tanstack-query-best-practices▌
deckardger/tanstack-agent-skills · updated May 31, 2026
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TanStack Query best practices for optimized data fetching, caching, mutations, and server state management in React.
- ›Covers 32 rules across 10 categories: query keys, caching strategies, mutations, error handling, prefetching, infinite queries, SSR integration, parallel queries, performance optimization, and offline support
- ›Each rule includes explanation, anti-patterns, recommended implementations, and contextual guidance for when to apply
- ›Prioritized by impact: critical rules preven
TanStack Query Best Practices
Comprehensive guidelines for implementing TanStack Query (React Query) patterns in React applications. These rules optimize data fetching, caching, mutations, and server state synchronization.
When to Apply
- Creating new data fetching logic
- Setting up query configurations
- Implementing mutations and optimistic updates
- Configuring caching strategies
- Integrating with SSR/SSG
- Refactoring existing data fetching code
Rule Categories by Priority
| Priority | Category | Rules | Impact |
|---|---|---|---|
| CRITICAL | Query Keys | 5 rules | Prevents cache bugs and data inconsistencies |
| CRITICAL | Caching | 5 rules | Optimizes performance and data freshness |
| HIGH | Mutations | 6 rules | Ensures data integrity and UI consistency |
| HIGH | Error Handling | 3 rules | Prevents poor user experiences |
| MEDIUM | Prefetching | 4 rules | Improves perceived performance |
| MEDIUM | Parallel Queries | 2 rules | Enables dynamic parallel fetching |
| MEDIUM | Infinite Queries | 3 rules | Prevents pagination bugs |
| MEDIUM | SSR Integration | 4 rules | Enables proper hydration |
| LOW | Performance | 4 rules | Reduces unnecessary re-renders |
| LOW | Offline Support | 2 rules | Enables offline-first patterns |
Quick Reference
Query Keys (Prefix: qk-)
qk-array-structure— Always use arrays for query keysqk-include-dependencies— Include all variables the query depends onqk-hierarchical-organization— Organize keys hierarchically (entity → id → filters)qk-factory-pattern— Use query key factories for complex applicationsqk-serializable— Ensure all key parts are JSON-serializable
Caching (Prefix: cache-)
cache-stale-time— Set appropriate staleTime based on data volatilitycache-gc-time— Configure gcTime for inactive query retentioncache-defaults— Set sensible defaults at QueryClient levelcache-invalidation— Use targeted invalidation over broad patternscache-placeholder-vs-initial— Understand placeholder vs initial data differences
Mutations (Prefix: mut-)
mut-invalidate-queries— Always invalidate related queries after mutationsmut-optimistic-updates— Implement optimistic updates for responsive UImut-rollback-context— Provide rollback context from onMutatemut-error-handling— Handle mutation errors gracefullymut-loading-states— Use isPending for mutation loading statesmut-mutation-state— Use useMutationState for cross-component tracking
Error Handling (Prefix: err-)
err-error-boundaries— Use error boundaries with useQueryErrorResetBoundaryerr-retry-config— Configure retry logic appropriatelyerr-fallback-data— Provide fallback data when appropriate
Prefetching (Prefix: pf-)
pf-intent-prefetch— Prefetch on user intent (hover, focus)pf-route-prefetch— Prefetch data during route transitionspf-stale-time-config— Set staleTime when prefetchingpf-ensure-query-data— Use ensureQueryData for conditional prefetching
Infinite Queries (Prefix: inf-)
inf-page-params— Always provide getNextPageParaminf-loading-guards— Check isFetchingNextPage before fetching moreinf-max-pages— Consider maxPages for large datasets
SSR Integration (Prefix: ssr-)
ssr-dehydration— Use dehydrate/hydrate pattern for SSRssr-client-per-request— Create QueryClient per requestssr-stale-time-server— Set higher staleTime on serverssr-hydration-boundary— Wrap with HydrationBoundary
Parallel Queries (Prefix: parallel-)
parallel-use-queries— Use useQueries for dynamic parallel queriesquery-cancellation— Implement query cancellation properly
Performance (Prefix: perf-)
perf-select-transform— Use select to transform/filter dataperf-structural-sharing— Leverage structural sharingperf-notify-change-props— Limit re-renders with notifyOnChangePropsperf-placeholder-data— Use placeholderData for instant UI
Offline Support (Prefix: offline-)
network-mode— Configure network mode for offline supportpersist-queries— Configure query persistence for offline support
How to Use
Each rule file in the rules/ directory contains:
- Explanation — Why this pattern matters
- Bad Example — Anti-pattern to avoid
- Good Example — Recommended implementation
- Context — When to apply or skip this rule
Full Reference
See individual rule files in rules/ directory for detailed guidance and code examples.
How to use tanstack-query-best-practices 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-best-practices
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tanstack-query-best-practices from GitHub repository deckardger/tanstack-agent-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-best-practices. Access the skill through slash commands (e.g., /tanstack-query-best-practices) 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
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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.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★★★★★51 reviews- ★★★★★Kiara Zhang· Dec 24, 2024
tanstack-query-best-practices fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yusuf Zhang· Dec 20, 2024
Registry listing for tanstack-query-best-practices matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Dec 12, 2024
Solid pick for teams standardizing on skills: tanstack-query-best-practices is focused, and the summary matches what you get after install.
- ★★★★★Ren Smith· Dec 12, 2024
I recommend tanstack-query-best-practices for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ishan Diallo· Dec 12, 2024
Solid pick for teams standardizing on skills: tanstack-query-best-practices is focused, and the summary matches what you get after install.
- ★★★★★Fatima Anderson· Nov 15, 2024
tanstack-query-best-practices has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Meera Zhang· Nov 11, 2024
Keeps context tight: tanstack-query-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yusuf Anderson· Nov 11, 2024
Useful defaults in tanstack-query-best-practices — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Nov 3, 2024
We added tanstack-query-best-practices from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Naina Gill· Nov 3, 2024
We added tanstack-query-best-practices from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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