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Tanstack Integration

Community
Dicklesworthstone
tanstack-integration

Find opportunities to improve web application code using TanStack libraries (Query, Table, Form, Router, etc.). Avoid man-with-hammer syndrome by applying TanStack after vanilla implementation works.

Overview

PublisherDicklesworthstone
Repositoryagent_flywheel_clawdbot_skills_and_integrations
Skill nametanstack-integration
Stars
73
Forks
16
Bundled files
Instructions only
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 Dicklesworthstone on GitHub. Read the source before you install it.

Installation

Install the Tanstack Integration 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.
    https://github.com/Dicklesworthstone/agent_flywheel_clawdbot_skills_and_integrations/tree/main/skills/tanstack-integration
  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/Dicklesworthstone/agent_flywheel_clawdbot_skills_and_integrations.git /tmp/agent_flywheel_clawdbot_skills_and_integrations
mkdir -p .claude/skills
cp -r /tmp/agent_flywheel_clawdbot_skills_and_integrations/skills/tanstack-integration .claude/skills/tanstack-integration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tanstack Integration 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 Integration 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 Integration 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 Integration — Strategic Library Adoption

Philosophy: Avoid "man with a hammer syndrome" (to whom everything appears as a nail). Start vanilla, then strategically adopt TanStack where it provides clear benefits.

Timing: Use this AFTER your app is already working pretty well in vanilla Next.js/React/Tailwind.


What is TanStack?

TanStack is a set of high-quality libraries for web applications:

LibraryPurpose
TanStack QueryServer state management, caching, synchronization
TanStack TableHeadless table/grid logic
TanStack FormForm state management and validation
TanStack RouterType-safe routing
TanStack VirtualVirtualization for large lists
TanStack RangerRange/slider components

The Anti-Pattern: Premature Adoption

Don't do this:

  1. Start new project
  2. Immediately install all TanStack libraries
  3. Force everything through TanStack patterns
  4. End up with over-engineered code

Why it's bad:

  • Not every feature needs TanStack
  • Adds complexity where simple solutions work
  • Makes code harder to understand for no benefit
  • "Man with a hammer" sees every problem as a nail

The Correct Pattern: Strategic Adoption

Do this instead:

  1. Build with vanilla Next.js 16, React 19, Tailwind
  2. Get the app working well
  3. Run the TanStack analysis prompt
  4. Adopt TanStack only where it clearly improves things
  5. Repeat several rounds

THE EXACT PROMPT — TanStack Analysis

Ok, I want you to look through the ENTIRE project and look for areas where, if we leveraged one of the many TanStack libraries (e.g., query, table, forms, etc), we could make part of the code much better, simpler, more performant, more maintainable, elegant, shorter, more reliable, etc. Use ultrathink

When to Use Each TanStack Library

TanStack Query

Good candidates:

  • API calls that need caching
  • Data that's fetched frequently
  • Optimistic updates
  • Background refetching
  • Pagination with caching
  • Infinite scroll

Skip if:

  • Simple one-time fetches
  • Static data
  • Data that doesn't need synchronization

TanStack Table

Good candidates:

  • Complex data tables with sorting/filtering
  • Tables with pagination
  • Column resizing/reordering
  • Row selection
  • Expandable rows
  • Server-side data tables

Skip if:

  • Simple static tables
  • Tables with < 20 rows
  • No interactivity needed

TanStack Form

Good candidates:

  • Complex multi-step forms
  • Forms with complex validation
  • Forms with dynamic fields
  • Forms with async validation
  • Wizard-style workflows

Skip if:

  • Simple contact forms
  • Forms with 3-4 fields
  • Basic validation needs

TanStack Router

Good candidates:

  • Large apps needing type-safe routing
  • Complex nested routes
  • Route-based code splitting
  • Search params management

Skip if:

  • Using Next.js App Router (already good)
  • Simple navigation needs
  • Few routes

TanStack Virtual

Good candidates:

  • Lists with 1000+ items
  • Infinite scroll views
  • Large data grids
  • Chat message lists

Skip if:

  • Lists with < 100 items
  • Already using windowing elsewhere
  • Performance is fine without it

Integration Workflow

Step 1: Get App Working

Build your app with vanilla patterns first:

  • fetch or axios for API calls
  • Native form handling
  • Simple HTML tables
  • Next.js routing

Step 2: Run Analysis

Use the TanStack analysis prompt with Claude Code + Opus 4.5 or Codex + GPT 5.2 (High reasoning effort).

