generate-validation-notebook

Tip: This skill works well with Sonnet. Run /model sonnet before invoking for faster generation.

Generate a SQL Notebook with validation queries for dbt changes.

Arguments: $ARGUMENTS

Parse the arguments:

• Target (required): first argument — a GitHub PR URL or local dbt repo path
• MC Base URL (optional): --mc-base-url <URL> — defaults to https://getmontecarlo.com
• Models (optional): --models <model1,model2,...> — comma-separated list of model filenames (without .sql extension) to generate queries for. Only these models will be included. By default, all changed models are included up to a maximum of 10.

Setup

Prerequisites:

• gh (GitHub CLI) — required for PR mode. Must be authenticated (gh auth status).
• python3 — required for helper scripts.
• pyyaml — install with pip3 install pyyaml (or pip install pyyaml, uv pip install pyyaml, etc.)

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

This skill includes two helper scripts in ${CLAUDE_PLUGIN_ROOT}/skills/generate-validation-notebook/scripts/:

• resolve_dbt_schema.py - Resolves dbt model output schemas from dbt_project.yml routing rules and model config overrides.
• generate_notebook_url.py - Encodes notebook YAML into a base64 import URL and opens it in the browser.

Mode Detection

Auto-detect mode from the target argument:

• If target looks like a URL (contains :// or github.com) -> PR mode
• If target is a path (., /path/to/repo, relative path) -> Local mode

Context

This command generates a SQL Notebook containing validation queries for dbt changes. The notebook can be opened in the MC Bridge SQL Notebook interface for interactive validation.

The output is an import URL that opens directly in the notebook interface:

<MC_BASE_URL>/notebooks/import#<base64-encoded-yaml>

Key Features:

• Database Parameters: Two text parameters (prod_db and dev_db) for selecting databases
• Schema Inference: Automatically infers schema per model from dbt_project.yml and model configs
• Single-table queries: Basic validation queries using {{prod_db}}.<SCHEMA>.<TABLE>
• Comparison queries: Before/after queries comparing {{prod_db}} vs {{dev_db}}
• Flexible usage: Users can set both parameters to the same database for single-database analysis

Notebook YAML Spec Reference

Key structure:

version: 1
metadata:
  id: string           # kebab-case + random suffix
  name: string         # display name
  created_at: string   # ISO 8601
  updated_at: string   # ISO 8601
default_context:       # optional database/schema context
  database: string
  schema: string
cells:
  - id: string
    type: sql | markdown | parameter
    content: string    # SQL, markdown, or parameter config (JSON)
    display_type: table | bar | timeseries

Parameter Cell Spec

Parameter cells allow defining variables referenced in SQL via {{param_name}} syntax:

- id: param-prod-db
  type: parameter
  content:
    name: prod_db              # variable name
    config:
      type: text                   # free-form text input
      default_value: "ANALYTICS"
      placeholder: "Prod database"
  display_type: table

Parameter types:

• text: Free-form text input (used for database names)
• schema_selector: Two dropdowns (database -> schema), value stored as DATABASE.SCHEMA
• dropdown: Select from predefined options

Task

Generate a SQL Notebook with validation queries based on the mode and target.

Phase 1: Get Changed Files

The approach differs based on mode:

If PR mode (GitHub PR):

1. Extract the PR number and repo from the target URL.

   • Example: https://github.com/monte-carlo-data/dbt/pull/3386 -> owner=monte-carlo-data, repo=dbt, PR=3386

2. Fetch PR metadata using gh:

gh pr view <PR#> --repo <owner>/<repo> --json number,title,author,mergedAt,headRefOid

3. Fetch the list of changed files:

gh pr view <PR#> --repo <owner>/<repo> --json files --jq '.files[].path'

4. Fetch the diff:

gh pr diff <PR#> --repo <owner>/<repo>

5. Filter the changed files list to only .sql files under models/ or snapshots/ directories (at any depth — e.g., models/, analytics/models/, dbt/models/). These are the dbt models to analyze. If no model SQL files were changed, report that and stop.

6. For each changed model file, fetch the full file content at the head SHA:

gh api repos/<owner>/<repo>/contents/<file_path>?ref=<head_sha> --jq '.content' | python3 -c "import sys,base64; sys.stdout.write(base64.b64decode(sys.stdin.read()).decode())"

7. Fetch dbt_project.yml for schema resolution. Detect the dbt project root by looking at the changed file paths — find the common parent directory that contains dbt_project.yml. Try these paths in order until one succeeds:

gh api repos/<owner>/<repo>/contents/<dbt_root>/dbt_project.yml?ref=<head_sha> --jq '.content' | python3 -c "import sys,base64; sys.stdout.write(base64.b64decode(sys.stdin.read()).decode())"

Common <dbt_root> locations: analytics, . (repo root), dbt, transform. Try each until found.

