Cloudflare Deploy

JetBrainsOfficialCreated by OpenAIDistributed by JetBrains

Skill
Tool integration
Cloud and DevOps
Apache-2.0
Medium risk

From the author

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.

— the description by OpenAI

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Cloudflare Deploy skill serves as a centralized hub for developers working within the Cloudflare ecosystem. It provides structured decision trees to help users select the appropriate Cloudflare product—ranging from compute and storage to AI and security—and offers guidance on authentication, deployment, and troubleshooting. The skill includes specific references for building stateful, distributed AI agents using the Cloudflare Agents SDK, covering lifecycle hooks, state management, and SQL integration.

What it does

Provides decision trees to select the right Cloudflare product for compute, storage, AI, or networking needs.
Offers guidance on authentication using Wrangler for local and CI/CD environments.
Includes detailed documentation for building stateful AI agents with the Cloudflare Agents SDK.
Supports infrastructure-as-code integration via Pulumi and Terraform.
Provides troubleshooting steps for network-related deployment failures.
Maps specific use cases to relevant product references and technical documentation.

When to use it

When you need to deploy, host, or publish a project on Cloudflare.
When you are unsure which Cloudflare product best fits your compute, storage, or AI requirements.
When building stateful AI agents that require persistent memory and real-time connections.
When you need to configure security, networking, or media optimization services on Cloudflare.
When troubleshooting deployment timeouts or network connectivity issues.

Advantages

Consolidates fragmented documentation into a single, navigable resource.
Simplifies product selection through clear, logic-based decision trees.
Provides ready-to-use code patterns for complex tasks like AI agent development.
Addresses common operational hurdles like authentication and network sandboxing.

Usage examples

“I need to deploy a full-stack web application to Cloudflare. Which product should I use?”: The agent will guide you to use Cloudflare Pages for Git-based deployments and provide the necessary steps to get started.
“How do I build a stateful AI chat agent on Cloudflare?”: The agent will provide the AIChatAgent class structure, explain how to manage message history, and show how to integrate with Workers AI.
“My deployment is failing with a network timeout. What should I do?”: The agent will recommend rerunning the deployment with escalated permissions using the sandbox_permissions=require_escalated flag.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/jetbrains/cloudflare-deploy/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip cloudflare-deploy.zip -d .agents/skills

Documentation by OpenAI

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:

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 detection → waf/ (managed ruleset)

"I need media/content"

Need media?
├─ Image optimization/transformation → images/
├─ Video streaming/encoding → stream/
├─ Browser automation/screenshots → browser-rendering/
└─ Third-party script management → zaraz/

"I need infrastructure-as-code"

Need IaC? → pulumi/ (Pulumi), terraform/ (Terraform), or api/ (REST API)

Product Index

Compute & Runtime

Product	Reference
Workers	`references/workers/`
Pages	`references/pages/`
Pages Functions	`references/pages-functions/`
Durable Objects	`references/durable-objects/`
Workflows	`references/workflows/`
Containers	`references/containers/`
Workers for Platforms	`references/workers-for-platforms/`
Cron Triggers	`references/cron-triggers/`
Tail Workers	`references/tail-workers/`
Snippets	`references/snippets/`
Smart Placement	`references/smart-placement/`

Storage & Data

Product	Reference
KV	`references/kv/`
D1	`references/d1/`
R2	`references/r2/`
Queues	`references/queues/`
Hyperdrive	`references/hyperdrive/`
DO Storage	`references/do-storage/`
Secrets Store	`references/secrets-store/`
Pipelines	`references/pipelines/`
R2 Data Catalog	`references/r2-data-catalog/`
R2 SQL	`references/r2-sql/`

AI & Machine Learning

Product	Reference
Workers AI	`references/workers-ai/`
Vectorize	`references/vectorize/`
Agents SDK	`references/agents-sdk/`
AI Gateway	`references/ai-gateway/`
AI Search	`references/ai-search/`

Networking & Connectivity

Product	Reference
Tunnel	`references/tunnel/`
Spectrum	`references/spectrum/`
TURN	`references/turn/`
Network Interconnect	`references/network-interconnect/`
Argo Smart Routing	`references/argo-smart-routing/`
Workers VPC	`references/workers-vpc/`

Security

Product	Reference
WAF	`references/waf/`
DDoS Protection	`references/ddos/`
Bot Management	`references/bot-management/`
API Shield	`references/api-shield/`
Turnstile	`references/turnstile/`

Media & Content

Product	Reference
Images	`references/images/`
Stream	`references/stream/`
Browser Rendering	`references/browser-rendering/`
Zaraz	`references/zaraz/`

Real-Time Communication

Product	Reference
RealtimeKit	`references/realtimekit/`
Realtime SFU	`references/realtime-sfu/`

Developer Tools

Product	Reference
Wrangler	`references/wrangler/`
Miniflare	`references/miniflare/`
C3	`references/c3/`
Observability	`references/observability/`
Analytics Engine	`references/analytics-engine/`
Web Analytics	`references/web-analytics/`
Sandbox	`references/sandbox/`
Workerd	`references/workerd/`
Workers Playground	`references/workers-playground/`

Infrastructure as Code

Product	Reference
Pulumi	`references/pulumi/`
Terraform	`references/terraform/`
API	`references/api/`

Other Services

Product	Reference
Email Routing	`references/email-routing/`
Email Workers	`references/email-workers/`
Static Assets	`references/static-assets/`
Bindings	`references/bindings/`
Cache Reserve	`references/cache-reserve/`

Troubleshooting

Escalated Network Access

If deployment fails due to network issues (timeouts, DNS errors, connection resets), rerun the deploy with escalated permissions (use sandbox_permissions=require_escalated). The deploy requires escalated network access when sandbox networking blocks outbound requests.

Example guidance to the user:

The deploy needs escalated network access to deploy to Cloudflare. I can rerun the command with escalated permissions—want me to proceed?

Files

ATTRIBUTION.mdAdded by AutoGineer930 B
LICENSE.txt10.5 KiB
SKILL.md7.4 KiB
agents/openai.yaml331 B
assets/cloudflare-small.svg1.2 KiB
assets/cloudflare.png1.2 KiB
references/agents-sdk/README.md2.7 KiB
references/agents-sdk/api.md5.7 KiB
references/agents-sdk/configuration.md3.5 KiB
references/agents-sdk/gotchas.md5.0 KiB
references/agents-sdk/patterns.md5.5 KiB
references/ai-gateway/README.md6.2 KiB
references/ai-gateway/configuration.md2.9 KiB
references/ai-gateway/dynamic-routing.md1.8 KiB
references/ai-gateway/features.md2.7 KiB
references/ai-gateway/sdk-integration.md2.6 KiB
references/ai-gateway/troubleshooting.md2.4 KiB
references/ai-search/README.md4.4 KiB
references/ai-search/api.md2.5 KiB
references/ai-search/configuration.md1.7 KiB
references/ai-search/gotchas.md2.1 KiB
references/ai-search/patterns.md2.0 KiB
references/analytics-engine/README.md3.1 KiB
references/analytics-engine/api.md3.1 KiB
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references/analytics-engine/gotchas.md2.0 KiB
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references/api-shield/README.md1.8 KiB
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references/hyperdrive/README.md2.8 KiB
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references/pages/gotchas.md7.8 KiB
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references/wrangler/api.md4.5 KiB
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references/wrangler/configuration.md4.6 KiB
references/wrangler/gotchas.md5.7 KiB
references/wrangler/patterns.md4.8 KiB
references/zaraz/IMPLEMENTATION_SUMMARY.md3.4 KiB
references/zaraz/README.md3.8 KiB
references/zaraz/api.md3.1 KiB
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references/r2-data-catalog/README.md

Cloudflare R2 Data Catalog Skill Reference

Expert guidance for Cloudflare R2 Data Catalog - Apache Iceberg catalog built into R2 buckets.

Reading Order

New to R2 Data Catalog? Start here:

Read "What is R2 Data Catalog?" and "When to Use" below
configuration.md - Enable catalog, create tokens
patterns.md - PyIceberg setup and common patterns
api.md - REST API reference as needed
gotchas.md - Troubleshooting when issues arise

Quick reference? Jump to:

What is R2 Data Catalog?

R2 Data Catalog is a managed Apache Iceberg REST catalog built directly into R2 buckets. It provides:

Apache Iceberg tables - ACID transactions, schema evolution, time-travel queries
Zero-egress costs - Query from any cloud/region without data transfer fees
Standard REST API - Works with Spark, PyIceberg, Snowflake, Trino, DuckDB
No infrastructure - Fully managed, no catalog servers to run
Public beta - Available to all R2 subscribers, no extra cost beyond R2 storage

What is Apache Iceberg?

Open table format for analytics datasets in object storage. Features:

ACID transactions - Safe concurrent reads/writes
Metadata optimization - Fast queries without full scans
Schema evolution - Add/rename/delete columns without rewrites
Time-travel - Query historical snapshots
Partitioning - Organize data for efficient queries

When to Use

Use R2 Data Catalog for:

Log analytics - Store and query application/system logs
Data lakes/warehouses - Analytical datasets queried by multiple engines
BI pipelines - Aggregate data for dashboards and reports
Multi-cloud analytics - Share data across clouds without egress fees
Time-series data - Event streams, metrics, sensor data

Don't use for:

Transactional workloads - Use D1 or external database instead
Sub-second latency - Iceberg optimized for batch/analytical queries
Small datasets (<1GB) - Setup overhead not worth it
Unstructured data - Store files directly in R2, not as Iceberg tables

