implementing-search-filter
Search & Filter Implementation
Implement search and filter interfaces with comprehensive frontend components and backend query optimization.
Purpose
This skill provides production-ready patterns for implementing search and filtering functionality across the full stack. It covers React/TypeScript components for the frontend (search inputs, filter UIs, autocomplete) and Python patterns for the backend (SQLAlchemy queries, Elasticsearch integration, API design). The skill emphasizes performance optimization, accessibility, and user experience.
When to Use
- Building product search with category and price filters
- Implementing autocomplete/typeahead search
- Creating faceted search interfaces with dynamic counts
- Adding search to data tables or lists
- Building advanced boolean search for power users
- Implementing backend search with SQLAlchemy or Django ORM
- Integrating Elasticsearch for full-text search
- Optimizing search performance with debouncing and caching
- Creating accessible search experiences
More from ancoleman/ai-design-components
creating-dashboards
Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.
245implementing-drag-drop
Implements drag-and-drop and sortable interfaces with React/TypeScript including kanban boards, sortable lists, file uploads, and reorderable grids. Use when building interactive UIs requiring direct manipulation, spatial organization, or touch-friendly reordering.
164administering-linux
Manage Linux systems covering systemd services, process management, filesystems, networking, performance tuning, and troubleshooting. Use when deploying applications, optimizing server performance, diagnosing production issues, or managing users and security on Linux servers.
127security-hardening
Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.
109building-ai-chat
Builds AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support. Use when creating ChatGPT-style interfaces, AI assistants, code copilots, or conversational agents. Handles streaming text, token limits, regeneration, feedback loops, tool usage visualization, and AI-specific error patterns. Provides battle-tested components from leading AI products with accessibility and performance built in.
74designing-distributed-systems
When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.
52