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By weaviate
Query, search, manage, and explore Weaviate vector databases using natural language, semantic/hybrid/keyword methods; import data from PDF/CSV/JSON files; generate example datasets; get AI answers with source citations; bootstrap clusters—all integrated into your coding workflow for rapid AI app prototyping.
npx claudepluginhub weaviate/agent-skills --plugin weaviateAsk questions and get generated answers with sources (Query Agent ask mode)
List all collections in Weaviate or get the schema of an individual collection
Create example data
Explore a Weaviate collection's data, including property metrics and sample objects
Fetch and filter objects from Weaviate collections
Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.
Search, query, and manage Weaviate vector database collections. Use for semantic search, hybrid search, keyword search, natural language queries with AI-generated answers, collection management, data exploration, filtered fetching, data imports from PDF/CSV/JSON/JSONL files, create example data and collection creation.
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Pinecone vector database integration. Streamline your Pinecone development with powerful tools for managing vector indexes, querying data, and rapid prototyping. Use slash commands like /quickstart to generate AGENTS.md files and initialize Python projects and /query to quickly explore indexes. Access the Pinecone MCP server for creating, describing, upserting and querying indexes with Claude. Perfect for developers building semantic search, RAG applications, recommendation systems, and other vector-based applications with Pinecone.
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage
Manage vector embeddings and similarity search
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
Advanced retrieval for AgentDB — MMR diversity rerank, explainable recall, metadata filters, hybrid (BM25 + dense) search.
OpenRAG agent skills: guided installation and SDK integration helpers.

Agent Skills to help developers build and use AI agents with Weaviate more effectively. Each skill is a folder containing instructions, scripts, and resources that agents like Claude Code, Cursor, GitHub Copilot, and others can discover to work more accurately and efficiently
Works with any agent that supports the Agent Skills format.
npx skills add weaviate/agent-skills
/plugin marketplace add weaviate/agent-skills
/plugin install weaviate@weaviate-plugins
git clone https://github.com/weaviate/agent-skills.git
cd agent-skills
claude --plugin-dir .
New to Weaviate? Run the interactive onboarding to set up your environment variables, import your own or sample data, and explore the full functionality of the available skills/commands:
/weaviate:quickstart
It is recommended to create a free cluster in the weaviate console.
export WEAVIATE_URL="https://your-cluster.weaviate.cloud"
export WEAVIATE_API_KEY="your-api-key"
For the complete env var list and header mapping, see:
Utility functions for the agent to directly interact with a Weaviate database.
Create Collections
Explore Collections (Aggregation, Metadata, Schema)
Query Collections (Keyword-, Vector-, Hybrid Search) (Support filters)
Import Data (supports multi-vector and PDF ingestion)
Query Agent
Blueprints for complete end-to-end AI applications with state-of-the art guidelines for agentic infrastructure.
Multimodal PDF Ingestion
Data Explorer
Retrieval Augmented Generation (Basic, Advanced, Agentic)
Agents
Query Agent Chatbot
Frontend Interface (optional)
The scripts and cookbooks in this agent skills repository rely on some widely adopted third-party packages. However, we are not responsible for the security of any third-party components, as new vulnerabilities may arise at any time. The user should run their own security scans before executing any code and review packages flagged for known issues.
# Interactive onboarding
/weaviate:quickstart
# Ask a question and get an AI-generated answer with source citations
/weaviate:ask query "What are the benefits of vector databases?" collections "Documentation"
# Search collections and get raw results
/weaviate:query query "machine learning tutorials" collections "Articles,BlogPosts" limit 5
# Search with different search types
/weaviate:search query "product SKU-123" collection "Products" type "keyword"
/weaviate:search query "similar items" collection "Products" type "semantic"
/weaviate:search query "best laptops" collection "Products" type "hybrid" alpha "0.7"
# List collections or get a collection's schema
/weaviate:collections
/weaviate:collections name "Articles"
# Explore data in a collection
/weaviate:explore "Products" limit 10
# Fetch objects by ID or with filters
/weaviate:fetch collection "Articles" id "UUID"
/weaviate:fetch collection "Articles" filters '{"property": "category", "operator": "equal", "value": "Science"}'
The skill is automatically discovered by compatible agents. Simply describe what you want: