Validate release readiness with evidence-based go/no-go decisions. Covers go/no-go checklists, smoke test suite design, staged rollout validation, rollback criteria and procedures, and post-deployment verification. Ensures release confidence comes from data, not feelings. Use when: "release ready,"

<objective "I think it's fine" ships the bug that the rollback you never practiced can't undo at 6 PM on a Friday. This skill turns "ready to ship" into something measurable: a go/no go checklist with evidence for each item, a sub 5 minute smoke suite, a staged rollout with metric gated promotion, rollback thresholds defined before deploy, and post deployment verification. Every section gives concrete criteria, not aspirations. </objective Discovery Questions Check .agents/qa project context.md first — if it exists, use it and skip anything answered there. Then ask only what's missing. Release cadence and process: How often do you release, and who makes the go/no go call? (Continuous/daily/weekly vs engineering lead/QA lead/release manager — sets how heavyweight the checklist should be.) Is there a release train schedule or is it ad hoc? How many environments exist between dev and production? (staging, pre prod, canary) Current state: What does the current go/no go process look like, and is it documented? Has a release ever been rolled back? How long did it take? (Reveals whether rollback is real or aspirational.) What was the last release incident and its root cause? Any release blocking bugs right now? Infrastructure and capabilities: Do you have rollback capability, and how long does a rollback take? Can you do staged/canary deployments? Do you have feature flags, and how are they managed? (Decides flag based vs infra level rollout.) What monitoring and alerting is in place? Are database migrations reversible? Team and communication: Who is on call during and after releases? How are stakeholders notified, and is there a release communication channel? How are release notes generated? Core Principles 1. Release confidence comes from evidence, not feelings "I think it's fine" is not a go/no go criterion. Evidence means: all CI pipelines green, smoke tests pass on staging, performance budgets met, no open P0/P1 bugs. If you can't point to data, you're not ready. 2. Smoke tests are the last safety net, not the only safety net Smoke tests catch catastrophic failures. They are not a substitute for thorough testing throughout the development cycle. If your smoke test suite is the only thing between you and production, you have a process problem upstream. 3. Staged rollouts reduce blast radius Deploying to 100% of users simultaneously means 100% of users are affected by any bug. Staged rollouts (canary, percentage based, ring based) let you catch issues when they affect 1% of users instead of all of them. 4. Rollback criteria must be defined BEFORE release If you wait until something is on fire to decide whether to roll back, you'll waste critical minutes debating. Define the criteria in advance, relative to baseline: "If error rate exceeds 2x baseline within 15 minutes, we roll back. No discussion needed." Tie the trigger to your DORA targets — a release whose error rate would push you past your change failure rate target, or whose recovery would blow your MTTR target, is one the rollback rule exists to stop. 5. Every release is a learning opportunity Post deployment verification isn't just about catching bugs. Track what went well, what was slow, what was stressful. Improve the process continuously. Go/No Go Checklist Use this as a template. Adapt it to your context. Every item should be verifiable with evidence, not just "I checked." Store the completed checklist as a versioned artifact (e.g. RELEASE <version .md or a tracked issue) so sign off is auditable. Automated Checks (Must Pass) [ ] All CI pipelines green — Unit tests, integration tests, E2E tests, type checking, linting [ ] Smoke test suite passes on staging — Critical user journeys verified in the staging environment [ ] No open P0/P1 bugs for this release — Check issue tracker, filter by milestone/label [ ] Performance budgets met — API response times and bundle size within thresholds; Lighthouse CI for frontend releases (skip Lighthouse for API/backend only releases — it measures page load, not service health) [ ] Security scan clean — No high/critical vulnerabilities in npm audit / Snyk / Dependabot [ ] API contract tests pass — No breaking changes to public APIs [ ] Visual regression tests pass — No unintended visual changes [ ] Accessibility checks pass — axe core scan shows no new violations Manual Checks (Verify Before Go) [ ] Feature flags reviewed — Document which flags are enabled/disabled in this release; confirm flag states for production [ ] Monitoring and alerts configured — New features have corresponding alerts (error rate, latency, business metrics) [ ] Rollback plan documented and tested — Written procedure exists; rollback has been practiced on staging [ ] Database migrations tested — Tested forward migration; backward migration verified if schema change is reversible [ ] Third party dependency changes reviewed — New or upgraded external dependencies checked for breaking changes [ ] Release notes prepared — Changelog updated, stakeholder facing summary written [ ] On call engineer identified — Named person is available and has context on the release contents [ ] Communication plan ready — Stakeholders know the release is happening; support team briefed on changes [ ] No conflicting releases — Other teams aren't deploying simultaneously [ ] Deploy window confirmed — Not deploying during peak traffic or before a weekend (unless continuous deployment) Risk Assessment [ ] Change scope categorized — Small (config change, copy update), Medium (new feature, refactor), Large (architecture change, migration) [ ] Blast radius estimated — What percentage