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caiaffacaiaffa/claude-code-ultimate-engineering-system/skills/production-readiness/SKILL.md

production-readiness

Skill17 starsChanged 59 days agoMITView on GitHub

Assess whether a service is ready for production and operable at scale — reliability, capacity, infrastructure (Kubernetes/AWS), and operational excellence. Use before launch and in periodic service reviews.

17 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use testing and handoff.

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.
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The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 17 edited versions we read keep these.

  • 4

    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:

    Verify a service can be run, scaled, and recovered in production — not just that the code works.

    line 8
  2. Distinctive:

    Before launching a new service or major feature.

    line 11
  3. Distinctive:

    Every service has a runbook.

    line 19
  4. Distinctive:

    No runbook. Alert with no action.

    line 42

“Common” is measured: kept by at least 6 in 10 of the 17 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

Mission

Verify a service can be run, scaled, and recovered in production — not just that the code works.

When to use

  • Before launching a new service or major feature.
  • Periodic service health / scorecard review.

Handoff

  • Receives from: release-planning or reliability-engineer.
  • Hands off to: reliability-engineer (gaps with severity).

Operational excellence

  • Every service has a runbook. Every alert maps to an action.
  • Health checks: liveness and readiness, distinct and meaningful.
  • Graceful shutdown and startup ordering handled.
  • Configuration via environment, secrets never in code.
  • SLIs/SLOs defined with concrete numbers (see observability skill).

Capacity & scaling

  • Known load profile: expected and peak RPS, growth rate.
  • Resource requests/limits set from measured usage, not guesses.
  • Autoscaling signal is the real bottleneck (CPU, queue depth), not a proxy.
  • A failure-mode plan for the dependency that saturates first.

Infrastructure (Kubernetes / AWS)

  • Pods: requests/limits set; no single point of failure; PodDisruptionBudget for critical workloads.
  • Rollout strategy is safe (rolling/canary); probes tuned so a slow start isn't killed.
  • IAM least-privilege; no wildcard permissions.
  • Stateful dependencies (DB, cache, queue) have defined failover behavior.
  • Cost: right-sized instances, no idle over-provisioning (cross-check engineering-economics).

Service scorecard

Evaluate against ~/.claude/engineering/SERVICE_SCORECARD.md section by section; report each as pass / gap / blocker.

Red flags

  • No runbook. Alert with no action. Resource limits unset.
  • Autoscaling on a proxy metric. Wildcard IAM. No failover plan for a stateful dependency.

Output

# PRODUCTION READINESS
Overall: READY | READY WITH GAPS | NOT READY
Gaps by severity (blocker / high / medium) with specific fixes
Scorecard section results

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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.

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