shipping-and-launch – skill Marketing & SEO cho agent AI

Supporting material availability

This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files:

• <support-root>/references/definition-of-done.md
• <support-root>/references/security-checklist.md
• <support-root>/references/performance-checklist.md
• <support-root>/references/accessibility-checklist.md

Obtain the required material from the [declared upstream source](https://github.com/addyosmani/agent-skills), or from a user-provided installation of that source. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable.

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)

Treat the rollout percentages, windows and response-time targets below as examples requiring workload-specific SLOs and sufficient sample sizes. Automated scanners do not establish full security or accessibility compliance. A feature flag changes exposure; it cannot undo persisted data, sent notifications or third-party effects.

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<React.PropsWithChildren, { hasError: boolean }> {
  state = { hasError: false };

  static getDerivedStateFromError() {
    return { hasError: true };
  }
  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,
    path: req.path, // omit query parameters that may contain secrets
    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. Redeploy a known-good artifact through the approved deployment workflow; a Git revert alone does not deploy
2. Veri

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