agentleFS
rrezartprebrezarrezartprebreza/spring-boot-skills/skills/spring-boot-4/production-observability/SKILL.md

production-observability

Skill344 starsChanged 3 months agoMITView on GitHub

Use when adding service-wide Spring Boot 4 Actuator, Micrometer Observation and Tracing, OpenTelemetry or OTLP 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.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 8 are distinctive, and 1 is 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.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Add the Boot 4 Actuator starter and one metrics registry selected by the platform.

    line 15
  2. Distinctive:

    Use Micrometer Observation for application metrics and traces.

    line 18
  3. Distinctive:

    Prefer Micrometer APIs over direct OpenTelemetry APIs in application code.

    line 19
  4. Distinctive:

    Use low-cardinality key values for metrics and high-cardinality values only for traces.

    line 26
  5. Distinctive:

    Never tag metrics with user IDs, entity IDs, URLs containing IDs, or exception messages.

    line 27
  6. Very specific:

    Avoid annotating already instrumented MVC controllers or repositories with @Observed.

    line 30
  7. Distinctive:

    Keep liveness independent from remote dependencies.

    line 34
  8. Distinctive:

    Put traffic-critical dependencies in readiness groups.

    line 35
  9. Distinctive:

    Keep sensitive health details hidden from unauthenticated callers.

    line 37

≈ “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 the Boot 4 Actuator starter and one metrics registry selected by the platform.
  • For OpenTelemetry tracing over OTLP, use spring-boot-starter-opentelemetry and Boot's management.opentelemetry.tracing.export.otlp.* properties.
  • Use Micrometer Observation for application metrics and traces.
  • Prefer Micrometer APIs over direct OpenTelemetry APIs in application code.
  • Export metrics and traces through OTLP when an OpenTelemetry collector is the platform boundary.
  • Expose only required actuator endpoints and secure non-public endpoints.

Observation conventions

  • Name observations by stable operation, such as orders.create.
  • Use low-cardinality key values for metrics and high-cardinality values only for traces.
  • Never tag metrics with user IDs, entity IDs, URLs containing IDs, or exception messages.
  • Register a ContextPropagatingTaskDecorator for async executor boundaries.
  • Enable Reactor automatic context propagation only deliberately and test it.
  • Avoid annotating already instrumented MVC controllers or repositories with @Observed.

Health and readiness

  • Keep liveness independent from remote dependencies.
  • Put traffic-critical dependencies in readiness groups.
  • Give custom health checks strict timeouts and stable detail keys.
  • Keep sensitive health details hidden from unauthenticated callers.

Logging and alerts

  • Emit structured logs correlated with trace and span IDs.
  • Redact credentials, tokens, personal data, prompts, and request bodies by default.
  • Alert on service-objective symptoms: error ratio, latency, saturation, or queue lag.
  • Include a runbook and service/dependency identity in every actionable alert.

Examples

  • See examples/good-observation.java, examples/good-observability.yml, and examples/bad-observation.java.

Official sources

Gotchas

  • Agent uses high-cardinality IDs as metric tags - reserve them for traces or logs.
  • Agent directly configures an OpenTelemetry SDK and disables Boot integration accidentally - prefer Boot and Micrometer support.
  • Agent exposes all actuator endpoints - expose the minimum and secure them.
  • Agent ties liveness to a database or broker - dependency outages can trigger restart loops.
  • Agent loses trace context in @Async work - register context propagation explicitly.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.