Terminal Ui

Guide to using Tailwind CSS in Capacitor mobile apps. Covers mobile-first design, touch targets, safe areas, dark mode, and performance optimization. Use this skill when users want to style Capacitor apps with Tailwind.

Audits built React and Next.js frontends for user-facing defects in code and rendered UI: state gaps, data loss, double submits, focus and keyboard failures, rollback gaps, stale async, accessibility markup, layout resilience, performance setup, typography, motion hazards, and microcopy. Returns file:line findings and a ship verdict. Use when asked to "review this PR for UX bugs", "audit this component", "check my UI", "is this accessible", "design QA this page", or "is this ready to ship". For product decisions use product-design; for agentic apps use ax-audit; for deep type or motion use typography-audit or ui-animation; for the wording of a string rather than whether it is missing or leaky, use copywriting.

Ghost CMS theme development with Handlebars templating, Alpine.js, Tailwind CSS, and performance optimization

Evidence-based memory optimization from real usage patterns. Analyzes recall performance, identifies bottlenecks, suggests consolidation/pruning/enrichment, and tracks improvement over time via checkpoint Q&A.

Comprehensive guidelines for building scalable Django REST APIs with proper architecture, authentication, and performance optimization.

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.