Cloudflare Workers local development with Wrangler, Miniflare, hot reload, debugging. Use for project setup, wrangler.jsonc configuration, or encountering local dev, HMR, binding simulation errors.
npx skills add https://github.com/secondsky/claude-skills --skill cloudflare-workers-dev-experience
Local development setup with Wrangler, Miniflare, and modern tooling.
# Create new project
bunx create-cloudflare@latest my-worker
# Or from scratch
mkdir my-worker && cd my-worker
bun init -y
bun add -d wrangler @cloudflare/workers-types
# Start local development
bunx wrangler dev
Scaffolding tools like bunx create-cloudflare download and execute remote code. Before running, follow supply chain security best practices:
npm config set ignore-scripts true (or Bun: disabled by default)socket package score npm <pkg> or use socket npm install <pkg> to check packagesLoad the dependency-upgrade skill for full security configuration including Socket CLI integration, cooldown setup, lockfile validation, and CI enforcement.
{
"$schema": "node_modules/wrangler/config-schema.json",
"name": "my-worker",
"main": "src/index.ts",
"compatibility_date": "2024-12-01",
// Development settings
"dev": {
"port": 8787,
"local_protocol": "http"
},
// Environment variables (non-secret)
"vars": {
"ENVIRONMENT": "development"
},
// Bindings
"kv_namespaces": [
{ "binding": "KV", "id": "abc123", "preview_id": "def456" }
],
"d1_databases": [
{ "binding": "DB", "database_id": "xyz789", "database_name": "my-db" }
],
"r2_buckets": [
{ "binding": "BUCKET", "bucket_name": "my-bucket" }
]
}
wrangler dev for local testing - Simulates Cloudflare runtime accuratelycompatibility_date - Controls runtime behavior, update quarterly@cloudflare/workers-types| Error | Symptom | Prevention |
|-------|---------|------------|
| Module not found | Import errors on deploy | Set "moduleResolution": "bundler" in tsconfig |
| Binding undefined | env.KV is undefined locally | Add preview_id to KV namespace config |
| HMR not working | Changes not reflecting | Check port conflicts, use --local flag |
| D1 schema mismatch | Queries fail locally | Run migrations on local DB |
| Type errors | Missing binding types | Generate types with wrangler types |
| CORS issues | Browser blocking requests | Add CORS headers in dev handler |
# Start dev server (recommended)
bunx wrangler dev
# With live reload
bunx wrangler dev --live-reload
# Remote mode (use actual Cloudflare services)
bunx wrangler dev --remote
# Specify environment
bunx wrangler dev --env staging
# Custom port
bunx wrangler dev --port 3000
tsconfig.json:
{
"compilerOptions": {
"target": "ES2022",
"module": "ES2022",
"moduleResolution": "bundler",
"lib": ["ES2022"],
"types": ["@cloudflare/workers-types"],
"strict": true,
"noEmit": true,
"skipLibCheck": true,
"allowSyntheticDefaultImports": true,
"forceConsistentCasingInFileNames": true
},
"include": ["src/**/*"],
"exclude": ["node_modules"]
}
{
"scripts": {
"dev": "wrangler dev",
"deploy": "wrangler deploy",
"deploy:staging": "wrangler deploy --env staging",
"deploy:production": "wrangler deploy --env production",
"test": "vitest",
"test:watch": "vitest --watch",
"type-check": "tsc --noEmit",
"lint": "eslint src/",
"types": "wrangler types",
"tail": "wrangler tail",
"db:migrate": "wrangler d1 migrations apply DB",
"db:studio": "wrangler d1 execute DB --local --command 'SELECT 1'"
}
}
// Development-only logging
export default {
async fetch(request: Request, env: Env): Promise<Response> {
if (env.ENVIRONMENT === 'development') {
console.log('Request:', request.method, request.url);
console.log('Headers:', Object.fromEntries(request.headers));
}
// Handler logic...
}
};
# Real-time logs from deployed worker
wrangler tail
# Filter by status
wrangler tail --status error
# Filter by method
wrangler tail --method POST
# JSON format for parsing
wrangler tail --format json
.vscode/launch.json:
{
"version": "0.2.0",
"configurations": [
{
"name": "Wrangler Dev",
"type": "node",
"request": "launch",
"runtimeExecutable": "bunx",
"runtimeArgs": ["wrangler", "dev", "--inspector-port", "9229"],
"skipFiles": ["<node_internals>/**"],
"sourceMaps": true
}
]
}
Load specific references based on the task:
references/local-development.md for complete setup guidereferences/wrangler-config.md for all configuration optionsreferences/debugging-tools.md for debugging techniques| Template | Purpose | Use When |
|----------|---------|----------|
| templates/wrangler-config.jsonc | Complete wrangler config | Starting new project |
| templates/dev-script.ts | Development utilities | Adding dev helpers |
| Script | Purpose | Command |
|--------|---------|---------|
| scripts/dev-setup.sh | Initialize dev environment | ./dev-setup.sh |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take secondsky/cloudflare-workers-dev-experience from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npm.
Without those the skill loads but fails at the first command.