What makes Cloudflare Deploy notable?

1.1K downloads. Trust evidence available

How should Cloudflare Deploy be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Cloudflare Deploy

Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or... Skill: Cloudflare Deploy Owner: tbeard602 Summary: Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-06-07T13:54:49.351Z | auto Initial release of the cloudflare-deploy skill. - Consolidates deployment guidance for Cloudflare Workers, Pages, and related platf

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Jun 7, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1745y3889m0whjz7wsbayv6kn87z206:cloudflare-deploy

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-tbeard602-cloudflare-deploy/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

50,891 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: cloudflare-deploy
description: Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.
---

# Cloudflare Deploy

Consolidated skill for building on the Cloudflare platform. Use decision trees below to find the right product, then load detailed references.

## Prerequisites

- When sandboxing blocks the deployment network calls, rerun with `sandbox_permissions=require_escalated`.
- The deployment might take a few minutes. Use appropriate timeout values.

## Authentication (Required Before Deploy)

Verify auth before `wrangler deploy`, `wrangler pages deploy`, or `npm run deploy`:

```bash
npx wrangler whoami    # Shows account if authenticated
```

Not authenticated? → `references/wrangler/auth.md`
- Interactive/local: `wrangler login` (one-time OAuth)
- CI/CD: Set `CLOUDFLARE_API_TOKEN` env var

## Quick Decision Trees

### "I need to run code"

```
Need to run code?
├─ Serverless functions at the edge → workers/
├─ Full-stack web app with Git deploys → pages/
├─ Stateful coordination/real-time → durable-objects/
├─ Long-running multi-step jobs → workflows/
├─ Run containers → containers/
├─ Multi-tenant (customers deploy code) → workers-for-platforms/
├─ Scheduled tasks (cron) → cron-triggers/
├─ Lightweight edge logic (modify HTTP) → snippets/
├─ Process Worker execution events (logs/observability) → tail-workers/
└─ Optimize latency to backend infrastructure → smart-placement/
```

### "I need to store data"

```
Need storage?
├─ Key-value (config, sessions, cache) → kv/
├─ Relational SQL → d1/ (SQLite) or hyperdrive/ (existing Postgres/MySQL)
├─ Object/file storage (S3-compatible) → r2/
├─ Message queue (async processing) → queues/
├─ Vector embeddings (AI/semantic search) → vectorize/
├─ Strongly-consistent per-entity state → durable-objects/ (DO storage)
├─ Secrets management → secrets-store/
├─ Streaming ETL to R2 → pipelines/
└─ Persistent cache (long-term retention) → cache-reserve/
```

### "I need AI/ML"

```
Need AI?
├─ Run inference (LLMs, embeddings, images) → workers-ai/
├─ Vector database for RAG/search → vectorize/
├─ Build stateful AI agents → agents-sdk/
├─ Gateway for any AI provider (caching, routing) → ai-gateway/
└─ AI-powered search widget → ai-search/
```

### "I need networking/connectivity"

```
Need networking?
├─ Expose local service to internet → tunnel/
├─ TCP/UDP proxy (non-HTTP) → spectrum/
├─ WebRTC TURN server → turn/
├─ Private network connectivity → network-interconnect/
├─ Optimize routing → argo-smart-routing/
├─ Optimize latency to backend (not user) → smart-placement/
└─ Real-time video/audio → realtimekit/ or realtime-sfu/
```

### "I need security"

```
Need security?
├─ Web Application Firewall → waf/
├─ DDoS protection → ddos/
├─ Bot detection/management → bot-management/
├─ API protection → api-shield/
├─ CAPTCHA alternative → turnstile/
└─ Credential leak det

references/agents-sdk/README.md

# Cloudflare Agents SDK

Cloudflare Agents SDK enables building AI-powered agents on Durable Objects with state, WebSockets, SQL, scheduling, and AI integration.

## Core Value
Build stateful, globally distributed AI agents with persistent memory, real-time connections, scheduled tasks, and async workflows.

## When to Use
- Persistent state + memory required
- Real-time WebSocket connections
- Long-running workflows (minutes/hours)
- Chat interfaces with AI models
- Scheduled/recurring tasks with state
- DB queries with agent state

## What Type of Agent?

| Use Case | Class | Key Features |
|----------|-------|--------------|
| AI chat interface | `AIChatAgent` | Auto-streaming, tools, message history, resumable |
| MCP tool provider | `Agent` + MCP | Expose tools to AI systems |
| Custom logic/routing | `Agent` | Full control, WebSockets, email, SQL |
| Real-time collaboration | `Agent` | WebSocket state, broadcasts |
| Email processing | `Agent` | `onEmail()` handler |

## Quick Start

**AI Chat Agent:**
```typescript
import { AIChatAgent } from "agents";
import { openai } from "@ai-sdk/openai";

export class ChatAgent extends AIChatAgent<Env> {
  async onChatMessage(onFinish) {
    return this.streamText({
      model: openai("gpt-4"),
      messages: this.messages,
      onFinish,
    });
  }
}
```

**Base Agent:**
```typescript
import { Agent } from "agents";

export class MyAgent extends Agent<Env> {
  onStart() {
    this.sql`CREATE TABLE IF NOT EXISTS users (id TEXT PRIMARY KEY)`;
  }
  
  async onRequest(request: Request) {
    return Response.json({ state: this.state });
  }
}
```

## Reading Order

| Task | Files to Read |
|------|---------------|
| Quick start | README only |
| Build chat agent | README → api.md (AIChatAgent) → patterns.md |
| Setup project | README → configuration.md |
| Add React frontend | README → api.md (Client Hooks) → patterns.md |
| Build MCP server | api.md (MCP) → patterns.md |
| Background tasks | api.md (Scheduling, Task Queue) → patterns.md |
| Debug issues | gotchas.md |

## Package Entry Points

| Import | Purpose |
|--------|---------|
| `agents` | Server-side Agent classes, lifecycle |
| `agents/react` | `useAgent()` hook for WebSocket connections |
| `agents/ai-react` | `useAgentChat()` hook for AI chat UIs |

## In This Reference
- [configuration.md](./configuration.md) - SDK setup, wrangler config, routing
- [api.md](./api.md) - Agent classes, lifecycle, client hooks
- [patterns.md](./patterns.md) - Common workflows, best practices
- [gotchas.md](./gotchas.md) - Common issues, limits

## See Also
- durable-objects - Agent infrastructure
- d1 - External database integration
- workers-ai - AI model integration
- vectorize - Vector search for RAG patterns

references/ai-gateway/README.md

# Cloudflare AI Gateway

Expert guidance for implementing Cloudflare AI Gateway - a universal gateway for AI model providers with analytics, caching, rate limiting, and routing capabilities.

## When to Use This Reference

- Setting up AI Gateway for any AI provider (OpenAI, Anthropic, Workers AI, etc.)
- Implementing caching, rate limiting, or request retry/fallback
- Configuring dynamic routing with A/B testing or model fallbacks
- Managing provider API keys securely with BYOK
- Adding security features (guardrails, DLP)
- Setting up observability with logging and custom metadata
- Debugging AI Gateway requests or optimizing configurations

## Quick Start

**What's your setup?**

- **Using Vercel AI SDK** → Pattern 1 (recommended) - see [sdk-integration.md](./sdk-integration.md)
- **Using OpenAI SDK** → Pattern 2 - see [sdk-integration.md](./sdk-integration.md)
- **Cloudflare Worker + Workers AI** → Pattern 3 - see [sdk-integration.md](./sdk-integration.md)
- **Direct HTTP (any language)** → Pattern 4 - see [configuration.md](./configuration.md)
- **Framework (LangChain, etc.)** → See [sdk-integration.md](./sdk-integration.md)

## Pattern 1: Vercel AI SDK (Recommended)

Most modern pattern using official `ai-gateway-provider` package with automatic fallbacks.

