The new open-source repository delivers structured skills, slash commands, and production-ready cookbooks to reduce AI coding errors and speed up Weaviate-based application development.
Amsterdam, Netherlands, Feb. 21, 2026 (GLOBE NEWSWIRE) — February 20, 2026 – Weaviate, the leading open-source AI database, today announced the launch of Weaviate Agent Skills, an innovative open-source repository that equips popular coding agents like Claude Code, Cursor, GitHub Copilot, VS Code, and Gemini CLI with precise tools for generating production-ready code targeting Weaviate workflows.
This release builds directly on Weaviate’s Query Agent, first previewed in March 2025 and reaching general availability in September 2025. The Query Agent supports natural language queries across multiple collections, featuring multi-collection routing, intelligent query expansion, decomposition for complex questions, user-defined filters, and reranking for optimal results. Developers can test Agent Skills immediately using Weaviate Cloud’s free Sandbox clusters—small instances designed for experimentation that last 14 days and can be extended or upgraded to production Shared Cloud setups.
Comprehensive Repository Tools
The repository at github.com/weaviate/agent-skills is structured into two core sections, providing full lifecycle support from basic operations to complete applications.
Weaviate Skills in the /skills/weaviate directory offer granular scripts for key tasks. These cover cluster management such as schema inspection, collection creation, and metadata retrieval; data lifecycle operations including imports from CSV, JSON, or JSONL files plus example data generation; agentic search powered by Query Agent; and advanced retrieval options like hybrid search (blending semantic and keyword with alpha parameters), pure semantic, or keyword modes.
Cookbooks in the /skills/weaviate-cookbooks folder provide end-to-end blueprints for production apps. Highlights include Query Agent chatbots built with FastAPI backends and Next.js frontends; multimodal PDF RAG pipelines using ModernVBERT for multivector embeddings alongside Ollama or Qwen3-VL for generation; basic, advanced, and agentic RAG implementations with decomposition and reranking; and DSPy-optimized agents incorporating custom tools and persistent memory.
Six Streamlined Slash Commands
Agent Skills introduces six intuitive commands that AI coding agents can auto-discover and execute, streamlining Weaviate interactions:
- /weaviate:ask: Delivers AI-generated answers with citations via Query Agent.
- /weaviate:collections: Lists all schemas or inspects specific collections.
- /weaviate:explore: Shows data metrics, counts, and sample objects.
- /weaviate:fetch: Retrieves objects by ID or filters by properties.
- /weaviate:query: Performs natural language searches across collections.
- /weaviate:search: Executes hybrid, semantic, or keyword searches with parameters like alpha blending.
For instance, developers can run “https://www.globenewswire.com/weaviate:search query ‘best laptops’ collection ‘Products’ type ‘hybrid’ alpha ‘0.7’” for balanced retrieval or “https://www.globenewswire.com/weaviate:ask What are vector database benefits?” against a Documentation collection.
CEO Bob van Luijt’s Vision
Bob van Luijt, Co-Founder and CEO of Weaviate—which he launched as an open-source vector search engine in March 2019—shared launch insights. “Weaviate Agent Skills bridges the gap between high-velocity AI coding and reliable infrastructure, letting developers build sophisticated AI systems without debugging agent hallucinations,” van Luijt stated.
As a prominent Netherlands-based technology entrepreneur, Van Luijt champions open-source AI tools. He positions Weaviate as a “batteries-included” stack that combines vector search, structured filtering, and agentic capabilities for modern AI applications.
Instant Setup for Developers
Integration is designed for speed. Install with a single command like npx skills add weaviate/agent-skills or via plugin managers in tools like Claude Code. Configure environment variables using your Weaviate Cloud endpoint and API key from a free Sandbox cluster.
Run /weaviate:quickstart for guided setup. This launch aligns with Weaviate’s 2025 momentum, including Query Agent GA, enhanced TypeScript/Python SDKs, multi-turn conversations, streaming responses, and new C#/Java clients for broader ecosystem support.
Weaviate invites the community to star the repo, submit pull requests for new cookbooks, and participate in discussions on GitHub, the Weaviate Forum, Slack workspace, and X.
Strategic Impact on AI Development
Agent Skills addresses a critical pain point: AI agents often generate inaccurate or incomplete code for vector databases due to hallucinations or outdated knowledge. By providing verified, modular tools, Weaviate enables faster iteration from prototype to production.
Early adopters report 3x reductions in debugging time for RAG pipelines and agentic apps. The repository’s modular design also facilitates contributions, with plans for expanded skills covering generative modules, tenancy isolation, and hybrid cloud deployments.
About Weaviate
Weaviate is an open-source, AI database that handles storage, retrieval, and orchestration for generative AI at scale. Backed by enterprise-grade Weaviate Cloud services, it powers agentic workflows—from simple semantic search to complex multi-agent systems—delivering sub-second latency on billions of objects.
Media Contact:
Philip Vollet
PR@weaviate.io
+49-160-96488554
CONTACT: Media Contact: Philip Vollet PR@weaviate.io +49-160-96488554
