Tanstack Ai Vue Skilld logo

Tanstack Ai Vue Skilld

Organization
skilld-dev
tanstack-ai-vue-skilld

Vue hooks for TanStack AI. ALWAYS use when writing code importing "@tanstack/ai-vue". Consult for debugging, best practices, or modifying @tanstack/ai-vue, tanstack/ai-vue, tanstack ai-vue, tanstack ai vue, ai.

Overview

Publisherskilld-dev
Repositoryvue-ecosystem-skills
Skill nametanstack-ai-vue-skilld
Stars
180
Forks
8
Bundled files
402
LicenseMIT
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.

  • 402 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by skilld-dev on GitHub. Read the source before you install it.

Installation

Install the Tanstack Ai Vue Skilld 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/skilld-dev/vue-ecosystem-skills.git /tmp/vue-ecosystem-skills
mkdir -p .claude/skills
cp -r /tmp/vue-ecosystem-skills/skills/tanstack-ai-vue-skilld .claude/skills/tanstack-ai-vue-skilld
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tanstack Ai Vue Skilld 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 Tanstack Ai Vue Skilld 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 Tanstack Ai Vue Skilld 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.

TanStack/ai @tanstack/ai-vue@0.7.0

Tags: latest: 0.7.0

References: Docs

API Changes

This section documents version-specific API changes for @tanstack/ai-vue v0.6.1 (current v0.x series). This library is pre-1.0 — all v0.x releases are in scope.

  • BREAKING: Monolithic adapter factories removed — openai(), anthropic(), etc. replaced by activity-specific functions: openaiText('gpt-5.2'), openaiSummarize('gpt-5-mini'), openaiImage('dall-e-3'), etc. Model name is now passed to the adapter factory, not to chat(). source

  • BREAKING: model parameter removed from chat() — model is now embedded in the adapter argument (e.g., adapter: openaiText('gpt-5.2') instead of adapter: openai(), model: 'gpt-4'). Passing model at the call site is silently ignored. source

  • BREAKING: Nested options object flattened — chat({ options: { temperature, maxTokens, topP } }) must be changed to chat({ temperature, maxTokens, topP }). Nested options are silently discarded. source

  • BREAKING: providerOptions renamed to modelOptionschat({ providerOptions: { ... } }) must be updated to chat({ modelOptions: { ... } }). Silently ignored if not updated. source

  • BREAKING: toResponseStream renamed to toServerSentEventsStream and now returns ReadableStream instead of Response — must manually create new Response(stream, { headers }). AbortController is now a separate parameter: toServerSentEventsStream(stream, abortController). source

  • BREAKING: embedding() function removed — embeddings support eliminated entirely. Use provider SDKs directly or vector DB native embedding APIs. source

  • BREAKING: chat({ as: 'promise' }) replaced by separate chatCompletion() function — as option removed from chat(). chat({ as: 'stream' }) is now just chat(). chat({ as: 'response' }) is now chat() + toServerSentEventsStream(). source

  • NEW: useChat returns status reactive ref — tracks lifecycle as 'ready' | 'submitted' | 'streaming' | 'error'. Previously there was no generation lifecycle state. source

  • NEW: sendMessage() accepts MultimodalContent object — sendMessage({ content: [{ type: 'text', content: '...' }, { type: 'image', source: { type: 'url', value: '...' } }] }) enables image/audio/video/document content alongside text. Added in v0.5.0. source

  • NEW: agentLoopStrategy parameter replaces bare maxIterations: number — use agentLoopStrategy: maxIterations(5), untilFinishReason(['stop']), or combineStrategies([...]). Old maxIterations number is converted automatically but deprecated. source

  • NEW: toolDefinition({ name, description, inputSchema, outputSchema?, needsApproval? }) — creates isomorphic tool definitions. Call .server(fn) for server-side execution or .client(fn) for client-side execution. Replaces ad-hoc tool objects. source

  • NEW: @tanstack/ai-client package — ChatClient class provides framework-agnostic headless chat state management with sendMessage(), reload(), stop(), clear(), addToolResult(), addToolApprovalResponse() methods. source

