// [DevOps] Use when deploying to Cloudflare (Workers, R2, D1, KV, Pages), Docker, or GCP (Compute Engine, GKE, Cloud Run).
[HINT] Download the complete skill directory including SKILL.md and all related files
| name | devops |
| version | 1.0.0 |
| description | [DevOps] Use when deploying to Cloudflare (Workers, R2, D1, KV, Pages), Docker, or GCP (Compute Engine, GKE, Cloud Run). |
| license | MIT |
| disable-model-invocation | true |
Goal: Deploy and manage cloud infrastructure across Cloudflare (Workers, R2, D1), Docker containers, and Google Cloud Platform.
Workflow:
Key Rules:
Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).
Comprehensive guide for deploying and managing cloud infrastructure across Cloudflare edge platform, Docker containerization, and Google Cloud Platform.
Use this skill when:
Best For:
Key Products:
Cost Profile: Pay-per-request, generous free tier, zero egress fees
Best For:
Key Capabilities:
Cost Profile: Infrastructure cost only (compute + storage)
Best For:
Key Services:
Cost Profile: Varied pricing, sustained use discounts, committed use contracts
# 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
# 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
# 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: references/gcloud-platform.md
cloudflare-platform.md - Edge computing overview, key componentscloudflare-workers-basics.md - Getting started, handler types, basic patternscloudflare-workers-advanced.md - Advanced patterns, performance, optimizationcloudflare-workers-apis.md - Runtime APIs, bindings, integrationscloudflare-r2-storage.md - R2 object storage, S3 compatibility, best practicescloudflare-d1-kv.md - D1 SQLite database, KV store, use casesbrowser-rendering.md - Puppeteer/Playwright automation on Cloudflaredocker-basics.md - Core concepts, Dockerfile, images, containersdocker-compose.md - Multi-container apps, networking, volumesgcloud-platform.md - GCP overview, gcloud CLI, authenticationgcloud-services.md - Compute Engine, GKE, Cloud Run, App Enginescripts/cloudflare-deploy.py - Automate Cloudflare Worker deploymentsscripts/docker-optimize.py - Analyze and optimize Dockerfiles# Cloudflare Workers (API Gateway)
# -> Docker containers on Cloud Run (Backend Services)
# -> R2 (Object Storage)
# Benefits:
# - Edge caching and routing
# - Containerized business logic
# - Global distribution
# 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"]
# 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
| Need | Choose |
|---|---|
| Sub-50ms latency globally | Cloudflare Workers |
| Large file storage (zero egress) | Cloudflare R2 |
| SQL database (global reads) | Cloudflare D1 |
| Containerized workloads | Docker + Cloud Run/GKE |
| Enterprise Kubernetes | GKE |
| Managed relational DB | Cloud SQL |
| Static site + API | Cloudflare Pages |
| WebSocket/real-time | Cloudflare Durable Objects |
| ML/AI pipelines | GCP Vertex AI |
| Browser automation | Cloudflare Browser Rendering |
wrangler devwrangler deploydatabasesapi-design[IMPORTANT] Use
TaskCreateto 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.
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.
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.
MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.
TaskCreate BEFORE startingfile:line evidence for every claim (confidence >80% to act)[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.