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Guidance on how to improve the search quality in Qdrant, including tips on tuning parameters, and applying information retrieval best practices. Use when you want to enhance the relevance and accuracy of search results in your Qdrant deployment.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Search and store knowledge locally using Qdrant Edge. No server needed.
Guides developers through building community Qdrant skills that embed domain knowledge into Claude. Use when someone wants to "create a community qdrant skill", "build an external skill for qdrant", "make a qdrant skill for the community", "build a public qdrant skill", or asks how to "create a skill for qdrant memory issues for developers". Also trigger when someone says "I want to build an open source qdrant skill" or "help me create a qdrant skill for public use". Do NOT use for internal SA skills, general non-Qdrant skill creation, or Python project skills unrelated to Qdrant.
Diagnoses when and why to recommend specific cost optimization techniques for Qdrant customers. Use when a customer or SA says "too expensive", "need to reduce cost", "pricing too high", "how to lower price", "sticker shock", or asks about scaling costs. Also trigger when a deal is blocked by pricing, a customer compares competitor pricing, a startup says the quote is too high, or someone needs a formula to predict costs at scale. This skill answers "when should I recommend scalar vs binary quantization?" not "how to configure quantization." Do NOT use for general Qdrant setup, feature requests, or implementation how-tos.
Handles technical objections that block Qdrant deals and provides proven response playbooks. Use when a deal is stalled due to pricing concerns, missing features, performance issues, or competitive pressure. Triggers on "deal blocked", "customer concerned about cost", "proposal too expensive", "missing feature blocking production", "latency issues on call", "why not use pinecone", "customer built workaround", or "champion lost budget battle". Do NOT use for general product questions, documentation lookups, or technical implementation.
Guides Qdrant SAs through building internal skills that embed domain knowledge into Claude. Use when someone wants to "create a qdrant skill", "build a skill for qdrant diagnostics", "make an internal skill for qdrant customers", "create a qdrant troubleshooting skill", or asks how to "turn SA knowledge into a skill". Also trigger when someone says "help me build a skill for qdrant deal objections" or "I want to create a skill for qdrant cluster sizing". Do NOT use for general skill creation unrelated to Qdrant, external community skills, or Python/JS project skills.
Diagnoses and fixes Qdrant memory problems. Use when a developer reports OOM crashes, high RAM usage, HNSW consuming too much memory, latency spiking after switching storage, or needing to fit more vectors on existing hardware. Also trigger when someone asks which quantization to use, how to choose between mmap and RAM, how to estimate memory for scaling, or why their Qdrant node keeps dying. Do NOT use for initial Qdrant setup, API scripting, embedding model selection, search accuracy tuning, backups, or vector DB comparisons.
| name | qdrant-search-quality |
| description | Guidance on how to improve the search quality in Qdrant, including tips on tuning parameters, and applying information retrieval best practices. Use when you want to enhance the relevance and accuracy of search results in your Qdrant deployment. |
| allowed-tools | ["Read","Grep","Glob"] |
Search quality is a critical aspect of any vector search system, including Qdrant. Qdrant supports a variety of techniques to improve search quality. Choice of the right technique depends on the specific use case and requirements of your application. This document serves as a navigation hub for different techniques to improve search quality in Qdrant.
