spring-boot-observability
Instrument Spring Boot with Micrometer metrics, distributed tracing, and structured logging. Use when adding Micrometer metrics, distributed tracing, or structured logging to Spring Boot. (triggers: logback-spring.xml, application.properties, micrometer, tracing, correlation-id, mdc)
Best use case
spring-boot-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Instrument Spring Boot with Micrometer metrics, distributed tracing, and structured logging. Use when adding Micrometer metrics, distributed tracing, or structured logging to Spring Boot. (triggers: logback-spring.xml, application.properties, micrometer, tracing, correlation-id, mdc)
Teams using spring-boot-observability should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/spring-boot-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How spring-boot-observability Compares
| Feature / Agent | spring-boot-observability | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Instrument Spring Boot with Micrometer metrics, distributed tracing, and structured logging. Use when adding Micrometer metrics, distributed tracing, or structured logging to Spring Boot. (triggers: logback-spring.xml, application.properties, micrometer, tracing, correlation-id, mdc)
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Spring Boot Observability ## **Priority: P0** ## Implementation Workflow 1. Add tracing and metrics dependencies to `pom.xml` 2. Configure `application.yaml` for tracing, actuator, and log correlation 3. Add structured logging with MDC context 4. Verify with `curl localhost:8080/actuator/prometheus` and check trace IDs in logs ## Enable Distributed Tracing - **Correlation IDs**: Enable trace/span ID injection. - **Propagation**: Propagate context across threads (`@Async`) and clients. - **OpenTelemetry**: Use OTel bridge (`micrometer-tracing-bridge-otel`). See [implementation examples](references/implementation.md) for application.yaml tracing configuration and actuator exposure. ## Configure Structured Logging - **Format**: Use JSON logging (`logstash-logback-encoder`) in production. - **MDC**: Use MDC for contextual info (userId, tenantId). Always clear MDC in a finally block. - **Output**: Log to stdout only. Let container handle shipping. See [implementation examples](references/implementation.md) for structured logging with MDC context and Logstash encoder. ## Secure Actuator Endpoints - **Security**: Secure `/actuator/**` with Admin role. - **Probes**: Enable K8s Liveness/Readiness probes. - **Verify**: Run `curl localhost:8080/actuator/prometheus` to confirm metrics are exposed. ## Anti-Patterns - **No System.out**: Use @Slf4j for all structured logging. - **No open Actuator**: Secure /actuator/** with Admin role. - **No DIY tracing**: Use Micrometer with OTel bridge. ## References - [Implementation Examples](references/implementation.md)
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