of users could be affected if something goes wrong? [ ] Revert complexity assessed — Can this be reverted in <5 minutes? Does reverting require a data migration? Smoke Test Suite Design What to Include Smoke tests cover critical user journeys only . If these fail, the application is fundamentally broken. Typical smoke test suite (5 8 tests): 1. Application health — Homepage loads, returns 200, no JavaScript errors in console 2. Authentication — User can log in with valid credentials, session is established 3. Core workflow — The primary value delivering action works (e.g., create a document, submit a form, add to cart or complete a purchase flow) 4. Data retrieval — Key data loads correctly (dashboard populates, search returns results, product page loads) 5. Payment/transaction (if applicable) — Payment flow completes with test credentials 6. API health — Primary API endpoints return valid responses with correct schemas 7. Navigation — Critical navigation paths work (deep links, redirects, menu items) 8. Error handling — Application shows a user friendly error page for invalid routes (404) What NOT to Include Edge cases (those belong in regression tests) Visual perfection (that belongs in visual regression tests) Performance benchmarks (that belongs in performance tests) Exhaustive form validation (that belongs in unit/integration tests) Keeping It Fast Target: under 5 minutes for the entire smoke suite. Run tests in parallel where possible Use API calls instead of UI interactions for setup (create test user via API, not through registration form) Skip non critical assertions (don't check exact copy text, check that elements exist) Use a dedicated test account with pre created data (don't create data from scratch each run) Avoid unnecessary waits — use smart waiting (wait for element, not sleep(3000) / waitForTimeout ) Environment Specific Smoke Tests Staging smoke tests: Full smoke suite (all 5 8 tests) Can use test payment providers Can test with feature flags in upcoming release configuration Can test database migrations Production smoke tests: Subset of staging smoke tests (3 5 tests) Use synthetic test accounts (clearly labeled, won't affect analytics) Never test with real payment transactions (use sandbox mode or skip) Focus on: app loads, auth works, core read operations work, API responds Post deployment smoke tests: Run immediately after deploy completes (within 60 seconds) Same as production smoke tests If any fail, trigger alert and begin rollback evaluation Staging is not production: it has different data volumes, traffic patterns, third party configurations, and infrastructure scale. That gap is exactly why production and post deployment smoke tests exist on top of staging verification. Staged Rollout Validation Rollout Stages A typical staged rollout. The same ladder expressed for flag based rollouts adds a 25% step (see below): Stage Traffic % Duration Purpose Canary 1% 15 30 min Catch crashes, exceptions, obvious failures Early adopters 10% 1 2 hours Validate error rates, latency, business metrics Partial rollout 25 50% 2 4 hours Confirm stability at scale Full rollout 100% — Monitor for 24 hours post deployment What to Monitor Between Stages Before promoting to the next stage, verify all of these: Error metrics: Error rate (HTTP 5xx) is not higher than baseline Exception count is not higher than baseline No new error types appearing in logs Performance metrics: P50 and P95 latency are within acceptable range (relative to baseline, not an absolute ceiling) No increase in timeout errors Database query times are stable Business metrics: Conversion rate is not dropping User engagement (page views, actions) is stable Revenue/transaction volume is normal (if applicable) Infrastructure metrics: CPU and memory usage are normal No increase in queue depth or message backlog No disk space issues from new logging Automated Promotion Criteria Define rules for automatic promotion between stages. Each gate combines an error rate ceiling, a latency ceiling expressed relative to baseline , a stability window, and (at higher stages) business metric guardrails. See references/rollout automation.md for the full canary→10%→50%→100% promotion ruleset. Feature Flag Gradual Rollout An alternative to infrastructure level canary deploys: 1. Deploy new code to 100% with the feature flag OFF 2. Enable the flag for internal users first (dogfooding) 3. Enable for 1% of users (canary equivalent) 4. Gradually increase: 10%, 25%, 50%, 100% 5. Remove the flag after full rollout is stable for 1 week Advantages: Faster rollback (just flip the flag), no infrastructure changes, can target specific user segments. Disadvantages: Code complexity (branching logic), stale flags become tech debt, doesn't catch infrastructure issues. Tooling Platform Best at Notes LaunchDarkly Enterprise scale; Guarded Rollouts (auto canary analysis, GA since May 2025); AI Configs for prompt/model rollouts; agent graphs Acquired Highlight.io in 2025 — also offers observability tied to flags Statsig Experiment first culture; Switchback experiments (Feb 2026 update) for two sided marketplaces; auto tune Acquired by OpenAI Sept 2025; still operates independently as of mid 2026, but weigh acquisition/roadmap risk before a multi year infrastructure bet GrowthBook OSS first; stale flag detection with code reference scanning; SQL based experimentation Strong fit when you want to self host and avoid vendor lock in Unleash OSS, GitOps style flag definition, environment scoping Apache 2 license; Enterprise tier for SSO/audit Flagsmith Kill switches as first class concept; canary alerts; OSS option Published "what is a kill switch" + "release testing" guides 2026 Harness FME (formerly Split) Targeted rollouts + monitoring tied to deploy pipelines; warehouse native experimentation + flag archiving (2026) Rebranded after Harness acquisition Vendor

release-readiness