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rrezartprebrezarrezartprebreza/spring-boot-skills/skills/spring-boot-3/production-observability/SKILL.md

production-observability

Skill344 starsChanged 3 months agoMITView on GitHub

Use when adding service-wide Spring Boot 3 Actuator, Micrometer metrics, distributed tracing, OpenTelemetry export, structured logging, health probes, dashboards, or alerts. Combine with ai-observability only for Spring AI-specific token, prompt, and model telemetry.

2 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 8 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈8

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Add Actuator and one metrics registry selected by the deployment platform.

    line 15
  2. Distinctive:

    Use Micrometer Observation for application metrics and traces.

    line 16
  3. Distinctive:

    Use Micrometer Tracing with the chosen bridge.

    line 17
  4. Distinctive:

    Keep metric tags low-cardinality: method, outcome, region, or bounded status.

    line 24
  5. Distinctive:

    Put request IDs, user IDs, order IDs, and exception messages only in traces or logs.

    line 25
  6. Distinctive:

    Keep liveness independent from remote systems so a dependency outage does not restart every pod.

    line 31
  7. Distinctive:

    Put required dependencies in readiness groups.

    line 32
  8. Distinctive:

    Write custom health indicators only for dependencies that affect traffic acceptance.

    line 33

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Production Observability

Instrument user-visible operations and operational boundaries, not every method.

Baseline

  • Add Actuator and one metrics registry selected by the deployment platform.
  • Use Micrometer Observation for application metrics and traces.
  • Use Micrometer Tracing with the chosen bridge; do not mix tracing APIs throughout business code.
  • Export through OTLP when the platform standardizes on OpenTelemetry collectors.
  • Expose only required actuator endpoints and secure every non-public endpoint.

Observation conventions

  • Name observations by stable operation, such as orders.create.
  • Keep metric tags low-cardinality: method, outcome, region, or bounded status.
  • Put request IDs, user IDs, order IDs, and exception messages only in traces or logs.
  • Propagate context across @Async, executor, and Reactor boundaries.
  • Record latency, throughput, failures, and saturation for every external dependency.

Health and readiness

  • Keep liveness independent from remote systems so a dependency outage does not restart every pod.
  • Put required dependencies in readiness groups.
  • Write custom health indicators only for dependencies that affect traffic acceptance.
  • Set explicit timeouts on health checks.

Logging and alerts

  • Emit structured logs with trace and span correlation.
  • Redact credentials, tokens, personal data, prompts, and payloads by default.
  • Alert on symptoms tied to service objectives, not raw metric noise.
  • Include runbook links and enough dimensions to identify the affected service and dependency.

Examples

  • See examples/good-observation.java and examples/bad-observation.java.

Gotchas

  • Agent tags metrics with user or entity IDs - this creates unbounded cardinality.
  • Agent exposes every actuator endpoint publicly - expose the minimum and secure it.
  • Agent makes liveness depend on the database - dependency outages then cause restart loops.
  • Agent logs request bodies and tokens for debugging - redact sensitive data before emission.
  • Agent creates spans but loses context in async work - configure context propagation explicitly.

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