JosiahSiegel/cloudflare-knowledge

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.