deployment-patterns
This Claude Code skill provides production deployment workflows, CI/CD best practices, and three core deployment strategies: rolling deployment for gradual updates with zero downtime, blue-green deployment for instant rollback capability, and canary deployment for testing changes with limited traffic exposure. Use it when setting up CI/CD pipelines, dockerizing applications, planning deployment strategies, implementing health checks, or preparing production releases with environment-specific configurations.
git clone --depth 1 https://github.com/affaan-m/ECC /tmp/deployment-patterns && cp -r /tmp/deployment-patterns/.kiro/skills/deployment-patterns ~/.claude/skills/deployment-patternsSKILL.md
# Deployment Patterns
Production deployment workflows and CI/CD best practices.
## When to Activate
- Setting up CI/CD pipelines
- Dockerizing an application
- Planning deployment strategy (blue-green, canary, rolling)
- Implementing health checks and readiness probes
- Preparing for a production release
- Configuring environment-specific settings
## Deployment Strategies
### Rolling Deployment (Default)
Replace instances gradually — old and new versions run simultaneously during rollout.
```
Instance 1: v1 → v2 (update first)
Instance 2: v1 (still running v1)
Instance 3: v1 (still running v1)
Instance 1: v2
Instance 2: v1 → v2 (update second)
Instance 3: v1
Instance 1: v2
Instance 2: v2
Instance 3: v1 → v2 (update last)
```
**Pros:** Zero downtime, gradual rollout
**Cons:** Two versions run simultaneously — requires backward-compatible changes
**Use when:** Standard deployments, backward-compatible changes
### Blue-Green Deployment
Run two identical environments. Switch traffic atomically.
```
Blue (v1) ← traffic
Green (v2) idle, running new version
# After verification:
Blue (v1) idle (becomes standby)
Green (v2) ← traffic
```
**Pros:** Instant rollback (switch back to blue), clean cutover
**Cons:** Requires 2x infrastructure during deployment
**Use when:** Critical services, zero-tolerance for issues
### Canary Deployment
Route a small percentage of traffic to the new version first.
```
v1: 95% of traffic
v2: 5% of traffic (canary)
# If metrics look good:
v1: 50% of traffic
v2: 50% of traffic
# Final:
v2: 100% of traffic
```
**Pros:** Catches issues with real traffic before full rollout
**Cons:** Requires traffic splitting infrastructure, monitoring
**Use when:** High-traffic services, risky changes, feature flags
## Docker
### Multi-Stage Dockerfile (Node.js)
```dockerfile
# Stage 1: Install dependencies
FROM node:22-alpine AS deps
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci --production=false
# Stage 2: Build
FROM node:22-alpine AS builder
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .
RUN npm run build
RUN npm prune --production
# Stage 3: Production image
FROM node:22-alpine AS runner
WORKDIR /app
RUN addgroup -g 1001 -S appgroup && adduser -S appuser -u 1001
USER appuser
COPY --from=builder --chown=appuser:appgroup /app/node_modules ./node_modules
COPY --from=builder --chown=appuser:appgroup /app/dist ./dist
COPY --from=builder --chown=appuser:appgroup /app/package.json ./
ENV NODE_ENV=production
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
CMD wget --no-verbose --tries=1 --spider http://localhost:3000/health || exit 1
CMD ["node", "dist/server.js"]
```
### Multi-Stage Dockerfile (Go)
```dockerfile
FROM golang:1.22-alpine AS builder
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-s -w" -o /server ./cmd/server
FROM alpine:3.19 AS runner
RUN apk --no-cache add ca-certificates
RUN adduser -D -u 1001 appuser
USER appuser
COPY --from=builder /server /server
EXPOSE 8080
HEALTHCHECK --interval=30s --timeout=3s CMD wget -qO- http://localhost:8080/health || exit 1
CMD ["/server"]
```
### Multi-Stage Dockerfile (Python/Django)
```dockerfile
FROM python:3.12-slim AS builder
WORKDIR /app
RUN pip install --no-cache-dir uv
COPY requirements.txt .
RUN uv pip install --system --no-cache -r requirements.txt
FROM python:3.12-slim AS runner
WORKDIR /app
RUN useradd -r -u 1001 appuser
USER appuser
COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
COPY --from=builder /usr/local/bin /usr/local/bin
COPY . .
ENV PYTHONUNBUFFERED=1
EXPOSE 8000
HEALTHCHECK --interval=30s --timeout=3s CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health/')" || exit 1
CMD ["gunicorn", "config.wsgi:application", "--bind", "0.0.0.0:8000", "--workers", "4"]
```
### Docker Best Practices
```
# GOOD practices
- Use specific version tags (node:22-alpine, not node:latest)
- Multi-stage builds to minimize image size
- Run as non-root user
- Copy dependency files first (layer caching)
- Use .dockerignore to exclude node_modules, .git, tests
- Add HEALTHCHECK instruction
- Set resource limits in docker-compose or k8s
# BAD practices
- Running as root
- Using :latest tags
- Copying entire repo in one COPY layer
- Installing dev dependencies in production image
- Storing secrets in image (use env vars or secrets manager)
```
## CI/CD Pipeline
### GitHub Actions (Standard Pipeline)
```yaml
name: CI/CD
on:
push:
branches: [main]
pull_request:
branches: [main]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: 22
cache: npm
- run: npm ci
- run: npm run lint
- run: npm run typecheck
- run: npm test -- --coverage
- uses: actions/upload-artifact@v4
if: always()
with:
name: coverage
path: coverage/
build:
needs: test
runs-on: ubuntu-latest
if: github.ref == 'refs/heads/main'
steps:
- uses: actions/checkout@v4
- uses: docker/setup-buildx-action@v3
- uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- uses: docker/build-push-action@v5
with:
push: true
tags: ghcr.io/${{ github.repository }}:${{ github.sha }}
cache-from: type=gha
cache-to: type=gha,mode=max
deploy:
needs: build
runs-on: ubuntu-latest
if: github.ref == 'refs/heads/main'
environment: production
steps:
- name: Deploy to production
run: |
# Platform-specific deployment command
# Railway: railway up
# Vercel: vercel --prWorkflow command scaffold for add-language-rules in everything-claude-code.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.
>
>
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
>