Architecture

┌─────────────────────────────────────────────────┐
│  Query Engines                                  │
│  (PyIceberg, Spark, Trino, Snowflake, DuckDB)  │
└────────────────┬────────────────────────────────┘
                 │
                 │ REST API (OAuth2 token)
                 ▼
┌─────────────────────────────────────────────────┐
│  R2 Data Catalog (Managed Iceberg REST Catalog)│
│  • Namespace/table metadata                     │
│  • Transaction coordination                     │
│  • Snapshot management                          │
└────────────────┬────────────────────────────────┘
                 │
                 │ Vended credentials
                 ▼
┌─────────────────────────────────────────────────┐
│  R2 Bucket Storage                              │
│  • Parquet data files                           │
│  • Metadata files                               │
│  • Manifest files                               │
└─────────────────────────────────────────────────┘

Key concepts:

Catalog URI - REST endpoint for catalog operations (e.g., https://<account-id>.r2.cloudflarestorage.com/iceberg/<bucket>)
Warehouse - Logical grouping of tables (typically same as bucket name)
Namespace - Schema/database containing tables (e.g., logs, analytics)
Table - Iceberg table with schema, data files, snapshots
Vended credentials - Temporary S3 credentials catalog provides for data access

Limits

Resource	Limit	Notes
Namespaces per catalog	No hard limit	Organize tables logically
Tables per namespace	<10,000 recommended	Performance degrades beyond this
Files per table	<100,000 recommended	Run compaction regularly
Snapshots per table	Configurable retention	Expire >7 days old
Partitions per table	100-1,000 optimal	Too many = slow metadata ops
Table size	Same as R2 bucket	10GB-10TB+ common
API rate limits	Standard R2 API limits	Shared with R2 storage operations
Target file size	128-512 MB	After compaction

Current Status

Public Beta (as of Jan 2026)

Available to all R2 subscribers
No extra cost beyond standard R2 storage/operations
Production-ready, but breaking changes possible
Supports: namespaces, tables, snapshots, compaction, time-travel, table maintenance

Decision Tree: Is R2 Data Catalog Right For You?

Start → Need analytics on object storage data?
         │
         ├─ No → Use R2 directly for object storage
         │
         └─ Yes → Dataset >1GB with structured schema?
                  │
                  ├─ No → Too small, use R2 + ad-hoc queries
                  │
                  └─ Yes → Need ACID transactions or schema evolution?
                           │
                           ├─ No → Consider simpler solutions (Parquet on R2)
                           │
                           └─ Yes → Need multi-cloud/multi-tool access?
                                    │
                                    ├─ No → D1 or external DB may be simpler
                                    │
                                    └─ Yes → ✅ Use R2 Data Catalog

Quick check: If you answer "yes" to all:

Dataset >1GB and growing
Structured/tabular data (logs, events, metrics)
Multiple query tools or cloud environments
Need versioning, schema changes, or concurrent access

→ R2 Data Catalog is a good fit.

In This Reference

configuration.md - Enable catalog, create API tokens, connect clients
api.md - REST endpoints, operations, maintenance
patterns.md - PyIceberg examples, common use cases
gotchas.md - Troubleshooting, best practices, limitations

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

What our scanner noticed· Medium risk

Possible data-exfiltration endpoints
Instructions that use the network
Downloads piped into a shell

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by

OpenAI

Publisher

JetBrains (Official)

Distributed by

JetBrains

Source

JetBrains/skills · cloudflare-deploy · commit 3f53782

Licence

Apache License 2.0 (redistribution allowed)

ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by OpenAI or JetBrains.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cloudflare-deploy.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by OpenAI

Cloudflare Deploy

Prerequisites

Authentication (Required Before Deploy)

Quick Decision Trees

"I need to run code"

"I need to store data"

"I need AI/ML"

"I need networking/connectivity"

"I need security"

"I need media/content"

"I need infrastructure-as-code"

Product Index

Compute & Runtime

Storage & Data

AI & Machine Learning

Networking & Connectivity

Security

Media & Content

Real-Time Communication

Developer Tools

Infrastructure as Code

Other Services

Troubleshooting

Escalated Network Access

Files

references/r2-data-catalog/README.md

Cloudflare R2 Data Catalog Skill Reference

Reading Order

What is R2 Data Catalog?

What is Apache Iceberg?

When to Use

Architecture

Limits

Current Status

Decision Tree: Is R2 Data Catalog Right For You?

In This Reference

See Also

Requirements and compatibility

Security

Attribution and licence

Related skills

Modal

Cloud Infrastructure Network Engineer

Deployment Validation Cloud Architect

Airflow Dag Patterns

Cost Optimization

Hybrid Cloud Networking

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