tanstack-query

pproenca/dot-skills · updated Apr 8, 2026

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$npx skills add https://github.com/pproenca/dot-skills --skill tanstack-query
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summary

Comprehensive performance optimization guide for TanStack Query v5 applications. Contains 40 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation.

skill.md

TanStack Query Best Practices

Comprehensive performance optimization guide for TanStack Query v5 applications. Contains 40 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation.

When to Apply

Reference these guidelines when:

  • Writing new queries, mutations, or data fetching logic
  • Implementing caching strategies (staleTime, gcTime)
  • Reviewing code for performance issues or request waterfalls
  • Refactoring existing TanStack Query code
  • Implementing infinite queries, Suspense, or optimistic updates

Rule Categories by Priority

Priority Category Impact Prefix
1 Query Key Structure CRITICAL tquery-
2 Caching Configuration CRITICAL cache-
3 Mutation Patterns HIGH mutation-
4 Prefetching & Waterfalls HIGH prefetch-
5 Infinite Queries MEDIUM infinite-
6 Suspense Integration MEDIUM suspense-
7 Error & Retry Handling MEDIUM error-
8 Render Optimization LOW-MEDIUM render-

Quick Reference

1. Query Key Structure (CRITICAL)

  • tquery-key-factories - Use centralized query key factories
  • tquery-hierarchical-keys - Structure keys from generic to specific
  • tquery-always-arrays - Always use array query keys
  • tquery-serializable-objects - Use serializable objects in keys
  • tquery-options-pattern - Use queryOptions for type-safe sharing
  • tquery-colocate-keys - Colocate query keys with features

2. Caching Configuration (CRITICAL)

  • cache-staletime-gctime - Understand staleTime vs gcTime
  • cache-global-defaults - Configure global defaults appropriately
  • cache-placeholder-vs-initial - Use placeholderData vs initialData correctly
  • cache-invalidation-precision - Invalidate with precision
  • cache-refetch-triggers - Control automatic refetch triggers
  • cache-enabled-option - Use enabled for conditional queries

3. Mutation Patterns (HIGH)

  • mutation-optimistic-updates - Implement optimistic updates with rollback
  • mutation-invalidate-onsettled - Invalidate in onSettled, not onSuccess
  • mutation-cancel-queries - Cancel queries before optimistic updates
  • mutation-setquerydata - Use setQueryData for immediate cache updates
  • mutation-avoid-parallel - Avoid parallel mutations on same data

4. Prefetching & Waterfalls (HIGH)

  • prefetch-avoid-waterfalls - Avoid request waterfalls
  • prefetch-on-hover - Prefetch on hover for perceived speed
  • prefetch-in-queryfn - Prefetch dependent data in queryFn
  • prefetch-server-components - Prefetch in Server Components
  • prefetch-flatten-api - Flatten API to reduce waterfalls

5. Infinite Queries (MEDIUM)

  • infinite-max-pages - Limit infinite query pages with maxPages
  • infinite-flatten-pages - Flatten pages for rendering
  • infinite-refetch-behavior - Understand infinite query refetch behavior
  • infinite-loading-states - Handle infinite query loading states correctly

6. Suspense Integration (MEDIUM)

  • suspense-use-suspense-hooks - Use Suspense hooks for simpler loading states
  • suspense-error-boundaries - Always pair Suspense with Error Boundaries
  • suspense-parallel-queries - Combine Suspense queries with useSuspenseQueries
  • suspense-boundaries-placement - Place Suspense boundaries strategically

7. Error & Retry Handling (MEDIUM)

  • error-retry-config - Configure retry with exponential backoff
  • error-conditional-retry - Use conditional retry based on error type
  • error-global-handler - Use global error handler for common errors
  • error-display-patterns - Display errors appropriately
  • error-throw-on-error - Use throwOnError with Error Boundaries

8. Render Optimization (LOW-MEDIUM)

  • render-select-memoize - Memoize select functions
  • render-select-derived - Use select to derive data and reduce re-renders
  • render-notify-props - Use notifyOnChangeProps to limit re-renders
  • render-structural-sharing - Understand structural sharing
  • render-tracked-props - Avoid destructuring all properties

How to Use

Read individual reference files for detailed explanations and code examples:

  • Section definitions - Category structure and impact levels
  • Reference files: references/{prefix}-{slug}.md

Each reference file contains:

  • Brief explanation of why it matters
  • Incorrect code example with explanation
  • Correct code example with explanation
  • Additional context and references

Related Skills

  • For generating type-safe query hooks, see orval skill
  • For mocking API responses in tests, see test-msw skill
  • For React 19 data fetching patterns, see react-19 skill

Full Compiled Document

For the complete guide with all rules expanded: AGENTS.md

how to use tanstack-query

How to use tanstack-query on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tanstack-query
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/pproenca/dot-skills --skill tanstack-query

The skills CLI fetches tanstack-query from GitHub repository pproenca/dot-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tanstack-query

Reload or restart Cursor to activate tanstack-query. Access the skill through slash commands (e.g., /tanstack-query) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.540 reviews
  • Mia Choi· Dec 28, 2024

    Registry listing for tanstack-query matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sakura Tandon· Dec 8, 2024

    tanstack-query reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Hiroshi Sanchez· Nov 27, 2024

    I recommend tanstack-query for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Naina Okafor· Nov 23, 2024

    tanstack-query is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Naina Mensah· Nov 19, 2024

    Useful defaults in tanstack-query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kofi Dixit· Oct 18, 2024

    Useful defaults in tanstack-query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Naina Nasser· Oct 14, 2024

    Keeps context tight: tanstack-query is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Mia Taylor· Oct 10, 2024

    I recommend tanstack-query for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mia Brown· Sep 13, 2024

    tanstack-query fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Sep 5, 2024

    tanstack-query has been reliable in day-to-day use. Documentation quality is above average for community skills.

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