Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rrezartprebreza/spring-boot-skills --skill production-observabilitygit clone --depth 1 https://github.com/rrezartprebreza/spring-boot-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rrezartprebreza/spring-boot-skills/production-observability)<a href="https://agentmods.dev/skills/rrezartprebreza/spring-boot-skills/production-observability"><img src="https://agentmods.dev/badge/skills/rrezartprebreza/spring-boot-skills/production-observability/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rrezartprebreza/spring-boot-skills/production-observability"><img src="https://agentmods.dev/badge/skills/rrezartprebreza/spring-boot-skills/production-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.00472 |
| Opus 5 | $0.00029 | $0.00236 |
| Sonnet 5 | $0.00012 | $0.00094 |
| Haiku 4.5 | $0.00006 | $0.00047 |
Grade A, and why
production-observability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.javaandexamples/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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 54 lines · 58 tokens per session scan A 0ad170439cc6
production-observability is a skill published in the GitHub repository rrezartprebreza/spring-boot-skills (260 stars, last pushed 4d ago), licensed MIT. It adds 58 tokens to every session and 472 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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