Monte Carlo Validation Notebook V2

Setup

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/monte-carlo-validation-notebook from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Parameter Cell Spec, Phase 1: Get Changed Files, Phase 2: Parse Changed Models, Phase 3: Generate Validation Queries, Phase 4: Build Notebook YAML, Phase 5: Generate Import URL.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Target (required): first argument — a GitHub PR URL or local dbt repo path
• MC Base URL (optional): --mc-base-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.
• 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.)

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. If the user asks how to install or set up MC Bridge, fetch the README from the mc-bridge repo and show the relevant quick start / setup instructions: bash gh api repos/monte-carlo-data/mc-bridge/readme --jq '.content' | base64 --decode Focus on: how to install, configure connections, and run MC Bridge.
2. Don't dump the entire README — extract just the setup-relevant sections.
3. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
4. Read the overview and provenance files before loading any copied upstream support files.
5. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
6. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
7. Validate the result against the upstream expectations and the evidence you can point to in the copied files.

Imported Workflow Notes

Imported: MC Bridge Setup Help

If the user asks how to install or set up MC Bridge, fetch the README from the mc-bridge repo and show the relevant quick start / setup instructions:

gh api repos/monte-carlo-data/mc-bridge/readme --jq '.content' | base64 --decode

Focus on: how to install, configure connections, and run MC Bridge. Don't dump the entire README — extract just the setup-relevant sections.

Imported: Summary

• Source: PR # - OR Local:
• Author:
• Changed Models: models (of changed)
• Generated Queries: queries

⚠️ If models were capped: "Only the first 10 of changed models were included. Re-run with --models to select specific models."

Imported: 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.

Examples

Example 1: Ask for the upstream workflow directly

Use @monte-carlo-validation-notebook-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @monte-carlo-validation-notebook-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @monte-carlo-validation-notebook-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @monte-carlo-validation-notebook-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Do NOT execute queries -- only generate the notebook
• Keep SQL readable -- proper formatting and meaningful aliases
• Include LIMIT 100 on queries that could return many rows
• Use double curly braces -- {{proddb}} NOT ${proddb}
• Use correct table format -- {{proddb}}.. and {{devdb}}..
• Always use the schema resolution script -- do NOT manually parse dbt_project.yml
• Schema is NOT a parameter -- only proddb and devdb are parameters

Imported Operating Notes

Imported: Important Guidelines

1. Do NOT execute queries -- only generate the notebook
2. Keep SQL readable -- proper formatting and meaningful aliases
3. Include LIMIT 100 on queries that could return many rows
4. Use double curly braces -- {{prod_db}} NOT ${prod_db}
5. Use correct table format -- {{prod_db}}.<SCHEMA>.<TABLE> and {{dev_db}}.<SCHEMA>.<TABLE>
6. Always use the schema resolution script -- do NOT manually parse dbt_project.yml
7. Schema is NOT a parameter -- only prod_db and dev_db are parameters
8. Skip ephemeral models -- they have no physical table
9. Truncate notebook name -- keep under 50 chars
10. Generate unique cell IDs -- use pattern like cell-p3-model-1
11. YAML multiline content -- use | block scalar for SQL with comments
12. ASCII-only YAML -- the script sanitizes and validates before encoding

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/monte-carlo-validation-notebook, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @changelog-automation-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @commit-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @create-pr-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @distributed-tracing-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• generate_notebook_url.py
• resolve_dbt_schema.py
• generate_notebook_url.py
• resolve_dbt_schema.py

Imported Reference Notes

Imported: Query Pattern Reference

Imported: 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>
   

Imported: 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/monte-carlo-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 and new values.
• New columns -- Columns in "after" SELECT that don't appear in "before" (pure additions).

2c. Model Classification

Classify each model as new or modified based on the diff:

• If the diff for this file contains new file mode → classify as new
• Otherwise → classify as modified

This classification determines which query patterns are generated in Phase 3.

