qdrant-advisor

Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — slow or degraded search, high or growing memory / OOM crashes, optimizer stuck or slow, indexing slowness, scaling and sharding decisions (node count, QPS, latency, multitenancy, vertical vs horizontal), poor or irrelevant search results, hybrid search and reranking, embedding-model migration, version upgrades and compatibility, monitoring and observability (Prometheus, Grafana, health checks, /metrics, /telemetry), deployment choices (local, Docker, self-hosted, Qdrant Cloud, embedded), or client-SDK questions (Python, TypeScript, Rust, Go, .NET, Java). Trigger especially when the context is clearly a Qdrant cluster, collection, or vector-search deployment. Always prefer this skill over answering from memory: it pulls current, authoritative guidance and only the relevant context.

v1.0.0New

#qdrant

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Install this skill

Run this command in your terminal. No account required — it auto-detects your AI tool and installs the skill file.

npx @skills-hub-ai/cli install qdrant-qdrant-advisor

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Setup by platform

Claude Code

~/.claude/skills/<skill>/SKILL.md

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Install

One-click setup for your editor

Run in your project root

npx @skills-hub-ai/cli install qdrant-qdrant-advisor --target claude-code

Instructions

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qdrant-horizontal-scalingDiagnoses and guides Qdrant horizontal scaling decisions. Use when someone asks 'vertical or horizontal?', 'how many nodes?', 'how many shards?', 'how to add nodes', 'resharding', 'data doesn't fit', or 'need more capacity'. Also use when data growth outpaces current deployment.0 installs qdrant-scaling-data-volumeGuides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.0 installs qdrant-memory-usage-optimizationDiagnoses and reduces Qdrant memory usage. Use when someone reports 'memory too high', 'RAM keeps growing', 'node crashed', 'out of memory', 'memory leak', or asks 'why is memory usage so high?', 'how to reduce RAM?'. Also use when memory doesn't match calculations, quantization didn't help, or nodes crash during recovery.0 installs qdrant-vertical-scalingGuides Qdrant vertical scaling decisions. Use when someone asks 'how to scale up a node', 'need more RAM', 'upgrade node size', 'vertical scaling', 'resize cluster', 'scale up vs scale out', or when memory/CPU is insufficient on current nodes. Also use when someone wants to avoid the complexity of horizontal scaling.0 installs qdrant-monitoring-debuggingDiagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.0 installs qdrant-scaling-query-volumeGuides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'.0 installs

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qdrant-scalingGuides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', or 'need to add capacity'.0 installs qdrant-edgeGuides building on Qdrant Edge, the embedded in-process shard. Use when someone asks 'how to sync Edge with the server', 'keep a local shard in sync with Qdrant Cloud', 'BM25 or keyword search on Edge', 'hybrid search on Edge', 'embeddings on device', 'Edge snapshots', 'apply a partial snapshot', 'why is my Edge search empty after inserts', or is writing custom sync, BM25, or fusion code against qdrant-edge. Also use when deciding what Edge ships built-in versus what you must implement.0 installs qdrant-search-quality-diagnosisDiagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', 'quality dropped after quantization', 'how to measure retrieval quality', 'build a golden set', 'ground truth dataset', or 'how to score recall@k'. Also use when search quality degrades without obvious changes.0 installs qdrant-search-qualityDiagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?', 'how to measure retrieval quality?', 'build a golden set', 'ground truth dataset', or 'how to score recall@k?'. Also use when search quality degrades after quantization, model change, or data growth.0 installs qdrant-monitoringGuides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', or needs to set up Prometheus, Grafana, or health checks. Also use when debugging production issues that require metric analysis.0 installs qdrant-relevance-feedbackExpanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense search relevance/recall', 'how to discover/get more relevant results from vector search', 'cheaper/better alternative to reranking', 'using a more heavy/big embedding model for dense search but can't afford it', 'finding more relevant documents beyond the initial search pool', or 'feedback loops'. Also trigger when the user has a search quality problem due to a dense retriever being weak and is considering reranking as a solution — this API may be a better fit0 installs

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skill-creatorCreate new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.6 installs skillifyCreate a reusable, self-healing, self-evolving global skill — either from the current conversation context OR from a plain-language description of a workflow. Triggers on: 'turn this into a skill', 'make a skill from this', 'skillify this', 'capture this as a skill', 'save this workflow as a skill', 'create a skill for this', 'make this repeatable', 'skill this up', 'build me a skill that does X', 'I want a skill for Y', or any time a workflow just played out and the user wants to preserve it. Invoke even if the user only hints at wanting to reuse what just happened.6 installs load-contextLoad a saved conversation context to resume previous work. Supports local files, git repos, and URLs. Triggered by 'load context', 'resume previous work', 'pick up where I left off', 'load saved session', 'reload context'.3 installs publish-skillPublish a locally installed skill to the skills-hub registry and sync it to the production platform. Use when: 'publish skill X', 'push skill to hub', 'add skill to registry', 'publish to skills hub', 'share this skill', 'make skill available globally'. Runs entirely from the Mac Studio — no VPS needed.3 installs extract-templateExtracts a reusable project template from a mature project — captures architecture, pipeline, conventions, CI/CD, Docker, testing, linting, and pitfalls into a portable template. Trigger: extract template, save as template, capture conventions, reusable template, templatize project, snapshot conventions.2 installs evolveSelf-improving skill system. Reads /recall and /metrics output, identifies which skills need patching based on rework patterns and regression data, generates additive patches, and logs changes. Evolves skills based on learnings from any tech stack.2 installs

Frequently asked questions about qdrant-advisor

What does the qdrant-advisor skill do?

How do I install the qdrant-advisor skill?

Run `npx @skills-hub-ai/cli install qdrant-qdrant-advisor` from your terminal. The CLI writes the SKILL.md to the correct location for your AI tool (e.g. ~/.claude/skills/qdrant-qdrant-advisor/ for Claude Code or ~/.cursor/skills/ for Cursor with --target cursor) and adds it to your project's .skills.json lockfile.

Which AI tools does qdrant-advisor work with?

qdrant-advisor runs in Claude Code. It follows the open Agent Skills standard (SKILL.md), so the same skill works in every supported tool without modification.

Is the qdrant-advisor skill free?

Yes. Every skill on skills-hub.ai is free and open-source. There are no premium tiers, paywalls, or usage limits. You only pay for whatever AI assistant you're already using.

How do I use qdrant-advisor after installing it?

In Claude Code, type `/qdrant-qdrant-advisor` (or whatever slash command the skill registers) and the AI follows the skill's instructions immediately. You can also reference it by name in natural language, your AI loads the skill into context when relevant.

Can I share the qdrant-advisor skill with my team?

Yes. Commit your project's .skills.json lockfile and teammates run `npx @skills-hub-ai/cli install` (no args) to install every skill at the exact version you pinned. Organization-scoped installs work via skills-hub.ai organizations.