Generate Validation Notebook Skill

Automatically generate SQL validation notebooks for dbt model changes. Given a GitHub PR or local dbt repository, this skill identifies modified models and produces a Monte Carlo SQL Notebook with targeted validation queries comparing baseline and development data.

What it does

1. Identifies changed dbt models from a PR diff or local branch
2. Analyzes each model's schema, config, segmentation fields, and time axis
3. Generates SQL validation queries (row counts, distribution checks, NULL rates, before/after comparisons, uniqueness checks)
4. Packages everything into a Monte Carlo SQL Notebook with parameterized database references
5. Outputs an import URL that opens the notebook directly in Monte Carlo's notebook interface

Prerequisites

• Claude Code or any MCP-capable editor
• GitHub CLI (gh) — required for PR mode, must be authenticated
• Python 3 with pyyaml installed (pip install pyyaml)
• MC Bridge running and connected to your warehouse

Setup

Via the mc-agent-toolkit plugin (recommended)

Install the plugin for your editor — see the main README for instructions. The skill is bundled automatically.

Standalone

Copy the skill to your local skills directory:

cp -r skills/generate-validation-notebook ~/.claude/skills/generate-validation-notebook

Usage

PR mode

/mc-generate-validation-notebook https://github.com/your-org/dbt/pull/123

Fetches the PR diff from GitHub, identifies changed models, and generates validation queries.

Local mode

/mc-generate-validation-notebook .

Uses git diff against the base branch to find changed models in the current repository.

Options

• --mc-base-url — Monte Carlo base URL (defaults to https://getmontecarlo.com)
• --models — only generate for specific models (by filename, without .sql)

What gets generated

The notebook includes:

• Parameter cells — prod_db and dev_db for selecting databases
• Markdown summary — PR metadata, changed models, usage instructions
• SQL validation queries organized by pattern:
  • Row counts (single and comparison)
  • Segmentation distribution
  • Changed field distribution
  • NULL rate checks
  • Uniqueness checks
  • Time-axis continuity
  • Before/after comparisons
  • Sample data previews

Up to 10 changed models are processed per invocation.

Supported warehouses

Generated SQL uses ANSI-compatible syntax that works across Snowflake, BigQuery, Redshift, and Athena. Minor adjustments may be needed for specific warehouse quirks.