shipping-and-launch

SKILL.md

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.)

 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:

// Feature flag check
const flags = await getFeatureFlags(userId);

if (flags.taskSharing) {
  // New feature: task sharing
  return <TaskSharingPanel task={task} />;
}

// Default: existing behavior
return null;

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

Rules:

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

1. DEPLOY to staging
   └── 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)
   └── Monitor for 1 week
   └── 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

Application metrics:
├── Error rate (total and by endpoint)
├── Response time (p50, p95, p99)
├── Request volume
├── Active users
└── Key business metrics (conversion, engagement)

Infrastructure metrics:
├── CPU and memory utilization
├── Database connection pool usage
├── Disk space
├── Network latency
└── Queue depth (if applicable)

Client metrics:
├── Core Web Vitals (LCP, INP, CLS)
├── JavaScript errors
├── API error rates from client perspective
└── Page load time

Error Reporting

// Set up error boundary with reporting
class ErrorBoundary extends React.Component {
  componentDidCatch(error: Error, info: React.ErrorInfo) {
    // Report to error tracking service
    reportError(error, {
      componentStack: info.componentStack,
      userId: getCurrentUser()?.id,
      page: window.location.pathname,
    });
  }

  render() {
    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) => {
  reportError(err, {
    method: req.method,
    url: req.url,
    userId: req.user?.id,
  });

  // Don't expose internals to users
  res.status(500).json({
    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)
   OR
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 the project-wide Definition of Done that every change must clear before this checklist, see references/definition-of-done.md

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

Before deploying:

 Pre-launch checklist completed (all sections green)

 Feature flag configured (if applicable)

 Rollback plan documented

 Monitoring dashboards set up

 Team notified of deployment

After deploying:

 Health check returns 200

 Error rate is normal

 Latency is normal

 Critical user flow works

 Logs are flowing

 Rollback tested or verified ready