Step 3: Evaluate Suggestions

The model will identify opportunities. For each:

  • Does the complexity justify the benefit?
  • Is the current solution actually problematic?
  • Will this improve maintainability?

Step 4: Selective Adoption

Only adopt where there's clear benefit. It's fine to:

  • Use TanStack Query but not Table
  • Use Table for one complex table, not all tables
  • Mix vanilla and TanStack approaches

Step 5: Repeat

Run the analysis again after changes. New opportunities may emerge.


Best Models for This Task

ModelConfiguration
Claude Code + Opus 4.5Use ultrathink
Codex + GPT 5.2High or Extra-High reasoning effort

Example Improvements

Before: Manual Data Fetching

typescript
// Vanilla approach
const [data, setData] = useState(null);
const [loading, setLoading] = useState(true);
const [error, setError] = useState(null);

useEffect(() => {
  fetch('/api/users')
    .then(res => res.json())
    .then(setData)
    .catch(setError)
    .finally(() => setLoading(false));
}, []);

After: TanStack Query

typescript
// TanStack Query approach
const { data, isLoading, error } = useQuery({
  queryKey: ['users'],
  queryFn: () => fetch('/api/users').then(res => res.json()),
});

Benefits:

  • Built-in caching
  • Automatic refetching
  • Request deduplication
  • DevTools support

Before: Complex Table Logic

typescript
// Vanilla approach with manual sorting, filtering, pagination
// ... 200+ lines of state management

After: TanStack Table

typescript
// TanStack Table handles sorting, filtering, pagination
const table = useReactTable({
  data,
  columns,
  getCoreRowModel: getCoreRowModel(),
  getSortedRowModel: getSortedRowModel(),
  getFilteredRowModel: getFilteredRowModel(),
  getPaginationRowModel: getPaginationRowModel(),
});

Benefits:

  • Headless (you control the UI)
  • All table logic handled
  • Consistent behavior
  • Much less code

Creating Beads for TanStack Work

bash
br create "Evaluate TanStack Query opportunities" -t enhancement -p 3
br create "Migrate user data fetching to TanStack Query" -t enhancement -p 2
br create "Implement data table with TanStack Table" -t feature -p 2
br create "Add TanStack Virtual to chat message list" -t performance -p 2

Complete Prompt Reference

TanStack Analysis

Ok, I want you to look through the ENTIRE project and look for areas where, if we leveraged one of the many TanStack libraries (e.g., query, table, forms, etc), we could make part of the code much better, simpler, more performant, more maintainable, elegant, shorter, more reliable, etc. Use ultrathink

Focused Query Analysis

Look through the project for data fetching patterns that would benefit from TanStack Query. Consider caching needs, refetching patterns, and optimistic updates. Identify the top 3 opportunities. Use ultrathink.

Focused Table Analysis

Look through the project for table/grid components that would benefit from TanStack Table. Consider sorting, filtering, pagination, and column management needs. Identify candidates. Use ultrathink.

Tips

  1. Don't over-adopt — Some vanilla patterns are fine
  2. Measure the benefit — Does it actually improve the code?
  3. Consider team familiarity — TanStack has a learning curve
  4. Check bundle size — Only import what you need
  5. Read the docs — TanStack documentation is excellent

Frequently asked questions

What does the Tanstack Integration AI skill do?

Find opportunities to improve web application code using TanStack libraries (Query, Table, Form, Router, etc.). Avoid man-with-hammer syndrome by applying TanStack after vanilla implementation works.

Why use Tanstack Integration on TypingMind?

Because you install it once and use it with any model. Tanstack Integration 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 Integration in TypingMind?

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

Which AI models can use Tanstack Integration?

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 Integration?

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

Is the Tanstack Integration AI skill free?

It is published on GitHub by Dicklesworthstone. Check the repository for licensing terms. 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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