  • NEW: Connection adapter factories — fetchServerSentEvents(url, options?), fetchHttpStream(url, options?), stream(fn) from @tanstack/ai-client. Pass to useChat({ connection: fetchServerSentEvents('/api/chat') }) instead of url: '/api/chat'. source

  • NEW: extendAdapter(factory, customModels) + createModel(name, modalities) — adds custom/fine-tuned model names to existing adapter factories with full type inference. Avoids as const casts. source

Also changed: clientTools(...tools) NEW (typed tool array, discriminated union narrowing) · createChatClientOptions(options) NEW · InferChatMessages<T> NEW · toServerSentEventsResponse(stream, init?) NEW (returns Response) · toHttpStream(stream) NEW · toHttpResponse(stream) NEW · assertMessages({ adapter }, messages) NEW (type-level assertion) · ThinkingStreamChunk NEW (chunk type for model reasoning)

Best Practices

  • useChat returns DeepReadonly<ShallowRef<T>> refs — never reassign messages directly; use setMessages() for manual updates. Changing connection or body options recreates the underlying ChatClient, requiring a component remount or a key prop change to take effect

  • Use status (added v0.4.0) instead of isLoading for granular lifecycle control — status.value tracks 'ready' | 'submitted' | 'streaming' | 'error', enabling distinct UI states for submission vs. active streaming source

  • Pass client tool arrays through clientTools() instead of as const — eliminates the need for const assertion while enabling full discriminated union narrowing on part.name, part.input, and part.output in message iteration source

  • Wrap useChat options with createChatClientOptions() and derive message types using InferChatMessages<typeof chatOptions> — this propagates tool types through the entire message type, making part.name a literal union and part.input/part.output typed from Zod schemas source

  • Define tools with toolDefinition() in a shared file, then call .server() in route handlers and .client() in Vue components — passing the bare definition to chat() signals the client will execute it, while passing .server() output executes it server-side automatically source

  • Use Zod schemas (v4.2+) over raw JSON Schema for inputSchema/outputSchema in toolDefinition() and chat({ outputSchema }) — JSON Schema infers any for tool inputs/outputs and unknown for structured output return types, losing all downstream type safety source

  • Set agentLoopStrategy: maxIterations(n) explicitly when tools are present — the default is 5 iterations, which is too low for multi-step agentic workflows; use untilFinishReason('stop') to exit as soon as the model finishes without hitting the limit source

  • Subscribe to aiEventClient with { withEventTarget: true } in production code — without this third argument the client only emits to the devtools event bus (absent in production builds); the flag also dispatches to the current EventTarget for application-level observability source

  • Prefer fetchServerSentEvents over fetchHttpStream for client connections — SSE provides automatic reconnection; pass URL and options as functions (not static values) when headers like Authorization must be re-evaluated on every request source

  • Use extendAdapter(baseFactory, [createModel('model-name', ['text', 'image'])]) to add TypeScript types for fine-tuned models or OpenAI-compatible proxies — this adds the model to the adapter's allowed type union with zero runtime overhead while preserving all original factory config parameters source

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 140 more files.

Frequently asked questions

What does the Tanstack Ai Vue Skilld AI skill do?

Vue hooks for TanStack AI. ALWAYS use when writing code importing "@tanstack/ai-vue". Consult for debugging, best practices, or modifying @tanstack/ai-vue, tanstack/ai-vue, tanstack ai-vue, tanstack ai vue, ai.

Why use Tanstack Ai Vue Skilld on TypingMind?

Because you install it once and use it with any model. Tanstack Ai Vue Skilld 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 Tanstack Ai Vue Skilld in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/skilld-dev/vue-ecosystem-skills/tree/main/skills/tanstack-ai-vue-skilld. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tanstack Ai Vue Skilld?

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 Tanstack Ai Vue Skilld?

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

Is the Tanstack Ai Vue Skilld AI skill free?

Yes. It is published on GitHub by skilld-dev 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