Results for “search-performance”

17 skills
More results
github
qdrant-minimize-latency
Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.
36.2k
kk20300113-png
benchmark
Performance regression detection using the browse daemon. Establishes baselines for page load times, Core Web Vitals, and resource sizes. Compares before/after on every PR. Tracks performance trends over time. Use when: "performance", "benchmark", "page speed", "lighthouse", "web vitals", "bundle size", "load time". (gstack) Voice triggers (speech-to-text aliases): "speed test", "check performance".
0
livelybug
benchmark
Performance regression detection using the browse daemon. (gstack)
0
keyargo
web-search
Search the web using DuckDuckGo and return top results
118 · bundle
haongo232
performance-profiling
Performance profiling principles. Measurement, analysis, and optimization techniques.
3 · bundle
ekatasingh1107
keyword-miner
Find keyword gaps, SERP opportunities, and low-competition high-intent keywords
2 · bundle
owl-listener
search-ux
Design search experiences that help users find what they need, recover from failure, and refine results.
1.7k
nickgallick
performance
Performance — Forge Skill
0
keyargo
news-search
Search for recent news articles on a given topic
118 · bundle
memento-teams
web-search
Search the web and fetch content from URLs, returning LLM-friendly markdown.
1.5k · bundle
leandrobenjaminl
perf-engineer
Diagnose and eliminate performance bottlenecks across web, API, database, and code with load testing, profiling, and optimization techniques.
0
nvidia
tilegym-cutile-autotuning
Adds autotuning to CuTile kernels using the exhaustive_search API with a tune-once/cache/direct-launch pattern, covering occupancy-only and complex tile-size search spaces.
2.2k · bundle
github
qdrant-scaling-qps
Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.
36.2k
jackychenlu
faiss
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
0 · bundle