vector-index-tuning
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
npx skills add majiayu000/claude-skill-registry --skill vector-index-tuning-balkonsen-ha-ai-gen-workflow --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Vector Index Tuning Guide to optimizing vector indexes for production performance. ## When to Use This Skill - Tuning HNSW parameters - Implementing quantization - Optimizing memory usage - Reducing search latency - Balancing recall vs speed - Scaling to billions of vectors ## Core Concepts ### 1. Index Type Selection ``` Data Size Recommended Index ──────────────────────────────────────── < 10K vectors → Flat (exact search) 10K - 1M → HNSW 1M - 100M → HNSW + Quantization > 100M → IVF + PQ or DiskANN ``` ### 2. HNSW Parameters | Parameter | Default | Effect | | ------------------ | ------- | ---------------------------------------------------- | | **M** | 16 | Connections per node, ↑ = better recall, more memory | | **efConstruction** | 100 | Build quality, ↑ = better index, slower build | | **efSearch** | 50 | Search quality, ↑ = better recall, slower search | ### 3. Quantization Types ``` Full Precision (FP32): 4 bytes × dimensions Half Precision (FP16): 2 bytes × dimensions INT8 Scalar: 1 byte × dimensions Product Quantization: ~32-64 bytes total Binary: dimensions/8 bytes ``` ## Templates ### Template 1: HNSW Parameter Tuning ```python import numpy as np from typing import Li
- When to Use This Skill
- Core Concepts
- 1. Index Type Selection
- 2. HNSW Parameters
- 3. Quantization Types
- Templates
- Template 1: HNSW Parameter Tuning
- Template 2: Quantization Strategies
- Template 3: Qdrant Index Configuration
- Template 4: Performance Monitoring
- Best Practices
- Do's
- Don'ts
What does the vector-index-tuning skill do?
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill vector-index-tuning-balkonsen-ha-ai-gen-workflow --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.