Note: For new models, Phase 2b diff analysis is skipped (there is no "before" to compare against). Phase 2a metadata extraction still applies.

Imported: Phase 3: Generate Validation Queries

For each changed model, generate the applicable queries based on its classification (new vs modified).

CRITICAL: Parameter Placeholder Syntax

Use double curly braces {{...}} for parameter placeholders. Do NOT use ${...} or any other syntax.

Correct: {{prod_db}}.PROD.AGENT_RUNS Wrong: ${prod_db}.PROD.AGENT_RUNS

Table Reference Format:

• Use {{prod_db}}.<SCHEMA>.<TABLE_NAME> for prod queries
• Use {{dev_db}}.<SCHEMA>.<TABLE_NAME> for dev queries
• <SCHEMA> is hardcoded per-model using the output from the schema resolution script

Query Patterns for NEW Models

For new models, all queries target {{dev_db}} only. No comparison queries are generated since no prod table exists.

Pattern 7-new: Total Row Count

Trigger: Always.

SELECT COUNT(*) AS total_rows
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>

Pattern 9: Sample Data Preview

Trigger: Always.

SELECT *
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>
LIMIT 20

Pattern 2-new: Core Segmentation Counts

Trigger: Always.

SELECT
    <segmentation_field>,
    COUNT(*) AS row_count
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>
GROUP BY <segmentation_field>
ORDER BY row_count DESC
LIMIT 100

Pattern 5: Uniqueness Check

Trigger: Always for new models (verify unique_key constraint from the start).

SELECT
    COUNT(*) AS total_rows,
    COUNT(DISTINCT <key_fields>) AS distinct_keys,
    COUNT(*) - COUNT(DISTINCT <key_fields>) AS duplicate_count
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>

SELECT <key_fields>, COUNT(*) AS n
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>
GROUP BY <key_fields>
HAVING COUNT(*) > 1
ORDER BY n DESC
LIMIT 100

Pattern 6-new: NULL Rate Check (all columns)

Trigger: Always. Checks all output columns since everything is new.

SELECT
    COUNT(*) AS total_rows,
    SUM(CASE WHEN <col1> IS NULL THEN 1 ELSE 0 END) AS <col1>_null_count,
    ROUND(100.0 * SUM(CASE WHEN <col1> IS NULL THEN 1 ELSE 0 END) / NULLIF(COUNT(*), 0), 2) AS <col1>_null_pct,
    SUM(CASE WHEN <col2> IS NULL THEN 1 ELSE 0 END) AS <col2>_null_count,
    ROUND(100.0 * SUM(CASE WHEN <col2> IS NULL THEN 1 ELSE 0 END) / NULLIF(COUNT(*), 0), 2) AS <col2>_null_pct
    -- repeat for each output column
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>

Pattern 8: Time-Axis Continuity

Trigger: Model is materialized='incremental' OR a time axis field was identified.

SELECT
    CAST(<time_axis> AS DATE) AS day,
    COUNT(*) AS row_count
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>
WHERE <time_axis> >= CURRENT_TIMESTAMP - INTERVAL '14' DAY
GROUP BY day
ORDER BY day DESC
LIMIT 30

Query Patterns for MODIFIED Models

For modified models, single-table queries use {{prod_db}} and comparison queries use both.

Pattern 7: Total Row Count

Trigger: Always.

SELECT COUNT(*) AS total_rows
FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>

Pattern 9: Sample Data Preview

Trigger: Always.

SELECT *
FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>
LIMIT 20

Pattern 2: Core Segmentation Counts

Trigger: Always.

SELECT
    <segmentation_field>,
    COUNT(*) AS row_count
FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>
GROUP BY <segmentation_field>
ORDER BY row_count DESC
LIMIT 100

Pattern 1: Changed Field Distribution

Trigger: Changed fields found in Phase 2b. Exclude added columns (from "New columns" in Phase 2b) — only include fields that exist in prod.

