Skip to main content
affpapa.org
RU EN
skill GitHub Copilot official · GitHub

qdrant-performance-optimization — GitHub Copilot skill

This skill maps Qdrant performance work across search speed, index building, and memory use. Use it when a deployment needs a focused performance review.

Install into GitHub Copilot · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add github/awesome-copilot --skill qdrant-performance-optimization
Open on GitHub

What qdrant-performance-optimization does

qdrant-performance-optimization acts as a navigation hub for Qdrant performance work. It splits the subject into search latency and throughput, vector index build time, and memory use for large datasets.

The skill links to separate skills for search speed, indexing, and memory management. Its allowed tools are Read, Grep, and Glob. The guidance takes dataset size, hardware, and configuration into account, and covers how to decide which storage is kept in memory and which stays on disk.

Install it when you want GitHub Copilot to organize a Qdrant performance investigation across these areas. For a single narrow issue, use the matching linked skill instead.

What it helps with

  • investigate Qdrant search latency and throughput
  • shorten vector index build time
  • control which Qdrant data stays in memory
  • choose a performance area to review first

Example prompts for the agent

  • Review this Qdrant deployment and identify the first performance checks.
  • Help me reduce search latency in this Qdrant configuration.
  • Analyze memory use and index build performance for this dataset.

How to install qdrant-performance-optimization

1Skills CLI (Vercel)

npx skills add github/awesome-copilot --skill qdrant-performance-optimization

2GitHub CLI

gh skill install github/awesome-copilot qdrant-performance-optimization

3Manually: put the files in place

.github/skills/qdrant-performance-optimization/SKILL.md

From the same set554

The whole set →
git-commit official github This skill reviews Git changes, stages related files, and creates Conventional Commits. Use it when you want an agent to make one commit or split work into separate commits. GitHub Copilot 47,165installs excalidraw-diagram-generator official github Turn a plain-language description into an Excalidraw diagram. Useful for mapping processes, system structure, and relationships between entities. GitHub Copilot 31,639installs multi-stage-dockerfile official github This skill guides GitHub Copilot in writing multi-stage Dockerfiles for different languages and frameworks. Use it when the final runtime image should contain fewer build dependencies. GitHub Copilot 29,688installs documentation-writer official github Skill that turns the agent into a technical writer following the Diátaxis framework: tutorials, how-to guides, reference, and explanation. Proposes structure before writing. GitHub Copilot 27,757installs prd official github Turns a feature idea into a structured PRD with user stories, technical requirements, and risks. Use it when planning a software feature or an AI-powered one. GitHub Copilot 24,035installs refactor official github This skill reshapes existing code without changing its external behavior. Use it for gradual cleanup of large functions, duplication, weak types, and code smells. GitHub Copilot 22,321installs java-springboot official github Gives GitHub Copilot Spring Boot development practices, from project layout to tests and security. Use it when writing or reviewing Java application code. GitHub Copilot 21,272installs create-readme official github This skill reviews a project and creates a structured README.md. Use it when the repository needs clear documentation based on its actual files and code. GitHub Copilot 19,081installs

Similar

xlsx official anthropics Creates, reads, edits, and converts spreadsheet files. Use it when the requested deliverable is an Excel workbook or another tabular file. Claude Code Claude.ai 187,223installs google-agents-cli-observability official Google This skill guides you through tracing and monitoring AI agents, including ADK agents. Use it when you need to inspect agent execution or monitor a deployed agent. Claude Code CX Codex Cursor +1 509,900installs caveman-evidence-review JuliusBrussee A read-only skill reviews Caveman Cloud evidence on LLM spend, traces, and routing. Use it to see where a project’s model budget goes. Claude Code CX Codex Cursor +1 103,500installs migration JuliusBrussee Enforces reversible, compatibility-safe transitions for schema, data, and API updates. Useful whenever data loss or rollout downtime is unacceptable. Claude Code CX Codex Cursor +1 102,900installs performance-optimization addyosmani An agent skill that measures before it changes code: Core Web Vitals budgets, N+1 queries, missing indexes and slow endpoints. Claude Code CX Codex Cursor +1 64,500installs supabase official supabase Guides the agent through database configuration and strict RLS policies in Supabase. CX Codex GP ChatGPT 8installs

Questions about qdrant-performance-optimization

How do I install qdrant-performance-optimization in GitHub Copilot?

Run npx skills add github/awesome-copilot --skill qdrant-performance-optimization. The skill is installed at .github/skills/qdrant-performance-optimization/SKILL.md.

What does qdrant-performance-optimization do?

It organizes Qdrant performance work around search speed, index building, and memory usage, with links to a dedicated skill for each area.

Which tools does the skill use?

The skill allows Read, Grep, and Glob. It also points to separate skills covering search speed, indexing performance, and memory usage.

Source text

SKILL.md · MIT 
---
name: qdrant-performance-optimization
description: "Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment."
allowed-tools:
  - Read
  - Grep
  - Glob
---


# Qdrant Performance Optimization

There are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.


## Search Speed Optimization

There are two different criteria for search speed: latency and throughput. 
Latency is the time it takes to get a response for a single query, while throughput is the number of queries that can be processed in a given time frame.
Depending on your use case, you may want to optimize for one or both of these metrics.

More on search speed optimization can be found in the [Search Speed Optimization](search-speed-optimization/SKILL.md) skill.


## Indexing Performance Optimization

Qdrant needs to build a vector index to perform efficient similarity search. The time it takes to build the index can vary depending on the size of your dataset, hardware, and configuration.

More on indexing performance optimization can be found in the [Indexing Performance Optimization](indexing-performance-optimization/SKILL.md) skill.


## Memory Usage Optimization

Vector search can be memory intensive, especially when dealing with large datasets.
Qdrant has a flexible memory management system, which allows you to precisely control which parts of storage are kept in memory and which are stored on disk. This can help you optimize memory usage without sacrificing performance.

More on memory usage optimization can be found in the [Memory Usage Optimization](memory-usage-optimization/SKILL.md) skill.

© github · MIT · github/awesome-copilot

See also

Project sponsors

Companies that keep this analytics open
Gold sponsor
Nutra.Club

Impressions, clicks and code copies — open statistics. We keep the numbers public for the same reason NeBlask itself is public. Become a sponsor