tanstack-query▌
pproenca/dot-skills · updated Apr 8, 2026
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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.
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 factoriestquery-hierarchical-keys- Structure keys from generic to specifictquery-always-arrays- Always use array query keystquery-serializable-objects- Use serializable objects in keystquery-options-pattern- Use queryOptions for type-safe sharingtquery-colocate-keys- Colocate query keys with features
2. Caching Configuration (CRITICAL)
cache-staletime-gctime- Understand staleTime vs gcTimecache-global-defaults- Configure global defaults appropriatelycache-placeholder-vs-initial- Use placeholderData vs initialData correctlycache-invalidation-precision- Invalidate with precisioncache-refetch-triggers- Control automatic refetch triggerscache-enabled-option- Use enabled for conditional queries
3. Mutation Patterns (HIGH)
mutation-optimistic-updates- Implement optimistic updates with rollbackmutation-invalidate-onsettled- Invalidate in onSettled, not onSuccessmutation-cancel-queries- Cancel queries before optimistic updatesmutation-setquerydata- Use setQueryData for immediate cache updatesmutation-avoid-parallel- Avoid parallel mutations on same data
4. Prefetching & Waterfalls (HIGH)
prefetch-avoid-waterfalls- Avoid request waterfallsprefetch-on-hover- Prefetch on hover for perceived speedprefetch-in-queryfn- Prefetch dependent data in queryFnprefetch-server-components- Prefetch in Server Componentsprefetch-flatten-api- Flatten API to reduce waterfalls
5. Infinite Queries (MEDIUM)
infinite-max-pages- Limit infinite query pages with maxPagesinfinite-flatten-pages- Flatten pages for renderinginfinite-refetch-behavior- Understand infinite query refetch behaviorinfinite-loading-states- Handle infinite query loading states correctly
6. Suspense Integration (MEDIUM)
suspense-use-suspense-hooks- Use Suspense hooks for simpler loading statessuspense-error-boundaries- Always pair Suspense with Error Boundariessuspense-parallel-queries- Combine Suspense queries with useSuspenseQueriessuspense-boundaries-placement- Place Suspense boundaries strategically
7. Error & Retry Handling (MEDIUM)
error-retry-config- Configure retry with exponential backofferror-conditional-retry- Use conditional retry based on error typeerror-global-handler- Use global error handler for common errorserror-display-patterns- Display errors appropriatelyerror-throw-on-error- Use throwOnError with Error Boundaries
8. Render Optimization (LOW-MEDIUM)
render-select-memoize- Memoize select functionsrender-select-derived- Use select to derive data and reduce re-rendersrender-notify-props- Use notifyOnChangeProps to limit re-rendersrender-structural-sharing- Understand structural sharingrender-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
orvalskill - For mocking API responses in tests, see
test-mswskill - For React 19 data fetching patterns, see
react-19skill
Full Compiled Document
For the complete guide with all rules expanded: AGENTS.md
How to use tanstack-query on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tanstack-query from GitHub repository pproenca/dot-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★40 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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