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generate-validation-notebook

Generate Monte Carlo SQL validation notebooks for changed dbt models.

90|6|Updated Feb 25, 2026
Search Tags:#automation#validation#dbt#etl#validate#data-quality#notebook
One-click install
npx skills add https://github.com/monte-carlo-data/mc-agent-toolkit --skill generate-validation-notebook
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: generate-validation-notebook
Source: https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/generate-validation-notebook
Command: npx skills add https://github.com/monte-carlo-data/mc-agent-toolkit --skill generate-validation-notebook

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) components.

What problem does it solve?

dbt model changes require validated data quality across environments; this skill automates the generation of SQL validation notebooks to compare baseline and development data.

Core Features & Use Cases

  • Identify changed dbt models from a PR or local repository and generate a Monte Carlo SQL Notebook with validation queries.
  • Resolve per-model schemas using dedicated schema-resolution scripts and produce parameterized notebooks for prod/dev contexts.
  • Publish an import URL to open the notebook in Monte Carlo Bridge for interactive validation.

Quick Start

Invoke the skill with a PR URL or a local dbt repository path to generate a validation notebook.

Frequently Asked Questions about generate-validation-notebook

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

FAQPage Schema
How do I generate SQL validation queries for changed dbt models in a pull request?▼

To generate SQL validation queries for changed dbt models, this skill identifies modified models from a PR or local repository, resolves schemas using dbt_project.yml, and produces a Monte Carlo SQL Notebook with parameterized prod/dev references.

What is automated dbt data quality validation across prod and dev environments?▼

Automated dbt data quality validation compares baseline and development data by resolving model schemas and generating a Monte Carlo SQL Notebook containing parameterized validation queries for interactive prod and dev context testing.

How do I use Monte Carlo Bridge to validate dbt model changes?▼

You validate dbt model changes by invoking this skill with a PR URL or local repository path to generate an import URL, which opens the parameterized SQL validation notebook directly in Monte Carlo Bridge.

Do I need PyYAML to resolve dbt schemas and generate validation notebooks?▼

Yes, you need PyYAML installed to resolve dbt schemas from dbt_project.yml and encode the generated Monte Carlo SQL Notebook containing validation queries with parameterized prod/dev references.

Can I validate local dbt repository changes instead of using a pull request URL?▼

Yes, you can validate local dbt repository changes by providing the local repository path instead of a PR URL, allowing the skill to collect model contents and resolve schemas for notebook generation.

What are the limitations of automating dbt data validation with generated SQL notebooks?▼

Automating dbt data validation is limited to models identifiable as changed within a PR or local repository and requires dbt_project.yml for schema resolution to parameterize prod/dev references in the Monte Carlo SQL Notebook.

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