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Found 253 Skills
Deploy and manage cloud infrastructure on Cloudflare (Workers, R2, D1, KV, Pages, Durable Objects, Browser Rendering), Docker containers, and Google Cloud Platform (Compute Engine, GKE, Cloud Run, App Engine, Cloud Storage). Use when deploying serverless functions to the edge, configuring edge computing solutions, managing Docker containers and images, setting up CI/CD pipelines, optimizing cloud infrastructure costs, implementing global caching strategies, working with cloud databases, or building cloud-native applications.
Set up Cloudflare Workers with Hono routing, Vite plugin, and Static Assets using production-tested patterns. Prevents 6 errors: export syntax, routing conflicts, HMR crashes, and Service Worker format confusion. Use when: creating Workers projects, configuring Hono or Vite for Workers, deploying with Wrangler, adding Static Assets with SPA fallback, or troubleshooting export syntax, API route conflicts, scheduled handlers, or HMR race conditions. Keywords: Cloudflare Workers, CF Workers, Hono, wrangler, Vite, Static Assets, @cloudflare/vite-plugin, wrangler.jsonc, ES Module, run_worker_first, SPA fallback, API routes, serverless, edge computing, "Cannot read properties of undefined", "Static Assets 404", "A hanging Promise was canceled", "Handler does not export", deployment fails, routing not working, HMR crashes
Complete knowledge domain for Cloudflare Workers AI - Run AI models on serverless GPUs across Cloudflare's global network. Use when: implementing AI inference on Workers, running LLM models, generating text/images with AI, configuring Workers AI bindings, implementing AI streaming, using AI Gateway, integrating with embeddings/RAG systems, or encountering "AI_ERROR", rate limit errors, model not found, token limit exceeded, or neurons exceeded errors. Keywords: workers ai, cloudflare ai, ai bindings, llm workers, @cf/meta/llama, workers ai models, ai inference, cloudflare llm, ai streaming, text generation ai, ai embeddings, image generation ai, workers ai rag, ai gateway, llama workers, flux image generation, stable diffusion workers, vision models ai, ai chat completion, AI_ERROR, rate limit ai, model not found, token limit exceeded, neurons exceeded, ai quota exceeded, streaming failed, model unavailable, workers ai hono, ai gateway workers, vercel ai sdk workers, openai compatible workers, workers ai vectorize
Use this skill whenever the user needs backend infrastructure management — creating database tables, running SQL, deploying serverless functions, managing storage buckets, deploying frontend apps, adding secrets, setting up cron jobs, checking logs, or running backend diagnostics — especially if the project uses InsForge. Trigger on any of these contexts: creating or altering database tables/schemas, writing RLS policies via SQL, deploying or invoking edge functions, creating storage buckets, deploying frontends to hosting, managing secrets/env vars, setting up scheduled tasks/cron, viewing backend logs, diagnosing backend health or performance issues, or exporting/importing database backups. If the user asks for these operations generically (e.g., "create a users table", "deploy my app", "set up a cron job", "check backend health") and you're unsure whether they use InsForge, consult this skill and ask. For writing frontend application code with the InsForge SDK (@insforge/sdk), use the insforge skill instead.
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
Use this skill when building MCP (Model Context Protocol) servers with TypeScript on Cloudflare Workers. This skill provides production-tested patterns for implementing tools, resources, and prompts using the official @modelcontextprotocol/sdk. It prevents 10+ common errors including export syntax issues, schema validation failures, memory leaks from unclosed transports, CORS misconfigurations, and authentication vulnerabilities. This skill should be used when developers need stateless MCP servers for API integrations, external tool exposure, or serverless edge deployments. For stateful agents with WebSockets and persistent storage, consider the Cloudflare Agents SDK instead. Supports multiple authentication methods (API keys, OAuth, Zero Trust), Cloudflare service integrations (D1, KV, R2, Vectorize), and comprehensive testing strategies. Production tested with token savings of ~70% vs manual implementation. Keywords: mcp, model context protocol, typescript mcp, cloudflare workers mcp, mcp server, mcp tools, mcp resources, mcp sdk, @modelcontextprotocol/sdk, hono mcp, streamablehttpservertransport, mcp authentication, mcp cloudflare, edge mcp server, serverless mcp, typescript mcp server, mcp api, llm tools, ai tools, cloudflare d1 mcp, cloudflare kv mcp, mcp testing, mcp deployment, wrangler mcp, export syntax error, schema validation error, memory leak mcp, cors mcp, rate limiting mcp
Deploy to Cloudflare edge platform. Use when deploying static sites to Pages, serverless functions to Workers, or configuring CDN/DNS. Covers Wrangler CLI.
Guide users to manage Alibaba Cloud resources using the Aliyun CLI command-line tool. Covers CLI installation, credential configuration, plugin management, command construction, and error troubleshooting. Use this skill when the user wants to operate Alibaba Cloud services from the terminal — including ECS (云服务器, cloud servers), Function Compute (函数计算, serverless), RDS (云数据库, databases), OSS (对象存储, object storage), SLS (日志服务, log service), VPC (专有网络, networking), ESS (弹性伸缩, auto scaling), and any other Alibaba Cloud product. Also use when the user mentions "aliyun", "阿里云", "阿里云CLI", "命令行", asks about CLI plugin installation, encounters Aliyun CLI errors (InvalidAccessKeyId, SignatureDoesNotMatch, Throttling), or needs help constructing aliyun commands with correct parameter syntax.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
Primary entry point for building, managing, and orchestrating data pipelines on Google Cloud. Guides users to the appropriate skill for dbt, Dataflow (Apache Beam), Dataform, Spark (Dataproc Serverless), BigQuery Data Transfer Service (DTS) or orchestration pipeline using Cloud Composer. Clarify requirements and resolve ambiguity for creating, updating and running data pipelines.
Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.
Optimize application performance - bundle size, API response times, database queries, React rendering, and serverless function performance. Use when investigating slow pages, profiling, load testing, or before production deployments.