Production readiness review
Skill Amey-Thakur/AI-SKILLS/skills/big-tech-processes/production-readiness-review
Run a production readiness review that checks SLOs, runbooks, capacity, and dependencies before an on-call team agrees to own a service. Use when a service is about to be onboarded to a support rotation or handed from its builders to operators.From its SKILL.md
npx -y skills add Amey-Thakur/AI-SKILLS --skill production-readiness-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 26 days oldThe repository was created 26 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
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Production readiness review
The PRR is Google SRE's gate for deciding whether a service is fit to be operated by people who did not build it. It exists because the failure modes that page an engineer at 3 a.m. are rarely visible from the code: they live in missing alerts, untested recovery, and a dependency the team forgot it had. Skip the PRR and the on-call team inherits every one of those surprises, one incident at a time.
Method
- Define SLOs and the error budget before anything else. Write the service level objectives as measurable indicators: request success rate, latency at the 99th percentile, freshness. Without an agreed budget there is no line between healthy and broken, and every alert becomes a judgment call at the worst possible hour.
- Demand runbooks for each alert, not a wiki of prose. Every page a responder can receive needs a runbook with symptoms, diagnosis steps, and the exact remediation. A runbook that says "investigate the issue" is a blank page with a title; test it by having someone off the team follow it cold.
- Audit every dependency and its failure behavior. List the databases, queues, and upstream services this one calls, each one's own SLO, and what happens when it is slow or down. Confirm timeouts, retries with backoff, and circuit breakers exist, so a dependency's bad day does not become a full outage here.
- Prove capacity against real load with headroom. Show load-test numbers, the current utilization, the autoscaling limits, and the quota ceilings. State the traffic multiple the service survives before it falls over, and confirm the plan for the next growth step rather than assuming linear scaling holds.
- Verify observability covers the SLOs. Dashboards for the golden signals, alerts wired to the error budget, structured logs, and distributed tracing across the request path. If an SLO can be violated without a page firing, the monitoring is decorative.
- Rehearse failure and recovery, then agree on ownership. Run a rollback, a failover, and a data-restore drill, and record the time each took. Close the PRR with the on-call team explicitly accepting the service, or listing the blockers that must clear before they will.
Checks
- Can a responder who has never seen this service resolve its top alert from the runbook alone?
- Does every hard dependency have a defined behavior for when it fails, not just for when it works?
- Has the recovery path been drilled and timed, or only diagrammed?
Boundaries
A PRR judges whether a service can be operated, not whether one release should ship: the go decision for a specific launch belongs to the launch-review skill. Scale the depth to the risk, follow your SRE org's PRR template where one exists, and route the incidents it fails to prevent into a postmortem.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.