agentleFS
int2t05int2t05/engineering-skills/skills/09-operate/observability/SKILL.md

observability

Skill3 starsChanged 29 days agoMITView on GitHub

Instrument service behavior and verify operators can diagnose it. Triggers on "add logging", "加日志".

87 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use gitops, background-jobs and 8 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • kcns008Skill

    gitops

    GitOps Agent (Flow) — manages ArgoCD applications, Helm charts, Kustomize overlays, deployment strategies (canary, blue-green, rolling), multi-cluster GitOps, and drift detection for Kubernetes and OpenShift clusters.

    Also in 4 projects that use this

  • YepAPISkill

    background-jobs

    Queue workers with BullMQ/Inngest/Trigger.dev, job retries, dead letter queues, concurrency.

    Also in 4 projects that use this

  • Mushfiqur-Rahman-RobinSkill

    rag-systems

    Best practices for building Retrieval-Augmented Generation systems — chunking, embedding, hybrid retrieval, reranking, indexing, caching, evaluation, observability — plus a catalog of RAG patterns and when each applies.

    Also in 3 projects that use this

  • YepAPISkill

    data-pipelines

    ETL patterns, cron scheduling, queue-based processing, and batch jobs with Inngest/BullMQ.

    Also in 4 projects that use this

  • wpankSkill

    caching

    Caching strategies, invalidation, eviction policies, HTTP caching, distributed caching, and anti-patterns. Use when designing cache layers, choosing eviction policies, debugging stale data, or optimizing read-heavy workloads.

    Also in 5 projects that use this

  • MicrosoftSkill

    data-modeling

    Use when the app needs to store or read data — records, a database, entities, CRUD, lists, "save/remember X", or per-user data with row-level security. This app's data layer is Rayfin's: you declare entities as TypeScript classes with decorators in rayfin/data/, register them in the schema, and read/write them through the typed rayfin-client. Covers: defining an @entity, field decorators, registering it, per-entity access with @role, owner-only row-level security, and the client query/mutation API. Triggers: data, database, model, entity, schema, table, record, CRUD, create, read, update, delete, list, store, save, persist, per-user, row-level security, RLS, access control, ownership.

    Also in 6 projects that use this

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 70 edited versions we read keep these.

  • 1

    Distinctive

    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 redacted structured logs, bounded metric labels and meaningful correlated spans using existing tooling.

    line 18

“Common” is measured: kept by at least 6 in 10 of the 70 edited versions of this file we could read. “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

Observability and Instrumentation

When to use

  • Instrument service behavior and verify operators can diagnose it.

Not for: incident-response for active incidents.

Steps

  1. Identify operational questions, critical flows and service objectives.
  2. Add redacted structured logs, bounded metric labels and meaningful correlated spans using existing tooling.
  3. Configure actionable symptom alerts with measured or requirement-based thresholds and a runbook: meaning, first query, recovery and escalation.
  4. Generate controlled traffic and failures, confirm actual signals and alert behavior and restore temporary settings.

Verify

  • Telemetry traces real success and failure without secrets; unavailable signal or delivery checks are explicit.

References

  • engineering-principles.md — shared decision boundaries, scope and verification.
  • references/observability-checklist.md — at-a-glance instrumentation checklist + pre-launch gate
  • references/slo-methodology.md — SLI definition, error budgets, burn-rate alerting, SLO-based release gating
  • references/cost-observability.md — FinOps: cost attribution, per-request cost, anomaly detection, optimization levers
  • runbook-template.md — runbook authoring structure (write when you create the alert, not during the incident)
  • references/slo-implementation.md — Prometheus recording/alerting rules, multi-window burn-rate alerts, error-budget queries, dashboards, review cadence

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.