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shipping-and-launch | Apiara Skills
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Skill
shipping-and-launch Prepares production launches. Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
Overview # Shipping and Launch
Overview Ship with confidence. The goal is not just to deploy — it's to deploy safely, with monitoring in place, a rollback plan ready, and a clear understanding of what success looks like. Every launch should be reversible, observable, and incremental.
When to Use Deploying a feature to production for the first time Releasing a significant change to users Migrating data or infrastructure Opening a beta or early access program Any deployment that carries risk (all of them) The Pre-Launch Checklist Code Quality [ ] All tests pass (unit, integration, e2e) [ ] Build succeeds with no warnings [ ] Lint and type checking pass [ ] Code reviewed and approved [ ] No TODO comments that should be resolved before launch [ ] No `console.log` debugging statements in production code [ ] Error handling covers expected failure modes Security [ ] No secrets in code or version control [ ] `npm audit` shows no critical or high vulnerabilities [ ] Input validation on all user-facing endpoints [ ] Authentication and authorization checks in place [ ] Security headers configured (CSP, HSTS, etc.)
Validate with a real task Run one small real task before keeping it in your long-term workflow.
[ ] Rate limiting on authentication endpoints
[ ] CORS configured to specific origins (not wildcard)
Performance [ ] Core Web Vitals within "Good" thresholds [ ] No N+1 queries in critical paths [ ] Images optimized (compression, responsive sizes, lazy loading) [ ] Bundle size within budget [ ] Database queries have appropriate indexes [ ] Caching configured for static assets and repeated queries
Accessibility [ ] Keyboard navigation works for all interactive elements [ ] Screen reader can convey page content and structure [ ] Color contrast meets WCAG 2.1 AA (4.5:1 for text) [ ] Focus management correct for modals and dynamic content [ ] Error messages are descriptive and associated with form fields [ ] No accessibility warnings in axe-core or Lighthouse
Infrastructure [ ] Environment variables set in production [ ] Database migrations applied (or ready to apply) [ ] DNS and SSL configured [ ] CDN configured for static assets [ ] Logging and error reporting configured [ ] Health check endpoint exists and responds
Documentation [ ] README updated with any new setup requirements [ ] API documentation current [ ] ADRs written for any architectural decisions [ ] Changelog updated [ ] User-facing documentation updated (if applicable)
Feature Flag Strategy Ship behind feature flags to decouple deployment from release:
const flags = await getFeatureFlags(userId);
// New feature: task sharing
return <TaskSharingPanel task={task} />;
// Default: existing behavior
**Feature flag lifecycle:**
1. DEPLOY with flag OFF → Code is in production but inactive
2. ENABLE for team/beta → Internal testing in production environment
3. GRADUAL ROLLOUT → 5% → 25% → 50% → 100% of users
4. MONITOR at each stage → Watch error rates, performance, user feedback
5. CLEAN UP → Remove flag and dead code path after full rollout
Every feature flag has an owner and an expiration date Clean up flags within 2 weeks of full rollout Don't nest feature flags (creates exponential combinations) Test both flag states (on and off) in CI
Staged Rollout
The Rollout Sequence └── Full test suite in staging environment
└── Manual smoke test of critical flows
2. DEPLOY to production (feature flag OFF)
└── Verify deployment succeeded (health check)
└── Check error monitoring (no new errors)
3. ENABLE for team (flag ON for internal users)
└── Team uses the feature in production
└── 24-hour monitoring window
4. CANARY rollout (flag ON for 5% of users)
└── Monitor error rates, latency, user behavior
└── Compare metrics: canary vs. baseline
└── 24-48 hour monitoring window
└── Advance only if all thresholds pass (see table below)
5. GRADUAL increase (25% -> 50% -> 100%)
└── Same monitoring at each step
└── Ability to roll back to previous percentage at any point
6. FULL rollout (flag ON for all users)
└── Clean up feature flag
Rollout Decision Thresholds Use these thresholds to decide whether to advance, hold, or roll back at each stage:
| Metric | Advance (green) | Hold and investigate (yellow) | Roll back (red) |
|--------|-----------------|-------------------------------|-----------------|
| Error rate | Within 10% of baseline | 10-100% above baseline | >2x baseline |
| P95 latency | Within 20% of baseline | 20-50% above baseline | >50% above baseline |
| Client JS errors | No new error types | New errors at <0.1% of sessions | New errors at >0.1% of sessions |
| Business metrics | Neutral or positive | Decline <5% (may be noise) | Decline >5% |
When to Roll Back Roll back immediately if:
Error rate increases by more than 2x baseline P95 latency increases by more than 50% User-reported issues spike Data integrity issues detected Security vulnerability discovered
Monitoring and Observability
What to Monitor ├── Error rate (total and by endpoint)
├── Response time (p50, p95, p99)
└── Key business metrics (conversion, engagement)
├── CPU and memory utilization
├── Database connection pool usage
└── Queue depth (if applicable)
├── Core Web Vitals (LCP, INP, CLS)
├── API error rates from client perspective
Error Reporting // Set up error boundary with reporting
class ErrorBoundary extends React.Component {
componentDidCatch(error: Error, info: React.ErrorInfo) {
// Report to error tracking service
componentStack: info.componentStack,
userId: getCurrentUser()?.id,
page: window.location.pathname,
if (this.state.hasError) {
return <ErrorFallback onRetry={() => this.setState({ hasError: false })} />;
return this.props.children;
// Server-side error reporting
app.use((err: Error, req: Request, res: Response, next: NextFunction) => {
// Don't expose internals to users
error: { code: 'INTERNAL_ERROR', message: 'Something went wrong' },
Post-Launch Verification In the first hour after launch:
1. Check health endpoint returns 200
2. Check error monitoring dashboard (no new error types)
3. Check latency dashboard (no regression)
4. Test the critical user flow manually
5. Verify logs are flowing and readable
6. Confirm rollback mechanism works (dry run if possible)
Rollback Strategy Every deployment needs a rollback plan before it happens:
Rollback Plan for [Feature/Release]
Trigger Conditions Error rate > 2x baseline P95 latency > [X]ms User reports of [specific issue]
Rollback Steps 1. Disable feature flag (if applicable)
1. Deploy previous version: `git revert <commit> && git push`
2. Verify rollback: health check, error monitoring
3. Communicate: notify team of rollback
Database Considerations Migration [X] has a rollback: `npx prisma migrate rollback` Data inserted by new feature: [preserved / cleaned up]
Time to Rollback Feature flag: < 1 minute Redeploy previous version: < 5 minutes Database rollback: < 15 minutes
See Also For security pre-launch checks, see `references/security-checklist.md` For performance pre-launch checklist, see `references/performance-checklist.md` For accessibility verification before launch, see `references/accessibility-checklist.md`
Common Rationalizations | Rationalization | Reality |
| "It works in staging, it'll work in production" | Production has different data, traffic patterns, and edge cases. Monitor after deploy. |
| "We don't need feature flags for this" | Every feature benefits from a kill switch. Even "simple" changes can break things. |
| "Monitoring is overhead" | Not having monitoring means you discover problems from user complaints instead of dashboards. |
| "We'll add monitoring later" | Add it before launch. You can't debug what you can't see. |
| "Rolling back is admitting failure" | Rolling back is responsible engineering. Shipping a broken feature is the failure. |
Red Flags Deploying without a rollback plan No monitoring or error reporting in production Big-bang releases (everything at once, no staging) Feature flags with no expiration or owner No one monitoring the deploy for the first hour Production environment configuration done by memory, not code "It's Friday afternoon, let's ship it"
Verification [ ] Pre-launch checklist completed (all sections green) [ ] Feature flag configured (if applicable) [ ] Rollback plan documented [ ] Monitoring dashboards set up [ ] Team notified of deployment [ ] Health check returns 200 [ ] Error rate is normal [ ] Latency is normal [ ] Critical user flow works [ ] Logs are flowing [ ] Rollback tested or verified ready 