qdrant
By qdrant
Equip AI agents to manage Qdrant vector databases: integrate SDKs across languages, select optimal deployments from local to cloud, perform zero-downtime model migrations and version upgrades, scale clusters, tune performance and search relevance, and set up monitoring with Prometheus.
npx claudepluginhub qdrant/skills --plugin qdrantComponent Overview
Component Details
Skills (8)
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
Guides 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.
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.
Guides 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'.
Diagnoses 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?'. Also use when search quality degrades after quantization, model change, or data growth.
Guidance on how to upgrade your Qdrant version without interrupting the availability of your application and ensuring data integrity.
README
Qdrant Skills - Agent Skills for Qdrant Vector Search
Agent skills for building with Qdrant vector search
Skills encode deep Qdrant knowledge so coding agents can make the engineering decisions that determine whether vector search works well: quantization, sharding, tenant isolation, hybrid search, model migration, and more.
Philosophy
Skills are not documentation. Qdrant already has docs in markdown. Skills answer "when?" and "why?", not "how?"
They are structured as the handbook of a Solutions Architect working on Qdrant: given a problem, navigate to the exact place in the documentation where the answer lives. No tutorials, no concept explanations. Only references and minimal snippets where absolutely necessary.
Disclaimer
These skills are under active development. Skill content and structure may change between versions as Qdrant evolves.
Installation
npx skills
Install using the npx skills CLI:
npx skills add qdrant/skills
Claude Code
Add the marketplace, then install all Qdrant skills:
/plugin marketplace add qdrant/skills
/plugin install qdrant@qdrant
Cursor
Install from the Cursor Marketplace or add manually via Settings > Rules > Add Rule > Remote Rule (GitHub) with qdrant/skills.
Clone / Copy
Clone this repo and copy the skill folders into the appropriate directory for your agent:
| Agent | Skill Directory | Docs |
|---|---|---|
| Claude Code | ~/.claude/skills/ | docs |
| Cursor | .cursor/skills/ | docs |
| OpenCode | ~/.config/opencode/skill/ | docs |
| OpenAI Codex | ~/.codex/skills/ | docs |
| Pi | ~/.pi/agent/skills/ | docs |
Quick Start
After installing, just ask your agent about Qdrant. Skills are triggered automatically when your question matches their description.
"I have 50M vectors on a single node and search is slow, should I add more nodes?"
→ qdrant-scaling skill activates, recommends quantization and vertical scaling before adding nodes
"My search results are returning irrelevant matches"
→ qdrant-search-quality skill activates, walks through diagnosis and search strategy options
"How do I switch from OpenAI embeddings to Cohere without downtime?"
→ qdrant-model-migration skill activates, guides zero-downtime migration with dual vectors
Skills
Skills are triggered automatically when your question matches their description.
| Skill | Useful for |
|---|---|
| qdrant-clients-sdk | SDK setup, code examples, snippet search across Python, TypeScript, Rust, Go, .NET, Java |
| qdrant-scaling | Scaling decisions: data volume, QPS, latency, query volume, horizontal vs vertical |
| qdrant-performance-optimization | Search speed, memory usage, indexing performance |
| qdrant-search-quality | Diagnosing bad results, search strategies, hybrid search |
| qdrant-monitoring | Metrics, health checks, debugging optimizer and cluster issues |
| qdrant-deployment-options | Choosing between local, self-hosted, cloud, and hybrid |
| qdrant-model-migration | Switching embedding models without downtime |
| qdrant-version-upgrade | Safe upgrade paths, compatibility guarantees, rolling upgrades |
MCP Servers
For additional Qdrant context, pair skills with these MCP servers:
| Server | Purpose |
|---|---|
| mcp-code-snippets | Search Qdrant docs and code examples across all SDKs |
| mcp-server-qdrant | Store and retrieve memories, manage collections directly |
Getting Help
Found a bug or wrong advice in a skill? Open an issue on GitHub and include:
- The skill name
- The prompt you gave your agent
- What the agent said vs what it should have said
Contributing
If you are interested in contributing, follow the instructions in CONTRIBUTING.md.
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