```typescript
import { createAiGateway } from 'ai-gateway-provider';
import { createOpenAI } from '@ai-sdk/openai';
import { generateText } from 'ai';

const gateway = createAiGateway({
  accountId: process.env.CF_ACCOUNT_ID,
  gateway: process.env.CF_GATEWAY_ID,
});

const openai = createOpenAI({ 
  apiKey: process.env.OPENAI_API_KEY 
});

// Single model
const { text } = await generateText({
  model: gateway(openai('gpt-4o')),
  prompt: 'Hello'
});

// Automatic fallback array
const { text } = await generateText({
  model: gateway([
    openai('gpt-4o'),              // Try first
    anthropic('claude-sonnet-4-5'), // Fallback
  ]),
  prompt: 'Hello'
});
```

**Install:** `npm install ai-gateway-provider ai @ai-sdk/openai @ai-sdk/anthropic`

## Pattern 2: OpenAI SDK

Drop-in replacement for OpenAI API with multi-provider support.

```typescript
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: `https://gateway.ai.cloudflare.com/v1/${accountId}/${gatewayId}/compat`,
  defaultHeaders: {
    'cf-aig-authorization': `Bearer ${cfToken}` // For authenticated gateways
  }
});

// Switch providers by changing model format: {provider}/{model}
const response = await client.chat.completions.create({
  model: 'openai/gpt-4o', // or 'anthropic/claude-sonnet-4-5'
  messages: [{ role: 'user', content: 'Hello!' }]
});
```

## Pattern 3: Workers AI Binding

For Cloudflare Workers using Workers AI.

```typescript
export default {
  async fetch(request, env, ctx) {
    const response = await env.AI.run(
      '@cf/meta/llama-3-8b-instruct',
      { messages: [{ role: 'user', content: 'Hello!' }] },
      { 
        gateway: { 
          id: 'my-gate

references/ai-search/README.md

# Cloudflare AI Search Reference

Expert guidance for implementing Cloudflare AI Search (formerly AutoRAG), Cloudflare's managed semantic search and RAG service.

## Overview

**AI Search** is a managed RAG (Retrieval-Augmented Generation) pipeline that combines:
- Automatic semantic indexing of your content
- Vector similarity search
- Built-in LLM generation

**Key value propositions:**
- **Zero vector management** - No manual embedding, indexing, or storage
- **Auto-indexing** - Content automatically re-indexed every 6 hours
- **Built-in generation** - Optional AI response generation from retrieved context
- **Multi-source** - Index from R2 buckets or website crawls

**Data source options:**
- **R2 bucket** - Index files from Cloudflare R2 (supports MD, TXT, HTML, PDF, DOC, CSV, JSON)
- **Website** - Crawl and index website content (requires Cloudflare-hosted domain)

**Indexing lifecycle:**
- Automatic 6-hour refresh cycle
- Manual "Force Sync" available (30s rate limit)
- Not designed for real-time updates

## Quick Start

**1. Create AI Search instance in dashboard:**
- Go to Cloudflare Dashboard → AI Search → Create
- Choose data source (R2 or website)
- Configure instance name and settings

**2. Configure Worker:**

```jsonc
// wrangler.jsonc
{
  "ai": {
    "binding": "AI"
  }
}
```

**3. Use in Worker:**

```typescript
export default {
  async fetch(request, env) {
    const answer = await env.AI.autorag("my-search-instance").aiSearch({
      query: "How do I configure caching?",
      model: "@cf/meta/llama-3.3-70b-instruct-fp8-fast"
    });
    
    return Response.json({ answer: answer.response });
  }
};
```

## When to Use AI Search

### AI Search vs Vectorize

| Factor | AI Search | Vectorize |
|--------|-----------|-----------|
| **Management** | Fully managed | Manual embedding + indexing |
| **Use when** | Want zero-ops RAG pipeline | Need custom embeddings/control |
| **Indexing** | Automatic (6hr cycle) | Manual via API |
| **Generation** | Built-in optional | Bring your own LLM |
| **Data sources** | R2 or website | Manual insert |
| **Best for** | Docs, support, enterprise search | Custom ML pipelines, real-time |

