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Found 381 Skills
Ruby on Rails performance and maintainability optimization guidelines for building backend APIs and frontend web applications. This skill should be used when writing, reviewing, or refactoring Ruby on Rails code to ensure optimal patterns for controllers, models, ActiveRecord queries, caching, views, API design, security, and background jobs. Triggers on tasks involving Rails controllers, ActiveRecord queries, migrations, Turbo/Hotwire, API endpoints, background jobs, or Rails performance improvements.
Complete knowledge domain for Cloudflare Hyperdrive - connecting Cloudflare Workers to existing PostgreSQL and MySQL databases with global connection pooling, query caching, and reduced latency. Use when: connecting Workers to existing databases, migrating PostgreSQL/MySQL to Cloudflare, setting up connection pooling, configuring Hyperdrive bindings, using node-postgres/postgres.js/mysql2 drivers, integrating Drizzle ORM or Prisma ORM, or encountering "Failed to acquire a connection from the pool", "TLS not supported by the database", "connection refused", "nodejs_compat missing", "Code generation from strings disallowed", or Hyperdrive configuration errors. Keywords: hyperdrive, cloudflare hyperdrive, workers hyperdrive, postgres workers, mysql workers, connection pooling, query caching, node-postgres, pg, postgres.js, mysql2, drizzle hyperdrive, prisma hyperdrive, workers rds, workers aurora, workers neon, workers supabase, database acceleration, hybrid architecture, cloudflare tunnel database, wrangler hyperdrive, hyperdrive bindings, local development hyperdrive
Implements Rails caching patterns for performance optimization. Use when adding fragment caching, Russian doll caching, low-level caching, HTTP caching with ETags, cache invalidation, or when user mentions caching, performance, cache keys, or Solid Cache.
Generate Go cache implementations following GO modular architechture conventions. Use when creating cache layers in internal/modules/<module>/cache/ - user state caching, session caching, rate limiting data, temporary data storage, or any domain cache that uses Redis for fast data access with TTL support.
Optimize CodeRabbit API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for CodeRabbit integrations. Trigger with phrases like "coderabbit performance", "optimize coderabbit", "coderabbit latency", "coderabbit caching", "coderabbit slow", "coderabbit batch".
Implements Syncfusion DataManager for local/remote binding, CRUD, querying, caching, and middleware. Supports JsonAdaptor, ODataAdaptor, ODataV4Adaptor, UrlAdaptor, WebApiAdaptor, WebMethodAdaptor, RemoteSaveAdaptor, GraphQLAdaptor, CustomDataAdaptor, and CustomAdaptor. Covers Query class, filtering, sorting, paging, grouping, persistence, offline mode, caching, and error handling.
Prevent silent decimal mismatch bugs across EVM chains. Covers runtime decimal lookup, chain-aware caching, bridged-token precision drift, and safe normalization for bots, dashboards, and DeFi tools.
Modern React data fetching patterns. Use when implementing caching, deduplication, optimistic updates, or parallel loading with TanStack Query, SWR, or Suspense.
Data file fetching and caching for geoscience applications. Download sample datasets with automatic caching, checksum verification, and multiple download sources. Use when Claude needs to: (1) Download datasets from URLs or DOIs, (2) Cache files locally with automatic verification, (3) Verify file integrity with SHA256/MD5 hashes, (4) Extract compressed archives (ZIP, TAR, GZIP), (5) Create data registries for reproducible workflows, (6) Fetch from Zenodo or other repositories.
Design and optimize systems for high concurrency, throughput, scalability, and elastic scale—concurrency models (threads, async/await, actors), lock-free patterns, connection pooling, caching stampede mitigation, horizontal scaling, load balancing, backpressure, queueing, rate limiting, bulkheads, read replicas, sharding, pool tuning, profiling, capacity planning, SLO-driven autoscaling, multi-region and CDN edge architecture. Use when the user asks about high concurrency, scalability, throughput, horizontal scaling, connection pooling, backpressure, rate limiting, caching stampede, read replica, sharding, autoscaling, capacity planning, lock contention, async scalability, or load balancing—not service decomposition (microservices-developer), event buses only (event-driven-architecture), generic CRUD (senior-software-engineer), SRE on-call only (site-reliability-engineer), load tests without architecture (performance-engineer), or cost-only FinOps (cloud-economist).
Build backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.