/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.
Claude Code
$ npx -y skills add wshobson/agents --skill vector-index-tuning --agent claude-code
Codex
$ npx skills add wshobson/agents --skill vector-index-tuning -a codex -y
Cursor
$ npx skills add wshobson/agents --skill vector-index-tuning -a cursor -y
Gemini CLI
$ npx skills add wshobson/agents --skill vector-index-tuning -a gemini-cli -y
GitHub Copilot
$ npx skills add wshobson/agents --skill vector-index-tuning -a github-copilot -y
OpenCode
$ npx skills add wshobson/agents --skill vector-index-tuning -a opencode -y
Windsurf
$ npx skills add wshobson/agents --skill vector-index-tuning -a windsurf -y
SKILL.md
vector-index-tuning.SKILL.mdname: vector-index-tuning
description: Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
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 and detailed worked examples
Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.
Best Practices
Do's
- **Benchmark with real queries** - Synthetic may not represent production
- **Monitor recall continuously** - Can degrade with data drift
- **Start with defaults** - Tune only when needed
- **Use quantization** - Significant memory savings
- **Consider tiered storage** - Hot/cold data separation
Don'ts
- **Don't over-optimize early** - Profile first
- **Don't ignore build time** - Index updates have cost
- **Don't forget reindexing** - Plan for maintenance
- **Don't skip warming** - Cold indexes are slow
Read more
name: vector-index-tuning description: Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
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 and detailed worked examples
Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.
Best Practices
Do's
- **Benchmark with real queries** - Synthetic may not represent production
- **Monitor recall continuously** - Can degrade with data drift
- **Start with defaults** - Tune only when needed
- **Use quantization** - Significant memory savings
- **Consider tiered storage** - Hot/cold data separation
Don'ts
- **Don't over-optimize early** - Profile first
- **Don't ignore build time** - Index updates have cost
- **Don't forget reindexing** - Plan for maintenance
- **Don't skip warming** - Cold indexes are slow
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Questions about vector-index-tuning.
- Which coding agents can use vector-index-tuning?
- vector-index-tuning installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

