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
Install
Use withClaudeCursorCodex
The file stays in the publisher's repository. PlugMCP does not host or rewrite it.
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.
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)
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
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
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)
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:
## 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
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.
"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
Error budget policy in place: know what action to take when budget drops below 20% and when it's exhausted
How do I install shipping-and-launch?
Download skills/shipping-and-launch/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does shipping-and-launch live?
The publisher keeps it at https://github.com/addyosmani/agent-skills.
Which agents can use shipping-and-launch?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.