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Production Observability

Community
rrezartprebreza
production-observability

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.

Overview

Publisherrrezartprebreza
Repositoryspring-boot-skills
Skill nameproduction-observability
Stars
322
Forks
52
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rrezartprebreza on GitHub. Read the source before you install it.

Installation

Install the Production Observability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rrezartprebreza/spring-boot-skills/tree/main/skills/spring-boot-4/production-observability
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rrezartprebreza/spring-boot-skills.git /tmp/spring-boot-skills
mkdir -p .claude/skills
cp -r /tmp/spring-boot-skills/skills/spring-boot-4/production-observability .claude/skills/production-observability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Production Observability in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Production Observability on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Production Observability is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Production Observability AI skill do?

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.

Why use Production Observability on TypingMind?

Because you install it once and use it with any model. Production Observability is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Production Observability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rrezartprebreza/spring-boot-skills/tree/main/skills/spring-boot-4/production-observability. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Production Observability?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Production Observability?

As many as you like. As long as a model supports skills, you can use Production Observability with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Production Observability AI skill free?

Yes. It is published on GitHub by rrezartprebreza under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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