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vector-index-tuning

8.1

by wshobson

59Favorites
426Upvotes
0Downvotes

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

vector-search

8.1

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0

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Data & Analytics

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Quick Review

Excellent skill for vector index optimization with comprehensive parameter tuning guidance, quantization strategies, and production-ready code templates. The description clearly covers when to use the skill (HNSW tuning, quantization, scaling), and the content delivers substantial task knowledge including benchmarking functions, memory estimation, and platform-specific configurations (Qdrant). Structure is clean with well-organized templates and clear parameter tables. Novelty is solid - while vector search is a known domain, the detailed parameter recommendations, quantization comparisons, and performance monitoring code would require significant token expenditure for a CLI agent to replicate. Minor room for improvement: could include more advanced topics like hybrid search or specific cloud platform optimizations, and performance monitoring could be more extensive. Overall, this is a highly practical skill that meaningfully reduces complexity for vector search optimization tasks.

LLM Signals

Description coverage8
Task knowledge9
Structure8
Novelty7

GitHub Signals

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Last commit 3 days ago

Publisher

wshobson

wshobson

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