Amsterdam, Netherlands — February 21, 2026 — Leads & Copy — Weaviate has launched Weaviate Agent Skills, an open-source repository providing coding agents with tools for generating production-ready code targeting Weaviate workflows.
The release builds on Weaviate’s Query Agent, which was first previewed in March 2025 and reached 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. Developers can test Agent Skills using Weaviate Cloud’s free Sandbox clusters.
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 tasks covering cluster management, data lifecycle operations, agentic search, and advanced retrieval options. Cookbooks in the /skills/weaviate-cookbooks folder provide end-to-end blueprints for production apps including Query Agent chatbots, multimodal PDF RAG pipelines, basic, advanced, and agentic RAG implementations, and DSPy-optimized agents.
Agent Skills introduces six commands that AI coding agents can auto-discover and execute:
/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 example, developers can run “/weaviate:search query ‘best laptops’ collection ‘Products’ type ‘hybrid’ alpha ‘0.7’” or “/weaviate:ask What are vector database benefits?” against a Documentation collection.
Weaviate Co-Founder and CEO Bob van Luijt said that Weaviate Agent Skills bridges the gap between high-velocity AI coding and reliable infrastructure, letting developers build AI systems without debugging agent hallucinations.
Van Luijt positions Weaviate as a “batteries-included” stack that combines vector search, structured filtering, and agentic capabilities for modern AI applications.
Integration is designed for speed with a single command like npx skills add weaviate/agent-skills or via plugin managers. Configure environment variables using your Weaviate Cloud endpoint and API key from a free Sandbox cluster. Run /weaviate:quickstart for guided setup.
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.
Agent Skills addresses the issue of AI agents generating inaccurate or incomplete code for vector databases. 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.
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, delivering sub-second latency on billions of objects.
Source: Weaviate
