MCP enables AI assistants to operate the vector database, but it doesn't understand Zvec's best practices — which index to use for which scenario? How to design a Schema properly? How should documents be chunked and stored? This kind of experiential knowledge cannot be obtained through tool calls alone.

Zvec Agent Skills injects Zvec domain knowledge into AI assistants, enabling them not only to call tools but also to think like a developer who is deeply familiar with Zvec. Once installed, you simply describe your business scenario and the AI will provide complete Schema designs, indexing strategies, and runnable code.

GitHub: https://github.com/zvec-ai/zvec-agent-skills

Prerequisites: Install the Zvec SDK for your language (Python or Node.js) before using the skill.

Installation

1. Install the Skill

npx skills add github:zvec-ai/zvec-agent-skills --skill "zvec"

2. Install Zvec Dependencies

Choose the installation method based on your development language:

Python:

pip install zvec

Node.js:

npm install @zvec/zvec

Using with Qoder/Claude

Starting a Conversation

Qoder/Claude automatically detects installed skills. You can directly ask Zvec-related questions during a conversation:

I want to build a RAG document retrieval system using Zvec

How do I perform hybrid search (vector + filter conditions) with Zvec?

Help me create a Collection for storing product information with name, price, and vector fields

Best Practices

1. Specify Your Development Language

When chatting with Qoder/Claude, start by stating your development language to get accurate code examples:

I'm using Python and want to implement semantic search with Zvec...

I need to use Zvec's multi-vector search in my Node.js project...

2. Describe Your Use Case

Provide specific business scenarios so Qoder/Claude can give more targeted advice:

I need to build an e-commerce product search system with the following requirements:
- Support semantic search by product description
- Filter by price range and stock status
- Approximately 500K product records

3. Ask for Decision Guidance

For technology selection questions, you can directly ask Qoder/Claude for recommendations:

I have 5 million records — what index type should I use?

In a RAG system, how should documents be chunked and stored in Zvec?

Typical Use Case Examples

Scenario 1: RAG Document Retrieval System

User input:

I want to build a RAG document retrieval system using Python and Zvec for a technical support knowledge base.
The documents are in Markdown format and need to support semantic search.

Qoder/Claude will provide:

Collection Schema design recommendations
Document chunking strategy
Vector generation and storage code
Retrieval query examples

Scenario 2: E-Commerce Product Search

User input:

I need to implement e-commerce product search in a Node.js project:
- Support semantic search on product names and descriptions
- Filter by price, category, and brand
- Support multimodal (image + text) search

Qoder/Claude will provide:

Multi-field Schema definition
Hybrid search (vector + scalar filtering) code
Multi-vector query and weighted ranking examples

I'm a [Python/Node.js] developer and want to use Zvec for [use case].
Please give me a complete quick-start code example.

Template 2: Specific Problem

I'm having an issue with Zvec:
- Language: [Python/Node.js]
- Problem: [specific description]
- Current code: [relevant code snippet]
- Error message: [if any]

Template 3: Architecture Consultation

I need to design a [system description] using Zvec as the vector database.
- Data scale: [volume]
- Query types: [search/filter/hybrid]
- Performance requirements: [latency/throughput]
Please help me design the Schema and indexing strategy.

Skills