qdrant
Qdrant vector database REST API via curl. Use this skill to store, search, and manage vector embeddings.
npx skills add majiayu000/claude-skill-registry --skill qdrant-neversight-skills-feed --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Qdrant API Use the Qdrant REST API via direct `curl` calls to **store and search vector embeddings** for RAG, semantic search, and recommendations. > Official docs: `https://qdrant.tech/documentation/` --- ## When to Use Use this skill when you need to: - **Store vector embeddings** for semantic search - **Search for similar vectors** using cosine, dot product, or euclidean distance - **Build RAG applications** with retrieval from vector store - **Implement recommendations** based on similarity - **Filter search results** by metadata/payload --- ## Prerequisites ### Option 1: Qdrant Cloud (Recommended) 1. Sign up at [Qdrant Cloud](https://cloud.qdrant.io/) 2. Create a cluster and get your URL and API key 3. Store credentials in environment variables ```bash export QDRANT_URL="https://xyz-example.aws.cloud.qdrant.io:6333" export QDRANT_API_KEY="your-api-key" ``` ### Option 2: Self-hosted Run Qdrant locally with Docker: ```bash docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant ``` ```bash export QDRANT_URL="http://localhost:6333" export QDRANT_API_KEY="" # Optional for local ``` --- > **Important:** When using `$VAR` in a command that pipes to another command, wrap the command con
- When to Use
- Prerequisites
- Option 1: Qdrant Cloud (Recommended)
- Option 2: Self-hosted
- How to Use
- 1. Check Server Status
- 2. List Collections
- 3. Create a Collection
- 4. Get Collection Info
- 5. Upsert Points (Insert/Update Vectors)
- 6. Search Similar Vectors
- 7. Search with Filters
- 8. Get Points by ID
- 9. Delete Points
export QDRANT_URL="https://xyz-example.aws.cloud.qdrant.io:6333"
export QDRANT_API_KEY="your-api-key"
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
export QDRANT_URL="http://localhost:6333"
export QDRANT_API_KEY="" # Optional for local
bash -c 'curl -s "https://api.example.com" -H "Authorization: Bearer $API_KEY"'
bash -c 'curl -s -X GET "${QDRANT_URL}" --header "api-key: ${QDRANT_API_KEY}"'
bash -c 'curl -s -X GET "${QDRANT_URL}/collections" --header "api-key: ${QDRANT_API_KEY}"'
bash -c 'curl -s -X GET "${QDRANT_URL}/collections/my_collection" --header "api-key: ${QDRANT_API_KEY}"'
bash -c 'curl -s -X DELETE "${QDRANT_URL}/collections/my_collection" --header "api-key: ${QDRANT_API_KEY}"'What does the qdrant skill do?
Qdrant vector database REST API via curl. Use this skill to store, search, and manage vector embeddings.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill qdrant-neversight-skills-feed --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.