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Wrangler

OrganizationPopular
cloudflare
wrangler

Run or troubleshoot Wrangler CLI commands and configure Worker projects for local development, deployment, and Cloudflare resource management.

Overview

Publishercloudflare
Repositoryskills
Skill namewrangler
Stars
2.8K
Forks
277
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cloudflare on GitHub. Read the source before you install it.

Installation

Install the Wrangler AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cloudflare/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/wrangler .claude/skills/wrangler
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wrangler in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wrangler on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wrangler is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wrangler CLI

Use the project's Wrangler version and retrieve the relevant documentation before writing commands or configuration. CLI flags and configuration fields change; do not rely on memorized examples.

Inspect the Project

  • Find the package manager, installed Wrangler version, package scripts, framework, and Wrangler config. Run commands through the project's scripts or package manager so they use its local version. Install dependencies using the existing lockfile when needed; do not silently upgrade Wrangler to match current docs. If Wrangler is not a dependency, follow the installation guide to add it locally.
  • Identify the config used by the build or deploy command, including framework-generated config. Edit its source rather than generated output.
  • Establish the target account, Worker, environment, and resource before running commands that change them. For data operations, determine whether the target is local or remote.

Retrieve What the Task Needs

Use the Cloudflare MCP docs tool if available, or fetch the relevant linked page directly. Follow links to the specific command or product involved; avoid loading the entire reference. If a page moves, rediscover it through the Wrangler command index or Cloudflare docs search.

TaskSource
Discover commands and flags, including resource management, deployments, rollback, and diagnosticsProject-local wrangler --help and wrangler <command> --help; command reference
Edit config or add a bindingInstalled wrangler/config-schema.json (usually under node_modules); configuration reference
Deploy a framework applicationFramework guides; follow the guide for the project's existing framework and adapter
Migrate an application to Workers when requestedPages to Workers; Vercel to Workers
Configure staging or productionEnvironments
Set secrets locally, in CI, or on a deployed WorkerSecrets
Generate binding and runtime typesTypeScript
Run locally or choose a testing approachLocal development; testing
Diagnose authentication or select an accountGeneral commands, including whoami; authentication profiles
Deploy an unauthenticated prototypeClaim deployments for eligibility, expiry, and claim URL handling; use a permanent account for production or CI

Use installed help and schema to check whether documented features exist in the project's version. If a required feature needs an upgrade, make that dependency explicit. If retrieval is unavailable, state the gap and use available local evidence rather than inventing syntax.

Apply the Change

  • Prefer wrangler.jsonc for new config. Set a new project's compatibility date to today; review runtime changes and test when advancing an existing project's date. Preserve existing project conventions and avoid incidental format migrations.
  • Check environment inheritance before adding bindings or variables. Some fields must be specified separately for each environment; a working default config does not establish that staging is configured.
  • With the Cloudflare Vite plugin, select the environment via CLOUDFLARE_ENV at dev or build time. Deploy the resulting build; setting an environment at deploy time does not retarget its flattened config. See Vite environments.
  • Reconcile dashboard changes with the config before deploying: Wrangler can overwrite dashboard variables and routes. When binding existing resources, verify their identifiers; omitted identifiers can trigger automatic provisioning.
  • Distinguish local simulation from remote bindings during development. A locally running Worker can still access real resources; check the selected bindings before testing writes.
  • Keep secret values out of command arguments, source code, and logs. Use the documented interactive input or protected file/stdin mechanism for the command. Local secret files must be ignored by version control and are not automatically uploaded as deployed secrets. For missing local secrets, check file precedence and any secrets.required declaration in the secrets docs.
  • Treat wrangler secret put and secret delete as deployments: they create a version and deploy it immediately. Use the documented wrangler versions secret workflow when the change must be staged.
  • Before a rollback, check rollback limitations: connected resources and their data are not rolled back with Worker code.

Validate

After changing config or bindings in a TypeScript project, regenerate types with the project's wrangler types command rather than hand-editing generated declarations. Run the relevant existing typecheck or tests.

For deployment changes, use the project's build workflow and wrangler deploy --dry-run where supported, with the intended config and environment. A successful dry run checks the build and packaging; it does not prove remote resources or runtime behavior work. Use task-specific local or remote checks as appropriate to the requested work.

Report what changed, the target environment, checks performed, and any unresolved validation gaps. Link the documentation used when the result depends on current command or configuration behavior.

Frequently asked questions

What does the Wrangler AI skill do?

Run or troubleshoot Wrangler CLI commands and configure Worker projects for local development, deployment, and Cloudflare resource management.

Why use Wrangler on TypingMind?

Because you install it once and use it with any model. Wrangler is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wrangler in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cloudflare/skills/tree/main/skills/wrangler. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wrangler?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wrangler?

As many as you like. As long as a model supports skills, you can use Wrangler with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wrangler AI skill free?

Yes. It is published on GitHub by cloudflare under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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