DevOps Skill

Quick Summary

Goal: Deploy and manage cloud infrastructure across Cloudflare (Workers, R2, D1), Docker containers, and Google Cloud.

Workflow:

1. Provider Selection — Choose Cloudflare (edge/​low-latency), Docker (containers/​microservices), or GCP (enterprise/​K8s)
2. Project Setup — Initialize with Wrangler CLI, Dockerfile, or gcloud CLI
3. Local Development — Test locally before deploying
4. Deploy & Verify — Deploy to the target provider/​runtime with health checks

Key Rules:

• Run containers as non-root user; scan images for vulnerabilities
• Use multi-stage Docker builds to minimize image size
• Store secrets in environment variables, never in code
• Use R2 over S3 when zero egress cost matters

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

DevOps Skill

Comprehensive guide for deploying and managing cloud infrastructure across Cloudflare edge services, Docker containerization, and Google Cloud.

When to Use This Skill

Use this skill when:

• Deploying serverless applications to Cloudflare Workers
• Containerizing applications with Docker
• Managing Google Cloud infrastructure with gcloud CLI
• Setting up CI/​CD pipelines across platforms
• Optimizing cloud infrastructure costs
• Implementing multi-region deployments
• Building edge-first architectures
• Managing container orchestration with Kubernetes
• Configuring cloud storage solutions (R2, Cloud Storage)
• Automating infrastructure with scripts and IaC

Provider Selection Guide

When to Use Cloudflare

Best For:

• Edge-first applications with global distribution
• Ultra-low latency requirements (<50ms)
• Static sites with serverless functions
• Zero egress cost scenarios (R2 storage)
• WebSocket/​real-time applications (Durable Objects)
• AI/​ML at the edge (Workers AI)

Key Products:

• Workers (serverless functions)
• R2 (object storage, S3-compatible)
• D1 (SQLite database with global replication)
• KV (key-value store)
• Pages (static hosting + functions)
• Durable Objects (stateful compute)
• Browser Rendering (headless browser automation)

Cost Profile: Pay-per-request, generous free tier, zero egress fees

When to Use Docker

Best For:

• Local development consistency
• Microservices architectures
• Multi-language stack applications
• Traditional VPS/​VM deployments
• Kubernetes orchestration
• CI/​CD build environments
• Database containerization (dev/​test)

Key Capabilities:

• Application isolation and portability
• Multi-stage builds for optimization
• Docker Compose for multi-container apps
• Volume management for data persistence
• Network configuration and service discovery
• Cross-architecture compatibility (amd64, arm64)

Cost Profile: Infrastructure cost only (compute + storage)

When to Use Google Cloud

Best For:

• Enterprise-scale applications
• Data analytics and ML pipelines (BigQuery, Vertex AI)
• Hybrid/​multi-cloud deployments
• Kubernetes at scale (GKE)
• Managed databases (Cloud SQL, Firestore, Spanner)
• Complex IAM and compliance requirements

Key Services:

• Compute Engine (VMs)
• GKE (managed Kubernetes)
• Cloud Run (containerized serverless)
• App Engine (PaaS)
• Cloud Storage (object storage)
• Cloud SQL (managed databases)

Cost Profile: Varied pricing, sustained use discounts, committed use contracts

Quick Start

Cloudflare Workers

# Install Wrangler CLI
npm install -g wrangler

# Create and deploy Worker
wrangler init my-worker
cd my-worker
wrangler deploy

See: references/​cloudflare-workers-basics.md

Docker Container

# Create Dockerfile
cat > Dockerfile <<EOF
FROM node:20-alpine
WORKDIR /​app
COPY package*.json ./
RUN npm ci --production
COPY . .
EXPOSE 3000
CMD ["node", "server.js"]
EOF

# Build and run
docker build -t myapp .
docker run -p 3000:3000 myapp

See: references/​docker-basics.md

Google Cloud Deployment

# Install and authenticate
curl https://sdk.cloud.google.com | bash
gcloud init
gcloud auth login

# Deploy to Cloud Run
gcloud run deploy my-service \
  --image gcr.io/​project/​image \
  --region us-central1

See the Google Cloud reference in references/

Reference Navigation

Cloudflare Developer Stack

• Cloudflare reference - Edge computing overview, key components
• cloudflare-workers-basics.md - Getting started, handler types, basic patterns
• cloudflare-workers-advanced.md - Advanced patterns, performance, optimization
• cloudflare-workers-apis.md - Runtime APIs, bindings, integrations
• cloudflare-r2-storage.md - R2 object storage, S3 compatibility, best practices
• cloudflare-d1-kv.md - D1 SQLite database, KV store, use cases
• browser-rendering.md - Puppeteer/​Playwright automation on Cloudflare