Save dbt_project.yml to /tmp/validation_notebook_working/<PR#>/dbt_project.yml.

If Local mode (Local Directory):

1. Change to the target directory.

2. Get current branch info:

git rev-parse --abbrev-ref HEAD

3. Detect base branch - try main, master, develop in order, or use upstream tracking branch.

4. Get the list of changed SQL files compared to base branch:

git diff --name-only <base_branch>...HEAD -- '*.sql'

5. Filter to only .sql files under models/ or snapshots/ directories (at any depth — e.g., models/, analytics/models/, dbt/models/). If no model SQL files were changed, report that and stop.

6. Get the diff for each changed file:

git diff <base_branch>...HEAD -- <file_path>

7. Read model files directly from the filesystem.

8. Find dbt_project.yml:

find . -name "dbt_project.yml" -type f | head -1

9. For notebook metadata in local mode, use:
   • ID: local-<branch-name>-<timestamp>
   • Title: Local: <branch-name>
   • Author: Output of git config user.name
   • Merged: "N/A (local)"

Model Selection (applies to both modes)

After filtering to .sql files under models/ or snapshots/:

1. If --models was specified: Filter the changed files list to only include models whose filename (without .sql extension, case-insensitive) matches one of the specified model names. If any specified model is not found in the changed files, warn the user but continue with the models that were found. If none match, report that and stop.

2. Model cap: If more than 10 models remain after filtering, select the first 10 (by file path order) and warn the user:

   ⚠️ <total_count> models changed — generating validation queries for the first 10 only.
   To generate for specific models, re-run with: --models <model1,model2,...>
   Skipped models: <list of skipped model filenames>
   

Phase 2: Parse Changed Models

For EACH changed dbt model .sql file, parse and extract:

2a. Model Metadata

Output table name -- Derive from file name:

• <any_path>/models/<subdir>/<model_name>.sql -> table is <MODEL_NAME> (uppercase, taken from the filename)

Output schema -- Use the schema resolution script:

1. Setup: Save dbt_project.yml and model files to /tmp/validation_notebook_working/<id>/ preserving paths:

   /tmp/validation_notebook_working/<id>/
   +-- dbt_project.yml
   +-- models/
       +-- <path>/<model>.sql
   

2. Run the script for each model:

   python3 ${CLAUDE_PLUGIN_ROOT}/skills/generate-validation-notebook/scripts/resolve_dbt_schema.py /tmp/validation_notebook_working/<id>/dbt_project.yml /tmp/validation_notebook_working/<id>/models/<path>/<model>.sql
   

3. Error handling: If the script fails, STOP immediately and report the error. Do NOT proceed with notebook generation if schema resolution fails.

4. Output: The script prints the resolved schema (e.g., PROD, PROD_STAGE, PROD_LINEAGE)

Note: Do NOT manually parse dbt_project.yml or model configs for schema -- always use the script. It handles model config overrides, dbt_project.yml routing rules, PROD_ prefix for custom schemas, and defaults to PROD.

Config block -- Look for {{ config(...) }} and extract:

• materialized -- 'table', 'view', 'incremental', 'ephemeral'
• unique_key -- the dedup key (may be a string or list)
• cluster_by -- clustering fields (may contain the time axis)

Core segmentation fields -- Scan the entire model SQL for fields likely to be business keys:

• Fields named *_id (e.g., account_id, resource_id, monitor_id) that appear in JOIN ON, GROUP BY, PARTITION BY, or unique_key
• Deduplicate and rank by frequency. Take the top 3.

Time axis field -- Detect the model's time dimension (in priority order):

1. is_incremental() block: field used in the WHERE comparison
2. cluster_by config: timestamp/date fields
3. Field name conventions: ingest_ts, created_time, date_part, timestamp, run_start_time, export_ts, event_created_time
4. ORDER BY DESC in QUALIFY/ROW_NUMBER

If no time axis is found, skip time-axis queries for this model.

2b. Diff Analysis

Parse the diff hunks for this file. Classify each changed line:

• Changed fields -- Lines added/modified in SELECT clauses or CTE definitions. Extract the output column name.
• Changed filters -- Lines added/modified in WHERE clauses.
• Changed joins -- Lines added/modified in JOIN ON conditions.
• Changed unique_key -- If unique_key in config was modified, note both old

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