Solid patterns for building semantic search systems with vector databases like Pinecone and Qdrant. Covers the essentials you actually need: distance metrics, indexing strategies, batch operations, and hybrid search with reranking. The templates handle real production concerns like quantization for memory efficiency, filtering, and proper batching. One thing I appreciate is it doesn't oversell approximate search accuracy, giving you realistic recall expectations for different index types. If you're building RAG systems or recommendation engines, this gives you working code that scales beyond toy datasets.
npx -y skills add wshobson/agents --skill similarity-search-patterns --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
juliusbrussee/caveman
mattpocock/skills
shadcn/improve
obra/superpowers
forrestchang/andrej-karpathy-skills
vercel-labs/skills