ktx-ai-data-agents

Build an agent context layer from metrics and wiki definitions for data warehouse queries.

7|2|Updated May 16, 2026

Search Tags:#mcp #data warehouse #semantic layer #wiki knowledge #ktx #sql query generation #metrics resolution

Ready to run

npx skills add https://github.com/Aradotso/mcp-skills --skill ktx-ai-data-agents

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ktx-ai-data-agents
Source: https://github.com/Aradotso/mcp-skills/tree/main/skills/ktx-ai-data-agents
Command: npx skills add https://github.com/Aradotso/mcp-skills --skill ktx-ai-data-agents

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ktx fixes the problem of AI agents generating incorrect or inconsistent SQL by giving them a verified context layer built from your metrics, wiki knowledge, and semantic layer so they query your warehouse accurately.

Core Features & Use Cases

Builds agent-ready data context: Learns from wiki content, deduplicates, and flags contradictions to anchor answers in approved business definitions.
Creates a semantic layer for safe querying: Generates canonical metrics, resolves joins, and handles tricky join paths (chasm/fan traps) so agents use consistent, correct logic.
Exposes MCP tools and CLI for end-to-end workflows: Supports semantic search and context-aware query execution for scenarios like revenue reporting, KPI explanation, and metric resolution.

Quick Start

Run ktx setup to configure your warehouse connections and build the initial context used by your AI agents.

Frequently Asked Questions about ktx-ai-data-agents

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

FAQPage Schema

How do I stop AI agents from generating incorrect SQL queries over my data warehouse?▼

You can prevent AI agents from generating incorrect SQL by providing them a verified context layer built from your semantic layer and wiki knowledge. This ensures queries use canonical metrics and correctly resolved joins for accurate reporting.

What is a semantic layer for AI data agents and how does it handle complex joins?▼

A semantic layer for AI data agents generates canonical metrics and resolves complex join paths including chasm and fan traps. It provides a safe querying interface that ensures agents apply consistent and correct business logic when executing SQL.

How do I configure an MCP server to expose data warehouse context for analytics agents?▼

You configure an MCP server to expose data warehouse context by running a setup process to define warehouse connections and build the initial context. This serves semantic search and context-aware SQL execution tools for agent integration.

Does my data stack require wiki knowledge ingestion for accurate agent metric resolution?▼

Ingesting wiki knowledge is required for accurate agent metric resolution because it anchors agent responses in approved business definitions. The system learns wiki content, deduplicates information, and flags contradictions to maintain data context accuracy.

What is the best way to resolve business metrics for AI agents before executing SQL?▼

The best way to resolve business metrics for AI agents is applying a context layer that searches approved metrics and wiki definitions. This resolves joins and handles tricky join paths before agents execute context-aware SQL for reporting tasks.

When should I not use a context layer for automated SQL query generation?▼

You should not use a context layer for automated SQL query generation when your data warehouse lacks established canonical metrics or approved wiki definitions. Without these semantic sources, the system cannot properly resolve joins or flag business logic contradictions.