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- Add date_added to all 950+ skills for complete tracking - Update version to 6.5.0 in package.json and README - Regenerate all indexes and catalog - Sync all generated files Features from merged PR #150: - Stars/Upvotes system for community-driven discovery - Auto-update mechanism via START_APP.bat - Interactive Prompt Builder - Date tracking badges - Smart auto-categorization All skills validated and indexed. Made-with: Cursor
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name, description, risk, source, date_added
| name | description | risk | source | date_added |
|---|---|---|---|---|
| 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. | unknown | community | 2026-02-27 |
Vector Index Tuning
Guide to optimizing vector indexes for production performance.
Use this skill when
- Tuning HNSW parameters
- Implementing quantization
- Optimizing memory usage
- Reducing search latency
- Balancing recall vs speed
- Scaling to billions of vectors
Do not use this skill when
- You only need exact search on small datasets (use a flat index)
- You lack workload metrics or ground truth to validate recall
- You need end-to-end retrieval system design beyond index tuning
Instructions
- Gather workload targets (latency, recall, QPS), data size, and memory budget.
- Choose an index type and establish a baseline with default parameters.
- Benchmark parameter sweeps using real queries and track recall, latency, and memory.
- Validate changes on a staging dataset before rolling out to production.
Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.
Safety
- Avoid reindexing in production without a rollback plan.
- Validate changes under realistic load before applying globally.
- Track recall regressions and revert if quality drops.
Resources
resources/implementation-playbook.mdfor detailed patterns, checklists, and templates.