This is a seriously full-featured semantic search system that goes way beyond basic vector similarity. You get three retrieval modes: standard dense embeddings for speed, hybrid sparse+dense fusion that benchmarks 20-49% better on mixed queries, and graph RAG for multi-hop reasoning that pulls 30-60% gains on complex questions. The smart mode runs a five-phase pipeline with query expansion, MMR diversity filtering, and recency decay weighting. It searches across namespaced memory stores (patterns, tasks, solutions, feedback, security) and can synthesize context from multiple sources. If you're building anything with conversational memory or need to retrieve related information across sessions, the architecture here is genuinely sophisticated.
npx -y skills add ruvnet/ruflo --skill memory-search --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