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.
These skills are under active development. Skill content and structure may change between versions as Qdrant evolves.
Install using the plugin marketplace:
/plugin marketplace add qdrant/skills
Install from the Cursor Marketplace or add manually via Settings > Rules > Add Rule > Remote Rule (Github) with qdrant/skills.
Install using the npx skills CLI:
npx skills add https://github.com/qdrant/skills
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 |
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 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 |
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 |
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
If you are interested in contributing follow the instructions in CONTRIBUTING.md.