### AI Search vs Direct Workers AI

| Factor | AI Search | Workers AI (direct) |
|--------|-----------|---------------------|
| **Context** | Automatic retrieval | Manual context building |
| **Use when** | Need RAG (search + generate) | Simple generation tasks |
| **Indexing** | Built-in | Not applicable |
| **Best for** | Knowledge bases, docs | Simple chat, transformations |

### search() vs aiSearch()

| Method | Returns | Use When |
|--------|---------|----------|
| `search()` | Search results only | Building custom UI, need raw chunks |
| `aiSearch()` | AI response + results | Need ready-to-use answer (chatbot, Q&A) |

### Real-time Updates Consideration

**AI Search is NOT ideal if:**
- Need real-time content updates (<6 hours)
- Content changes multiple times per hour
- Strict freshness requirements

**AI

references/analytics-engine/README.md

# Cloudflare Workers Analytics Engine Reference

Expert guidance for implementing unlimited-cardinality analytics at scale using Cloudflare Workers Analytics Engine.

## What is Analytics Engine?

Time-series analytics database designed for high-cardinality data (millions of unique dimensions). Write data points from Workers, query via SQL API. Use for:
- Custom user-facing analytics dashboards
- Usage-based billing & metering
- Per-customer/per-feature monitoring
- High-frequency instrumentation without performance impact

**Key Capability:** Track metrics with unlimited unique values (e.g., millions of user IDs, API keys) without performance degradation.

## Core Concepts

| Concept | Description | Example |
|---------|-------------|---------|
| **Dataset** | Logical table for related metrics | `api_requests`, `user_events` |
| **Data Point** | Single measurement with timestamp | One API request's metrics |
| **Blobs** | String dimensions (max 20) | endpoint, method, status, user_id |
| **Doubles** | Numeric values (max 20) | latency_ms, request_count, bytes |
| **Indexes** | Filtered blobs for efficient queries | customer_id, api_key |

## Reading Order

| Task | Start Here | Then Read |
|------|------------|-----------|
| **First-time setup** | [configuration.md](configuration.md) → [api.md](api.md) → [patterns.md](patterns.md) | |
| **Writing data** | [api.md](api.md) → [gotchas.md](gotchas.md) (sampling) | |
| **Querying data** | [api.md](api.md) (SQL API) → [patterns.md](patterns.md) (examples) | |
| **Debugging** | [gotchas.md](gotchas.md) → [api.md](api.md) (limits) | |
| **Optimization** | [patterns.md](patterns.md) (anti-patterns) → [gotchas.md](gotchas.md) | |

## When to Use Analytics Engine

```
Need to track metrics? → Yes
  ↓
Millions of unique dimension values? → Yes
    ↓
  Need real-time queries? → Yes
      ↓
    Use Analytics Engine ✓

Alternative scenarios:
- Low cardinality (<10k unique values) → Workers Analytics (free tier)
- Complex joins/relations → D1 Database
- Logs/debugging → Tail Workers (logpush)
- External tools → Send to external analytics (Datadog, etc.)
```

## Quick Start

1. Add binding to `wrangler.jsonc`:
```jsonc
{
  "analytics_engine_datasets": [
    { "binding": "ANALYTICS", "dataset": "my_events" }
  ]
}
```

2. Write data points (fire-and-forget, no await):
```typescript
env.ANALYTICS.writeDataPoint({
  blobs: ["/api/users", "GET", "200"],
  doubles: [145.2, 1],  // latency_ms, count
  indexes: [customerId]
});
```

3. Query via SQL API (HTTP):
```sql
SELECT blob1, SUM(double2) AS total_requests
FROM my_events
WHERE index1 = 'customer_123'
  AND timestamp >= NOW() - INTERVAL '7' DAY
GROUP BY blob1
ORDER BY total_requests DESC
```

## In This Reference

- **[configuration.md](configuration.md)** - Setup, bindings, TypeScript types, limits
- **[api.md](api.md)** - `writeDataPoint()`, SQL API, query syntax
- **[patterns.md](patterns.md)** - Use cases, examples, anti-patterns
- **[gotchas.md](gotchas.md)*

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