screen-reader-testing
Test web applications with screen readers including VoiceOver, NVDA, and JAWS. Use when validating screen reader compatibility, debugging accessibility issues,…
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
$ npx -y skills add wshobson/agents --skill vector-index-tuning --agent claude-codeHow it fires
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Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
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.
Guide to optimizing vector indexes for production performance.
Data Size Recommended Index ──────────────────────────────────────── < 10K vectors → Flat (exact search) 10K - 1M → HNSW 1M - 100M → HNSW + Quantization > 100M → IVF + PQ or DiskANN
| 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 |
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
Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.
Production-ready agentic workflow building blocks: 94 plugins, 202 agents, 183 skills, 105 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, the Antigravity CLI, GitHub Copilot, and Pi from a single Markdown source.
Repo: wshobson/agents
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