Cloudflare Knowledge Skill

Cloudflare Knowledge Skill

Comprehensive Cloudflare platform knowledge covering Workers, edge storage, AI, MCP, and Zero Trust. Acts as a lean orchestrator over deep references in references/.

Activation Triggers

Activate this skill when users ask about:

• Cloudflare Workers development, Wrangler CLI, wrangler.jsonc configuration
• Storage services (R2, D1, KV, Durable Objects, Queues, Hyperdrive)
• Workers AI inference (LLM, TTS, STT, image, embeddings, vision)
• MCP server development on Workers
• Zero Trust (tunnels, WARP, Access policies)
• Workflows and durable execution, Vectorize, Pages, cron triggers
• CI/​CD with GitHub Actions or Workers Builds
• Observability, load balancing, cost optimization

Reference Map

Load only the reference(s) the current task needs:

Platform Overview

Cloudflare is a global edge computing platform with 300+ data centers providing:

• Workers — Serverless JavaScript/​TypeScript/​Python/​WASM at the edge
• Pages — Static site and full-stack app hosting
• R2 — S3-compatible object storage with zero egress fees
• D1 — Serverless SQLite database (strongly consistent, 10 GB max)
• KV — Eventually consistent key-value store
• Durable Objects — Stateful coordination with WebSocket Hibernation
• Queues — Async message processing with DLQ
• Hyperdrive — Database connection pooling for remote Postgres/​MySQL
• Workers AI — LLM/​TTS/​STT/​image/​embeddings/​vision at the edge
• Zero Trust — Identity-based security platform
• Vectorize — Vector database for RAG
• Workflows — Durable multi-step execution

Core Workflow

1. Scaffold — npm create cloudflare@latest then npx wrangler login. Wrangler CLI details: wrangler-cli-and-config.md.
2. Pick a storage primitive — KV for config/​sessions, R2 for blobs, D1 for relational, Durable Objects for coordination, Queues for async, Hyperdrive for remote SQL. Characteristics and trade-offs: storage-services-deep-dive.md.
3. Add bindings to wrangler.jsonc — KV namespaces, R2 buckets, D1 databases, DO, Queues, AI, Vectorize, service bindings, cron triggers, routes, observability. Full schema: wrangler-cli-and-config.md.
4. Implement handlers — fetch, scheduled, queue, email. Per-binding APIs: storage-services-deep-dive.md. AI invocations: ai-workers-usage.md.
5. Develop locally — npx wrangler dev (use --remote for remote bindings; trigger crons via /​__scheduled?cron=*+*+*+*+*).
6. Deploy — npx wrangler deploy [--env staging]. Roll back with npx wrangler rollback. CI/​CD recipes (GitHub Actions, Workers Builds): wrangler-cli-and-config.md.

Quick Decision Guide

Best Practices

Performance

1. Use edge caching — cache API responses via caches.default.
2. Minimize cold starts — keep Workers small, prefer dynamic imports.
3. Use Service Bindings — zero-cost Worker-to-Worker calls.
4. Batch operations — combine KV/​R2/​D1 operations.
5. Use Hyperdrive for remote PostgreSQL/​MySQL.

Security

1. Use wrangler secret put for credentials, never hardcode.
2. Validate and sanitize all user input.
3. Always use HTTPS; enforce on routes.
4. Implement rate limiting (Workers Rate Limiting API or WAF rules).
5. Use Zero Trust Access for internal services (see zero-trust-setup.md).

Cost Optimization

1. Use Static Assets (free, unlimited static file serving).
2. Sample logs via observability.logs.head_sampling_rate for high-traffic Workers.
3. Use KV/​R2 for caching to reduce D1 or external API calls.
4. Batch Queue messages to reduce per-message overhead.
5. Choose model size to fit task in Workers AI — see ai-workers-models.md.
6. Full pricing tables and cross-cloud comparison: cost-comparison.md.

Quick Reference

Full command surface (every flag, every subcommand) is in references/​wrangler-cli-and-config.md.