Vector Database Optimizer
$2.99OfficialOptimize vector databases: index selection (HNSW, IVF), parameter tuning, metadata filtering, and cost-performance tradeoffs.
datavector-databasehnswannperformance-tuning· by SkillingMain
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 3 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when vector search recall or latency is missing targets, when index memory or build time is blowing the budget, or when designing a new vector store at scale. It applies to dedicated vector DBs (Pinecone, Weaviate, Qdrant, Milvus) and libraries (FAISS, pgvector, ScaNN). Reach for it whenever you're tuning approximate nearest neighbor (ANN) search rather than just storing vectors.
Inputs to gather
- Vector count and dimensionality
- Query QPS and p50/p95/p99 latency targets
- Recall@k requirements (exact or acceptable approximation)
- Metadata filtering needs (pre-filter, post-filter, hybrid)
- Index build time and memory budget
- Update pattern (mostly sta
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