TanStack Query (React Query) best practices for data fetching, caching, mutations, and server state management. Activate when building data-driven React applications with server state.
npx skills add https://github.com/DeckardGer/tanstack-agent-skills --skill 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.
| 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 |
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-serializablecache-)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 differencesmut-)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 trackingerr-)err-error-boundaries — Use error boundaries with useQueryErrorResetBoundaryerr-retry-config — Configure retry logic appropriatelyerr-fallback-data — Provide fallback data when appropriatepf-)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 prefetchinginf-)inf-page-params — Always provide getNextPageParaminf-loading-guards — Check isFetchingNextPage before fetching moreinf-max-pages — Consider maxPages for large datasetsssr-)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 HydrationBoundaryparallel-)parallel-use-queries — Use useQueries for dynamic parallel queriesquery-cancellation — Implement query cancellation properlyperf-)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 UIoffline-)network-mode — Configure network mode for offline supportpersist-queries — Configure query persistence for offline supportEach rule file in the rules/ directory contains:
See individual rule files in rules/ directory for detailed guidance and code examples.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Take deckardger/tanstack-query-best-practices from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.