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metricflow-semantic-authoring

Generate validated MetricFlow semantic model YAML from database table schemas.

1.5k|222|Updated Jul 4, 2025
Search Tags:#data-engineering#knowledge-base#yaml-validation#semantic-model#metricflow#sql-profiling
One-click install
npx skills add https://github.com/Datus-ai/Datus-agent --skill metricflow-semantic-authoring
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: metricflow-semantic-authoring
Source: https://github.com/Datus-ai/Datus-agent/tree/main/datus/resources/skills/metricflow-semantic-authoring
Command: npx skills add https://github.com/Datus-ai/Datus-agent --skill metricflow-semantic-authoring

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually writing MetricFlow semantic model YAML is time-consuming, error-prone, and often fails validation, leading to broken metrics and inconsistent data definitions across data teams.

Core Features & Use Cases

  • Automated Semantic Modeling: Generates production-ready MetricFlow YAML directly from database table schemas, eliminating manual YAML writing effort.
  • Iterative Validation & Fixing: Runs built-in validation checks and automatically fixes YAML errors until the model passes all requirements.
  • Knowledge Base Publishing: Automatically publishes validated semantic models to the central Knowledge Base for reuse across all data tools and agents.
  • Use Case: A data engineer can turn a set of raw sales and user database tables into a fully validated, reusable semantic model in minutes instead of hours of manual work, ensuring all downstream metrics are consistent and accurate.

Quick Start

Use the metricflow-semantic-authoring skill to generate a validated semantic model for the public.sales fact table and publish it to the Knowledge Base.

Frequently Asked Questions about metricflow-semantic-authoring

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

FAQPage Schema
How do I generate MetricFlow semantic models from existing database tables?▼

Generating MetricFlow semantic models from database tables is automated by extracting schemas to produce production-ready YAML files. This eliminates manual YAML writing, reducing errors and ensuring validated models for downstream metrics.

Why does my manually written MetricFlow YAML fail validation?▼

MetricFlow YAML validation often fails due to manual errors or incorrect definitions. This Skill runs iterative validation checks and automatically fixes YAML errors until the semantic model passes all requirements.

What is the best way to maintain consistent metric definitions across data teams?▼

Maintaining consistent metric definitions requires publishing validated semantic models to a central Knowledge Base. This Skill automatically publishes MetricFlow YAML to the Knowledge Base, enabling reuse across downstream data tools.

Can I use historical SQL profiling to inform semantic model decisions?▼

Historical SQL profiling is optionally supported to inform semantic modeling decisions. By analyzing past SQL queries, the Skill helps guide the structure and definitions of the generated MetricFlow semantic model YAML.

Does MetricFlow semantic authoring require strict document boundary rules for metrics?▼

MetricFlow semantic authoring strictly adheres to document boundary rules for semantic model and metric definitions. This ensures the generated YAML files are properly structured and compliant with MetricFlow integration standards.

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