deployment

name	deployment
description	Use when setting up CI/CD pipelines, creating deployment configurations, generating deploy checklists, or configuring infrastructure. Triggers: new project needs deployment, migrating CI/CD provider, adding staging/production environments, automating release process, setting up monitoring for deploys.

Deployment

Overview

Set up CI/CD pipelines and deployment configurations that automate the path from code to production. This skill detects the deployment target, generates pipeline config, creates pre/post-deploy checklists, and configures monitoring — producing a fully automated, rollback-ready deployment pipeline.

Announce at start: "I am using the deployment skill to set up the deployment pipeline."

Phase 1: Detect Deployment Target

STOP after this phase — present findings to user for confirmation before proceeding.

Ask questions to identify the full deployment context:

Platform Detection:

• Where does this deploy? (Vercel, AWS, GCP, Azure, DigitalOcean, self-hosted)
• Container-based? (Docker, Kubernetes)
• Serverless? (Lambda, Cloud Functions, Edge Functions)

CI/CD Detection:

• What CI system? (GitHub Actions, GitLab CI, CircleCI, Jenkins)
• What triggers deployments? (push to main, tags, manual)
• Multi-environment? (dev, staging, production)

Infrastructure Detection:

• Database migrations needed?
• Environment variables management? (secrets manager, .env)
• CDN/caching? Asset pipeline?
• Monitoring/alerting? (Datadog, Sentry, New Relic)

Platform Selection Decision Table

Project Type	Recommended Platform	CI/CD	Why
Static site / SPA	Vercel, Netlify, Cloudflare Pages	Built-in	Zero config, edge CDN
Node.js API	AWS ECS, Cloud Run, Railway	GitHub Actions	Container support, auto-scaling
Monorepo (frontend + backend)	Vercel + AWS / Railway	GitHub Actions	Split concerns, independent scaling
Enterprise / compliance-heavy	AWS EKS, GKE	GitLab CI, Jenkins	Full control, audit trails
Hobby / side project	Railway, Fly.io, Render	Built-in or GitHub Actions	Simple, low cost
ML / data pipelines	AWS SageMaker, GCP Vertex	GitHub Actions + Airflow	GPU support, pipeline orchestration

Phase 2: Design Pipeline

STOP after this phase — present pipeline design to user for approval before generating config.

Standard Pipeline Stages

┌─────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐
│  Build   │──▶│   Test   │──▶│  Lint/   │──▶│  Deploy  │──▶│  Verify  │
│          │   │          │   │  Check   │   │          │   │          │
└─────────┘   └──────────┘   └──────────┘   └──────────┘   └──────────┘

Build: Install dependencies, compile, bundle Test: Unit tests, integration tests, coverage check Lint/Check: Linting, type checking, security audit Deploy: Push to target environment Verify: Health checks, smoke tests, monitoring

Branch Strategy Decision Table

Branch	Action	Environment	Gate
feature/*	Build + Test + Lint	None	PR checks pass
main	Build + Test + Lint + Deploy	Staging	All checks green
release/* or tags	Build + Test + Lint + Deploy	Production	Manual approval
hotfix/*	Build + Test + Deploy	Production (expedited)	Senior approval

Deployment Strategy Decision Table

Strategy	When to Use	Risk Level	Rollback Speed
Direct deploy	Solo/hobby projects, staging	High	Slow (redeploy)
Blue-green	Apps with health checks, low-downtime needs	Low	Instant (switch)
Canary	High-traffic production, gradual rollout	Very Low	Fast (reroute)
Rolling	Kubernetes clusters, stateless services	Low	Medium
Feature flags	Decoupled deploy from release	Very Low	Instant (toggle)

Phase 3: Generate Config

GitHub Actions Example

name: CI/CD Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

jobs:
  build-and-test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'
      - run: npm ci
      - run: npm run lint
      - run: npm run type-check
      - run: npm test -- --coverage
      - run:   

  
     
       
     
     
    
        
      

  
     
      
     
     
    
        
      

GitLab CI Example

stages:
  - build
  - test
  - deploy

build:
  stage: build
  script:
    - npm ci
    - npm run build
  artifacts:
    paths: [dist/]

test:
  stage: test
  script:
    - npm run lint
    - npm run type-check
    - npm test -- --coverage

deploy-staging:
  stage: deploy
  environment: staging
  script:
    - # platform-specific deploy
  only:
    - main

deploy-production:
  stage: deploy
  environment: production
  script:
    - # platform-specific deploy
  when: 
  
     

Phase 4: Create Deployment Checklists

STOP — present checklists to user. Customize based on their stack.

Pre-Deploy Checklist

## Pre-Deploy Checklist

- [ ] All tests passing on CI
- [ ] Code reviewed and approved
- [ ] No critical/high security vulnerabilities
- [ ] Environment variables configured for target environment
- [ ] Database migrations tested (if applicable)
- [ ] Feature flags configured (if applicable)
- [ ] Rollback plan documented
- [ ] Monitoring/alerts configured
- [ ] Changelog updated
- [ ] Version bumped

Post-Deploy Verification

## Post-Deploy Verification

- [ ] Health check endpoint returns 200
- [ ] Smoke tests passing
- [ ] Error rate within normal range
- [ ] Response times within SLA
- [ ] Database migrations applied successfully
- [ ] Feature flags active/inactive as expected
- [ ] Monitoring dashboard showing expected metrics
- [ ] No new errors in error tracking (Sentry, etc.)

Phase 5: Review and Finalize

Present the complete pipeline configuration to the user:

1. VERIFY CI/CD config file syntax is valid
2. VERIFY all environment variables are documented
3. VERIFY rollback plan exists
4. VERIFY pre/post-deploy checklists are complete
5. VERIFY the pipeline can be tested locally (act, etc.)

Save config to .github/workflows/ or equivalent.

Anti-Patterns / Common Mistakes

Anti-Pattern	Why It Is Wrong	What to Do Instead
Manual production deploys	Error-prone, no audit trail	Automate via CI/CD pipeline
No rollback plan	Stuck if deploy breaks production	Define rollback before every deploy
Skipping staging	Bugs found in production	Always deploy to staging first
Secrets in code/config files	Security breach risk	Use secrets manager or env vars
latest tag for production images	Non-reproducible deploys	Pin specific version tags
No concurrency control	Conflicting deploys	Add concurrency groups to CI
Deploying without health checks	No visibility into deploy health	Add health endpoint + post-deploy check
Alert fatigue from noisy monitors	Real issues get missed	Alert on symptoms, tune thresholds

Key Principles

• Automate everything — no manual steps in the critical path
• Fast feedback — fail early, fail fast
• Environment parity — staging matches production
• Rollback-ready — every deploy has a rollback plan
• Observable — monitoring before, during, and after deploy
• Secure — no secrets in code, use secrets management
• Idempotent — deploying the same version twice produces the same result

Integration Points

Skill	Integration
senior-devops	Provides Docker, K8s, and IaC patterns used in deploy config
git-commit-helper	Conventional commits drive changelog and version bumping
finishing-a-development-branch	Branch completion triggers deployment pipeline
verification-before-completion	Post-deploy verification gate
security-review	Security scan stage in the pipeline
planning	Deployment plan is part of the implementation plan

Skill Type

FLEXIBLE — Adapt pipeline design, platform selection, and tooling to the project's cloud provider, team size, and operational maturity. The principles (automation, rollback, observability) are constant; specific tools are interchangeable.