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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontanstack-queryExecute the skills CLI command in your project's root directory to begin installation:
Fetches tanstack-query from pproenca/dot-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tanstack-query. Access via /tanstack-query in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
Reference these guidelines when:
| 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- |
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 featurescache-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 queriesmutation-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 dataprefetch-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 waterfallsinfinite-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 correctlysuspense-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 strategicallyerror-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 Boundariesrender-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 propertiesRead individual reference files for detailed explanations and code examples:
references/{prefix}-{slug}.mdEach reference file contains:
orval skilltest-msw skillreact-19 skillFor the complete guide with all rules expanded: AGENTS.md
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
pproenca/dot-skills
pproenca/dot-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
Registry listing for tanstack-query matched our evaluation — installs cleanly and behaves as described in the markdown.
tanstack-query reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend tanstack-query for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tanstack-query is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in tanstack-query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in tanstack-query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: tanstack-query is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend tanstack-query for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tanstack-query fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
tanstack-query has been reliable in day-to-day use. Documentation quality is above average for community skills.
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