user-search-strategies

user-search-strategies is a skill for Claude Code from 2aronS/qdrant-scale. It costs 92 tokens per session (1,225 once invoked), scanned A, original, no licence file.

A guide for choosing search strategies. It covers hybrid search, reranking, result diversity, maximum marginal relevance, and relevance feedback.

In plain words
What is it for?
Use it to choose retrieval and ranking methods for poor relevance, insufficient diversity, or missing results.
Why use it?
It helps decide how to improve search when relevant items exist but are not being returned or results are too similar.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the user plugin — 26 skills shipped together

Good fit Use it to choose retrieval and ranking methods for poor relevance, insufficient diversity, or missing results.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/2arons/qdrant-scale/search-strategies/github.svg)](https://agentmods.dev/skills/2arons/qdrant-scale/search-strategies)
Your own site
<a href="https://agentmods.dev/skills/2arons/qdrant-scale/search-strategies"><img src="https://agentmods.dev/badge/skills/2arons/qdrant-scale/search-strategies/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for user-search-strategies

Your own site · 80×15
<a href="https://agentmods.dev/skills/2arons/qdrant-scale/search-strategies"><img src="https://agentmods.dev/badge/skills/2arons/qdrant-scale/search-strategies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,225 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 $0.00092 $0.01225
Opus 5 $0.00046 $0.00613
Sonnet 5 $0.00018 $0.00245
Haiku 4.5 $0.00009 $0.00122

Measured 9d ago against content hash 83d13d43007c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

user-search-strategies 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 9d 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-search-quality/search-strategies/SKILL.md · 69 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

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 9d ago First seen · 69 lines · 92 tokens per session scan A 83d13d43007c

Subscribe to this mod's changes

user-search-strategies is a skill published in the GitHub repository 2aronS/qdrant-scale (5 stars, last pushed 3mo ago), with no licence file. It adds 92 tokens to every session and 1,225 once invoked, about $0.0005 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

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

llm-deployment

Use when lLM deployment and serving — vLLM, Ollama, TGI, llama.cpp. Model quantization, GPU optimization, API serving. Use when working with llm deployment.

oyi77/1ai-skills · 44 tokens

Inference

Guides model-serving and runtime-inference decisions across local, remote, and packaged deployment paths.

agentic-in/elephant-agent · 21 tokens

Vector Databases

Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.

agentic-in/elephant-agent · 26 tokens