Docker Containerization

• docker-basics.md - Core concepts, Dockerfile, images, containers
• docker-compose.md - Multi-container apps, networking, volumes

Google Cloud

• Google Cloud reference - GCP overview, gcloud CLI, authentication
• gcloud-services.md - Compute Engine, GKE, Cloud Run, App Engine

Python Utilities

• scripts/​cloudflare-deploy.py - Automate Cloudflare Worker deployments
• scripts/​docker-optimize.py - Analyze and optimize Dockerfiles

Common Workflows

Edge + Container Hybrid

# Cloudflare Workers (API Gateway)
# -> Docker containers on Cloud Run (Backend Services)
# -> R2 (Object Storage)

# Benefits:
# - Edge caching and routing
# - Containerized business logic
# - Global distribution

Multi-Stage Docker Build

# Build stage
FROM node:20-alpine AS build
WORKDIR /​app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

# Production stage
FROM node:20-alpine
WORKDIR /​app
COPY --from=build /​app/​dist ./​dist
COPY --from=build /​app/​node_modules ./​node_modules
USER node
CMD ["node", "dist/​server.js"]

CI/​CD Pipeline Pattern

# 1. Build: Docker multi-stage build
# 2. Test: Run tests in container
# 3. Push: Push to registry (GCR, Docker Hub)
# 4. Deploy: Deploy to Cloudflare Workers / Cloud Run
# 5. Verify: Health checks and smoke tests

Best Practices

Security

• Run containers as non-root user
• Use service account impersonation (GCP)
• Store secrets in environment variables, not code
• Scan images for vulnerabilities (Docker Scout)
• Use API tokens with minimal permissions

Performance

• Multi-stage Docker builds to reduce image size
• Edge caching with Cloudflare KV
• Use R2 for zero egress cost storage
• Implement health checks for containers
• Set appropriate timeouts and resource limits

Cost Optimization

• Use Cloudflare R2 instead of S3 for large egress
• Implement caching strategies (edge + KV)
• Right-size container resources
• Use sustained use discounts (GCP)
• Monitor usage with cloud provider dashboards

Development

• Use Docker Compose for local development
• Wrangler dev for local Worker testing
• Named gcloud configurations for multi-environment
• Version control infrastructure code
• Implement automated testing in CI/​CD

Decision Matrix

Resources

• Cloudflare Docs: https://developers.cloudflare.com
• Docker Docs: https://docs.docker.com
• GCP Docs: https://cloud.google.com/docs
• Wrangler CLI: https://developers.cloudflare.com/workers/wrangler/
• gcloud CLI: https://cloud.google.com/sdk/gcloud

Implementation Checklist

Cloudflare Workers

• Install Wrangler CLI
• Create Worker project
• Configure wrangler.toml (bindings, routes)
• Test locally with wrangler dev
• Deploy with wrangler deploy

Docker

• Write Dockerfile with multi-stage builds
• Create .dockerignore file
• Test build locally
• Push to registry
• Deploy to target provider/​runtime

Google Cloud

• Install gcloud CLI
• Authenticate with service account
• Create project and enable APIs
• Configure IAM permissions
• Deploy and monitor resources

Related

• db-migrate

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[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

<!-- SYNC:ai-mistake-prevention -->

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path. Keep domain concepts out of generic/​shared/​infrastructure layers. A reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/​customer/​product IDs, business entities, feature rules. The leak compiles and runs, so it passes review silently while coupling the "reusable" layer to one consumer. Push domain fields/​logic down into the consumer via subclass or composition.

<!-- /​SYNC:ai-mistake-prevention --> <!-- SYNC:critical-thinking-mindset -->

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

<!-- /​SYNC:critical-thinking-mindset --> <!-- SYNC:critical-thinking-mindset:reminder -->

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

<!-- /​SYNC:critical-thinking-mindset:reminder --> <!-- SYNC:ai-mistake-prevention:reminder -->

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

<!-- /​SYNC:ai-mistake-prevention:reminder -->

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.