Maintain Custom Addons Dev Watch logo

Maintain Custom Addons Dev Watch

OrganizationPopular
TanStack
maintain-custom-addons-dev-watch

Build and iterate custom add-ons/templates with tanstack add-on init, add-on compile, add-on dev, and tanstack create --dev-watch, including sync loop preconditions, watch-path validation, and project metadata constraints.

Overview

PublisherTanStack
Repositorycli
Skill namemaintain-custom-addons-dev-watch
Stars
1.3K
Forks
184
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 TanStack on GitHub. Read the source before you install it.

Installation

Install the Maintain Custom Addons Dev Watch 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/TanStack/cli.git /tmp/cli
mkdir -p .claude/skills
cp -r /tmp/cli/packages/cli/skills/maintain-custom-addons-dev-watch .claude/skills/maintain-custom-addons-dev-watch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maintain Custom Addons Dev Watch 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 Maintain Custom Addons Dev Watch 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 Maintain Custom Addons Dev Watch 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.

Maintain Custom Add-ons In Dev Watch

Use this skill for local add-on authoring workflows where you continuously compile and sync package output into a target app.

Setup

bash
npx @tanstack/cli add-on init
npx @tanstack/cli add-on compile

Core Patterns

Run add-on dev loop while editing source

bash
npx @tanstack/cli add-on dev

Sync watched framework directory into a sandbox target app

bash
# --dev-watch is a flag on `create`, not on `dev`
npx @tanstack/cli create my-sandbox --dev-watch ../path/to/framework-dir

Re-run compile before apply when changing metadata

bash
npx @tanstack/cli add-on compile
npx @tanstack/cli add my-custom-addon

Common Mistakes

HIGH Use --dev-watch with --no-install

Wrong:

bash
npx @tanstack/cli create my-sandbox --dev-watch ../my-addon-package --no-install

Correct:

bash
npx @tanstack/cli create my-sandbox --dev-watch ../my-addon-package

Dev-watch rejects --no-install, so automated loops fail before any sync work starts.

Source: packages/cli/src/dev-watch.ts:112

HIGH Start dev-watch without valid framework directory

Wrong:

bash
npx @tanstack/cli create my-sandbox --dev-watch ../missing-or-invalid-dir

Correct:

bash
npx @tanstack/cli create my-sandbox --dev-watch ../valid-framework-dir

Watch setup validates that the path exists, is a directory, and contains at least one of add-ons/, assets/, or framework.json. Invalid targets fail before file syncing begins.

Source: packages/cli/src/command-line.ts:599

CRITICAL Author add-on from code-router project

Wrong:

bash
npx @tanstack/cli add-on init

Correct:

bash
# Run add-on init from a file-router project
npx @tanstack/cli add-on init

Custom add-on authoring expects file-router mode and exits when run from incompatible project modes.

Source: packages/create/src/custom-add-ons/add-on.ts

HIGH Run add-on workflows without scaffold metadata

Wrong:

bash
npx @tanstack/cli add-on dev

Correct:

bash
# Run in a project scaffolded by TanStack CLI (contains .cta.json), then:
npx @tanstack/cli add-on dev

Custom add-on flows rely on persisted scaffold options, so missing metadata blocks initialization and update paths.

Source: packages/create/src/custom-add-ons/shared.ts:158

HIGH Tension: Backwards support vs deterministic automation

This domain's patterns conflict with add-addons-existing-app. Tooling assumes reusable automation, but hidden metadata preconditions from legacy support make add-on loops non-portable across repositories.

See also: add-addons-existing-app/SKILL.md § Common Mistakes

Frequently asked questions

What does the Maintain Custom Addons Dev Watch AI skill do?

Build and iterate custom add-ons/templates with tanstack add-on init, add-on compile, add-on dev, and tanstack create --dev-watch, including sync loop preconditions, watch-path validation, and project metadata constraints.

Why use Maintain Custom Addons Dev Watch on TypingMind?

Because you install it once and use it with any model. Maintain Custom Addons Dev Watch 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 Maintain Custom Addons Dev Watch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TanStack/cli/tree/main/packages/cli/skills/maintain-custom-addons-dev-watch. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Maintain Custom Addons Dev Watch?

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 Maintain Custom Addons Dev Watch?

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

Is the Maintain Custom Addons Dev Watch AI skill free?

Yes. It is published on GitHub by TanStack under the MIT 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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