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ktx-ai-data-agents-context

Ingest warehouse metadata, dbt docs, LookML, and wiki content into a semantic context layer.

2|1|Updated May 16, 2026
Search Tags:#mcp#dbt#ai-agents#semantic-layer#context-layer#data-warehouses
One-click install
npx skills add https://github.com/Aradotso/ai-agent-skills --skill ktx-ai-data-agents-context
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ktx-ai-data-agents-context
Source: https://github.com/Aradotso/ai-agent-skills/tree/main/skills/ktx-ai-data-agents-context
Command: npx skills add https://github.com/Aradotso/ai-agent-skills --skill ktx-ai-data-agents-context

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data teams and AI agents need accurate, up-to-date warehouse context to craft correct queries and insights; this Skill provides a reusable context layer that ingests wiki content, dbt docs, LookML, and metadata to align agent queries with the data stack.

Core Features & Use Cases

  • Builds and maintains a semantic layer from warehouse metadata, dbt, LookML, and wiki content
  • Exposes context to AI agents via MCP and CLI tools for accurate data querying
  • Use Case: When configuring an enterprise data agent, ingest source docs and let agents generate correct SQL against warehouse structures

Quick Start

Run ktx setup to initialize and connect your data warehouse context layer

Frequently Asked Questions about ktx-ai-data-agents-context

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I give AI data agents context from dbt docs and LookML for querying data warehouses?▼

To give AI data agents context from dbt docs and LookML, you need a semantic context layer that ingests warehouse metadata and wiki content. This structures documentation so agents can generate accurate SQL against your data stack.

What is a semantic context layer for autonomous data agents?▼

A semantic context layer for autonomous data agents is a searchable knowledge base built from warehouse metadata, dbt docs, and LookML. It aligns agent queries with actual data structures to ensure accurate data analysis.

How do I set up an AI data agent to query data warehouses using MCP and CLI tools?▼

To set up an AI data agent to query data warehouses using MCP and CLI tools, run ktx setup to initialize the project. This connects your warehouse metadata and exposes the context layer to agents via MCP.

Can I use wiki content and warehouse metadata to improve AI data agent query accuracy?▼

Yes, you can use wiki content and warehouse metadata to improve AI data agent query accuracy. Ingesting these sources into a context layer provides the reliable documentation required for agents to generate correct SQL.

What do I need to configure before building a data warehouse context layer for AI agents?▼

Before building a data warehouse context layer for AI agents, you need validated database connections and integration with MCP or CLI tools. You also need existing warehouse documentation like dbt docs or LookML available for ingestion.

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