SELECT
    <changed_field>,
    COUNT(*) AS row_count,
    ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER(), 2) AS pct
FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>
GROUP BY <changed_field>
ORDER BY row_count DESC
LIMIT 100

Pattern 5: Uniqueness Check

Trigger: JOIN condition changed, unique_key changed, or model is incremental.

SELECT
    COUNT(*) AS total_rows,
    COUNT(DISTINCT <key_fields>) AS distinct_keys,
    COUNT(*) - COUNT(DISTINCT <key_fields>) AS duplicate_count
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>

SELECT <key_fields>, COUNT(*) AS n
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>
GROUP BY <key_fields>
HAVING COUNT(*) > 1
ORDER BY n DESC
LIMIT 100

Pattern 6: NULL Rate Check

Trigger: New column added, or column wrapped in COALESCE/NULLIF.

Important: Added columns (from "New columns" in Phase 2b) do NOT exist in prod yet. For added columns, query {{dev_db}} only. For modified columns (COALESCE/NULLIF changes), compare both databases.

For added columns (dev only):

SELECT
    COUNT(*) AS total_rows,
    SUM(CASE WHEN <column> IS NULL THEN 1 ELSE 0 END) AS null_count,
    ROUND(100.0 * SUM(CASE WHEN <column> IS NULL THEN 1 ELSE 0 END) / NULLIF(COUNT(*), 0), 2) AS null_pct
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>

For modified columns (prod vs dev):

SELECT
    'prod' AS source,
    COUNT(*) AS total_rows,
    SUM(CASE WHEN <column> IS NULL THEN 1 ELSE 0 END) AS null_count,
    ROUND(100.0 * SUM(CASE WHEN <column> IS NULL THEN 1 ELSE 0 END) / NULLIF(COUNT(*), 0), 2) AS null_pct
FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>
UNION ALL
SELECT
    'dev' AS source,
    COUNT(*) AS total_rows,
    SUM(CASE WHEN <column> IS NULL THEN 1 ELSE 0 END) AS null_count,
    ROUND(100.0 * SUM(CASE WHEN <column> IS NULL THEN 1 ELSE 0 END) / NULLIF(COUNT(*), 0), 2) AS null_pct
FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>

Pattern 8: Time-Axis Continuity

Trigger: Model is materialized='incremental' OR a time axis field was identified.

SELECT
    CAST(<time_axis> AS DATE) AS day,
    COUNT(*) AS row_count
FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>
WHERE <time_axis> >= CURRENT_TIMESTAMP - INTERVAL '14' DAY
GROUP BY day
ORDER BY day DESC
LIMIT 30

Pattern 3: Before/After Comparison

Trigger: Always (for changed fields + top segmentation field). Modified models only.

Important: Exclude added columns (from "New columns" in Phase 2b) from <group_fields>. Only use fields that exist in BOTH prod and dev. Added columns don't exist in prod and will cause query errors.

WITH prod AS (
    SELECT <group_fields>, COUNT(*) AS cnt
    FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>
    GROUP BY <group_fields>
),
dev AS (
    SELECT <group_fields>, COUNT(*) AS cnt
    FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>
    GROUP BY <group_fields>
)
SELECT
    COALESCE(b.<field>, d.<field>) AS <field>,
    COALESCE(b.cnt, 0) AS cnt_prod,
    COALESCE(d.cnt, 0) AS cnt_dev,
    COALESCE(d.cnt, 0) - COALESCE(b.cnt, 0) AS diff
FROM prod b
FULL OUTER JOIN dev d ON b.<field> = d.<field>
ORDER BY ABS(diff) DESC
LIMIT 100

Pattern 7b: Row Count Comparison

Trigger: Always. Modified models only.

