qdrant

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.

Usage

Skills are hosted at skills.qdrant.tech. Pass the URL of a skill directly to your agent — no installation required:

Use skills.qdrant.tech

This keeps your agent's context focused: it fetches only the skill relevant to your current problem rather than loading everything upfront.

If you prefer skills to be available offline or without passing URLs manually, you can install them locally. Refer to the Installation section.

Quick Start

The recommended way is the URL method: ask your agent about Qdrant and pass skills.qdrant.tech in your prompt.

"I have 50M vectors on a single node and search is slow, should I add more nodes? Use skills.qdrant.tech"

"My search results are returning irrelevant matches. Use skills.qdrant.tech"

If you use the installation method, 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

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-edge	Building on the embedded shard: server sync, on-device BM25, snapshots, reuse vs reimplement
qdrant-model-migration	Switching embedding models without downtime
qdrant-version-upgrade	Safe upgrade paths, compatibility guarantees, rolling upgrades

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

MCP Servers

For additional Qdrant context, pair skills with these MCP servers: