user-scaling-qps

user-scaling-qps is a skill for Claude Code from 2aronS/qdrant-scale. It costs 57 tokens per session (789 once invoked), scanned A, original, no licence file.

A guide for increasing query throughput, meaning the number of queries a system can handle per second, and supporting more simultaneous requests.

In plain words
What is it for?
Use it when working on QPS, batch search, read replicas, query performance, or higher concurrent-query capacity.
Why use it?
It helps diagnose or plan for systems that are too slow, cannot handle enough concurrent queries, or need to process searches in batches.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the user plugin — 26 skills shipped together

Good fit Use it when working on QPS, batch search, read replicas, query performance, or higher concurrent-query capacity.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/2arons/qdrant-scale/scaling-qps
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add 2aronS/qdrant-scale --skill scaling-qps
Clone the repo
git clone --depth 1 https://github.com/2aronS/qdrant-scale

Made for: Claude Code.

Or install user, the plugin that ships this one along with the rest of its 26 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for user-scaling-qps

README.md
[![agentmods](https://agentmods.dev/badge/skills/2arons/qdrant-scale/scaling-qps.svg)](https://agentmods.dev/skills/2arons/qdrant-scale/scaling-qps)
Your own site
<a href="https://agentmods.dev/skills/2arons/qdrant-scale/scaling-qps"><img src="https://agentmods.dev/badge/skills/2arons/qdrant-scale/scaling-qps.svg" alt="Measured on agentmods" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 789 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00057 $0.00789
Opus 5 $0.00028 $0.00394
Sonnet 5 $0.00011 $0.00158
Haiku 4.5 $0.00006 $0.00079

Measured 7d ago against content hash f6d268c27f2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

user-scaling-qps scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/user-scaling/scaling-qps/SKILL.md · 59 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 59 lines · 57 tokens per session scan A f6d268c27f2b

Subscribe to this mod's changes

user-scaling-qps is a skill published in the GitHub repository 2aronS/qdrant-scale (5 stars, last pushed 3mo ago), with no licence file. It adds 57 tokens to every session and 789 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

fly-io

Use when deploying or operating an app on Fly.io — writing fly.toml, placing Machines in regions near users, attaching Volumes, managing secrets, or picking a scaling lever (autostop/autostart, scale count, fly-replay). NOT choosing which host to deploy on (that is deployment), NOT a git-push PaaS with no regions…

ericrisco/rsc-harness · 87 tokens

doris-debug-deployment

Use for Doris FE/BE startup failures, port conflicts, prioritynetworks misrouting, metadir corruption, and ADD/DROP BACKEND issues.

apache/doris-skills · 36 tokens

chroma

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…

synthetic-sciences/openscience · 63 tokens

qdrant-vector-search

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

synthetic-sciences/openscience · 46 tokens

pinecone

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

synthetic-sciences/openscience · 63 tokens

ci-cd-pipeline

Design and implement CI/CD pipelines for automated testing and deployment.

furkangonel/cowrangler · 18 tokens