## When to use this skill - Improve metrics - Apply Micrometer - Add metrics observability - Refactor Micrometer instrumentation ## Workflow 1. **Define measurement goals and meter contract** Identify key service indicators (throughput, latency, error ratio, saturation) and map each to stable metric names, units, and low-cardinality tags. 2. **Select meter types and instrument code paths** Apply Counter/Timer/Gauge/DistributionSummary/LongTaskTimer where appropriate, ensuring consistent naming conventions and reusable tags. 3. **Harden instrumentation for production** Control cardinality, avoid dynamic meter churn, configure histogram/percentile strategy only where needed, and align export settings with the telemetry backend. 4. **Validate and operationalize metrics** Verify metrics in tests and runtime endpoints, confirm expected labels/units, and ensure dashboards/alerts can consume the emitted series. ## Reference For detailed guidance, examples, and constraints, see [references/182-java-observability-metrics-micrometer.md](references/182-java-observability-metrics-micrometer.md).
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