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Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.

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addyosmani-agent-skills
94k25 skills4 agents9 commands1 hook
Install
$ npx -y skills add addyosmani/agent-skills --skill shipping-and-launch --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/shipping-and-launch

Context preview

The summary Claude sees to decide when to auto-load this skill.

Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.

SKILL.md

shipping-and-launch.SKILL.md
name: shipping-and-launch
description: Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.

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
  • [ ] The ecosystem's dependency audit (`npm audit`, `pip-audit`, `cargo 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)
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