cloudflare-knowledge

What this skill does

# 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:

| Topic | File | When to load |
|-------|------|--------------|
| All Wrangler CLI commands, complete `wrangler.jsonc` schema, GitHub Actions, Workers Builds | `references/wrangler-cli-and-config.md` | Initializing projects, configuring bindings, writing CI pipelines, troubleshooting wrangler |
| KV, R2, D1, Durable Objects, Queues, Hyperdrive — characteristics, TypeScript APIs, best practices, WebSocket Hibernation, multipart upload | `references/storage-services-deep-dive.md` | Picking a storage service, writing handler code for any binding, designing schema or partitioning |
| Workers AI catalog (text/TTS/STT/image/vision/embeddings), invocation examples, MCP server on Workers, Cloudflare Tunnel install + Access policies + WARP | `references/ai-workers-usage.md` | Invoking AI models, building an MCP server, setting up `cloudflared`, configuring Zero Trust ingress |
| Workers AI model selection (which model for which task, context windows, perf) | `references/ai-workers-models.md` | Deciding between Llama, Mistral, Qwen, DeepSeek, Whisper variants, etc. |
| Deeper MCP server development (transport types, auth, tool schemas) | `references/mcp-server-development.md` | Building production MCP servers, debugging transport |
| Deeper Zero Trust setup (org policies, identity providers, posture checks) | `references/zero-trust-setup.md` | Production Zero Trust rollout |
| Cost comparison vs AWS/Azure/GCP, pricing tables, optimization tactics | `references/cost-comparison.md` | Budget planning, plan selection, cost optimization |
| Integrating non-Cloudflare services (Stripe, OpenAI, GitHub, third-party APIs from Workers) | `references/third-party-integrations.md` | Wiring external APIs into a Worker |

## 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

| Task | Choice | Reference |
|------|--------|-----------|
| Store user sessions, config flags | KV (eventually consistent) | `storage-services-deep-dive.md` |
| Store media, backups, datasets | R2 (zero egress, 5 TB objects) | `storage-services-deep-dive.md` |
| Relational queries, ACID | D1 (SQLite, strong consistency) | `storage-services-deep-dive.md` |
| Real-time coordination, chat, counters | Durable Objects (+ WebSocket Hibernation) | `storage-services-deep-dive.md` |
| Background jobs, decoupling | Queues (at-least-once, DLQ) | `storage-services-deep-dive.md` |
| Remote Postgres/MySQL with low latency | Hyperdrive | `storage-services-deep-dive.md` |
| LLM/embedding/TTS/STT at the edge | Workers AI | `ai-workers-usage.md` + `ai-workers-models.md` |
| Expose internal app without opening firewall | Cloudflare Tunnel (`cloudflared`) | `ai-workers-usage.md` (quickstart) + `zero-trust-setup.md` (production) |
| Build MCP server on Workers | `@cloudflare/mcp-server` | `ai-workers-usage.md` (quickstart) + `mcp-server-development.md` (deep) |
| Integrate Stripe, OpenAI, GitHub, etc. | Third-party API patterns | `third-party-integrations.md` |
| Plan budget vs AWS/Azure/GCP | Pricing comparison | `cost-comparison.md` |

## 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

| Task | Command |
|------|---------|
| New project | `npm create cloudflare@latest` |
| Local dev | `npx wrangler dev` |
| Deploy | `npx wrangler deploy` |
| Create D1 | `npx wrangler d1 create <name>` |
| Create KV | `npx wrangler kv namespace create <NAME>` |
| Create R2 | `npx wrangler r2 bucket create <name>` |
| Set secret | `npx wrangler secret put <NAME>` |
| Create queue | `npx wrangler queues create <name>` |
| Create tunnel | `cloudflared tunnel create <name>` |
| Create Hyperdrive | `npx wrangler hyperdrive create <name> --connection-string=...` |

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