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Agent skill

Shipping and Launch Checklist

by addyosmani in addyosmani/agent-skills

Prepares a production launch with a pre-launch checklist, monitoring, a staged rollout and a rollback plan so every release is reversible and observable.

MITAuto-check passedDevOps & Cloud

Install Shipping and Launch Checklist

skills CLI
$ npx skills add addyosmani/agent-skills --skill shipping-and-launch -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install addyosmani/agent-skills shipping-and-launch --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/addyosmani/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/shipping-and-launch .claude/skills/shipping-and-launch && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
shipping-and-launch
GitHub stars
104k
Used in
1 other repo
Token cost
~2.8k tokens
SKILL.md length
891 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

Prepares a production launch with a pre-launch checklist, monitoring, a staged rollout and a rollback plan so every release is reversible and observable.

  • Deploying a feature to production for the first time
  • SKILL.md covers Overview, When to Use, The Pre-Launch Checklist and Feature Flag Strategy, plus 8 more sections
  • Calls npm and cargo
  • Building a pre-launch checklist before a significant release

What it does

This skill frames a launch as a safe deployment: monitoring in place, a rollback plan ready and a clear definition of success, so every launch is reversible, observable and incremental. It applies to first-time production deploys, significant releases, data or infrastructure migrations and beta programs. The central tool is a pre-launch checklist grouped into code quality, security, performance, accessibility, infrastructure and documentation.

Checklist items include passing tests, build, lint and type checks, review, no stray debug statements, no secrets in version control, a clean dependency audit, input validation, authentication checks, security headers, rate limiting on authentication endpoints and specific CORS origins. Performance items cover Core Web Vitals, N+1 queries, image optimization, bundle size, indexes and caching. Accessibility items cover keyboard navigation, screen readers, WCAG 2.1 AA contrast and axe-core or Lighthouse warnings. Infrastructure items cover environment variables, migrations, DNS and SSL, a CDN, logging, error reporting and a health check endpoint.

When your agent uses it

  • Deploying a feature to production for the first time
  • Building a pre-launch checklist before a significant release
  • Planning a staged rollout and a rollback strategy
  • Setting up monitoring before a launch

Example prompts

  • “Give me a pre-launch checklist for shipping the new billing page to production.”
  • “Plan a staged rollout for the search rewrite, with a rollback plan.”
  • “What monitoring should be in place before we open the beta?”

What it can do on your machine

Read from SKILL.md and the folder at commit 1401c8b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm
    • cargo

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Shipping and Launch Checklist loads about 2.8k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 891 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from addyosmani/agent-skills at commit 1401c8b, republished under its MIT licence (© addyosmani). 891 words, ~2,802 tokens.

Download SKILL.mdSave it as .claude/skills/shipping-and-launch/SKILL.md (or your agent's skills folder).
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:

typescript
// 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:

MetricAdvance (green)Hold and investigate (yellow)Roll back (red)
Error rateWithin 10% of baseline10-100% above baseline>2x baseline
P95 latencyWithin 20% of baseline20-50% above baseline>50% above baseline
Client JS errorsNo new error typesNew errors at <0.1% of sessionsNew errors at >0.1% of sessions
Business metricsNeutral or positiveDecline <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
typescript
// 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)
Show full SKILL.md (381 more words)Show less

Error Budget Release Gate

Your service's error budget — the fraction of requests or time your SLO allows to fail — determines whether it's safe to ship. Use it as an objective gate — not a negotiation:

Budget remaining > 20%  →  Ship normally; monitor closely
Budget remaining 0–20%  →  Slow rollouts only; no high-risk changes
Budget exhausted        →  Freeze feature work; focus entirely on reliability
Budget resets           →  Resume normal pace; bake in the fix that recovered it

A high burn rate during a canary (consuming budget faster than the baseline pace) is a hold signal in the rollout thresholds table above — treat it the same as an elevated error rate.

Rollback Strategy

Every deployment needs a rollback plan before it happens:

markdown
## 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: <verified command or runbook link>
- 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
  • For the alerting rules and SLO-tied thresholds, see observability-and-instrumentation

Common Rationalizations

RationalizationReality
"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.
"The error rate looks fine, let's keep shipping"Check the burn rate, not just the current error rate. Consuming budget faster than baseline is a hold signal even when individual thresholds are green.

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"
  • Error budget exhausted but feature work continues unchanged

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

For every shipped service:

  • Error budget policy in place: know what action to take when budget drops below 20% and when it's exhausted

© addyosmani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/shipping-and-launch of addyosmani/agent-skills.

Open the folder on GitHubat commit 1401c8b

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in addyosmani/agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Alicloud Acs Agent Sandboxcinience/alicloud-skills397—~2.7kAutomated safety check: PassMIT
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Aqua Metricsoracle/accelerated-data-science125—~1.5kAutomated safety check: PassUPL-1.0

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Categories

Questions about Shipping and Launch Checklist

What does Shipping and Launch Checklist do?

Prepares a production launch with a pre-launch checklist, monitoring, a staged rollout and a rollback plan so every release is reversible and observable. This skill frames a launch as a safe deployment: monitoring in place, a rollback plan ready and a clear definition of success, so every launch is reversible, observable and incremental. It applies to first-time production deploys, significant releases, data or infrastructure migrations and beta programs.

When should I use Shipping and Launch Checklist?

Shipping and Launch Checklist fits situations like: deploying a feature to production for the first time; building a pre-launch checklist before a significant release; planning a staged rollout and a rollback strategy; setting up monitoring before a launch.

How do I install Shipping and Launch Checklist in Claude Code?

Run `npx skills add addyosmani/agent-skills --skill shipping-and-launch -a claude-code`. Or copy the skill folder (skills/shipping-and-launch in addyosmani/agent-skills) into .claude/skills/shipping-and-launch in your project. Claude Code loads it when a task matches its description.

How do I install Shipping and Launch Checklist in Codex?

Run `npx skills add addyosmani/agent-skills --skill shipping-and-launch -a codex`. Or copy the skill folder (skills/shipping-and-launch in addyosmani/agent-skills) into .agents/skills/shipping-and-launch in your project. Codex loads it when a task matches its description.

Can I use Shipping and Launch Checklist in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add addyosmani/agent-skills --skill shipping-and-launch -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/shipping-and-launch, .gemini/skills/shipping-and-launch, .github/skills/shipping-and-launch and .opencode/skills/shipping-and-launch in your project.

What does Shipping and Launch Checklist need to run?

Going by SKILL.md and its folder, Shipping and Launch Checklist needs the command-line tools its instructions call (npm and cargo).

Does Shipping and Launch Checklist access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Shipping and Launch Checklist safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Shipping and Launch Checklist use?

Shipping and Launch Checklist is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Shipping and Launch Checklist use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Shipping and Launch Checklist?

Skills that share tags, products or a category with Shipping and Launch Checklist: SageMaker Production Defaults (huggingface/skills, 11k stars), Dashboard Preview (m4r1k/Eneru, 149 stars), Alicloud Acs Agent Sandbox (cinience/alicloud-skills, 397 stars) and Onboarding Validation (open-edge-platform/edge-ai-suites, 140 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Shipping and Launch Checklist?

addyosmani (a GitHub user) maintains it in addyosmani/agent-skills, which has 104,189 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 3, 2026.

Source: addyosmani/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.