SELECT 'prod' AS source, COUNT(*) AS row_count FROM {{prod_db}}.<SCHEMA>.<TABLE_NAME>
UNION ALL
SELECT 'dev' AS source, COUNT(*) AS row_count FROM {{dev_db}}.<SCHEMA>.<TABLE_NAME>

Imported: Phase 4: Build Notebook YAML

4a. Metadata

version: 1
metadata:
  id: validation-pr-<PR_NUMBER>-<random_suffix>
  name: "Validation: PR #<PR_NUMBER> - <PR_TITLE_TRUNCATED>"
  created_at: "<current_iso_timestamp>"
  updated_at: "<current_iso_timestamp>"

4b. Parameter Cells

Only include prod_db if there are modified models. If all models are new, only include dev_db.

# Include ONLY if there are modified models:
- id: param-prod-db
  type: parameter
  content:
    name: prod_db
    config:
      type: text
      default_value: "ANALYTICS"
      placeholder: "Prod database (e.g., ANALYTICS)"
  display_type: table

# Always include:
- id: param-dev-db
  type: parameter
  content:
    name: dev_db
    config:
      type: text
      default_value: "PERSONAL_<USER>"
      placeholder: "Dev database (e.g., PERSONAL_JSMITH)"
  display_type: table

4c. Markdown Summary Cell

- id: cell-summary
  type: markdown
  content: |
    # Validation Queries for <PR or Local Branch>
    ## Summary
    - **Title:** <title>
    - **Author:** <author>
    - **Source:** <PR URL or "Local branch: <branch>">
    - **Status:** <merge_timestamp or "Not yet merged" or "N/A (local)">
    ## Changes
    <brief description based on diff analysis>
    ## Changed Models
    - `<SCHEMA>.<TABLE_NAME>` (from `<file_path>`)
    ## How to Use
    1. Select your Snowflake connector above
    2. Set **dev_db** to your dev database (e.g., `PERSONAL_JSMITH`)
    3. If modified models are present, set **prod_db** to your prod database (e.g., `ANALYTICS`)
    4. Run single-table queries first, then comparison queries
  display_type: table

4d. SQL Cell Format

- id: cell-<pattern>-<model>-<index>
  type: sql
  content: |
    /*
    ========================================
    <Pattern Name (human-readable, e.g. "Total Row Count" — do NOT include pattern numbers like "Pattern 7:")>
    ========================================
    Model: <SCHEMA>.<TABLE_NAME>
    Triggered by: <why this pattern was generated>
    What to look for: <interpretation guidance>
    ----------------------------------------
    */
    <actual_sql_query>
  display_type: table

4e. Cell Organization

Cells are ordered consistently for both model types, following this sequence:

New models:

1. Summary markdown cell (note that model is new)
2. Parameter cells (dev_db only — no prod_db if all models are new)
3. Total row count (Pattern 7-new)
4. Sample data preview (Pattern 9)
5. Core segmentation counts (Pattern 2-new)
6. Uniqueness check (Pattern 5), NULL rate check (Pattern 6-new), Time-axis continuity (Pattern 8)

Modified models:

1. Summary markdown cell
2. Parameter cells (prod_db, dev_db)
3. Total row count (Pattern 7)
4. Sample data preview (Pattern 9)
5. Core segmentation counts (Pattern 2)
6. Changed field distribution (Pattern 1)
7. Uniqueness check (Pattern 5), NULL rate check (Pattern 6), Time-axis continuity (Pattern 8)
8. Before/after comparisons (Pattern 3), Row count comparison (Pattern 7b)

Imported: Phase 5: Generate Import URL

1. Write notebook YAML to /tmp/validation_notebook_working/<id>/notebook.yaml
2. Run the URL generation script:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/monte-carlo-validation-notebook/scripts/generate_notebook_url.py /tmp/validation_notebook_working/<id>/notebook.yaml --mc-base-url <MC_BASE_URL>

3. The script validates both YAML syntax and notebook schema (required fields on metadata and cells). If validation fails, read the error messages carefully, fix the YAML to match the spec in Phase 4, and re-run.

Imported: Phase 6: Output

Present:

# Validation Notebook Generated

#### Imported: Notebook Opened

The notebook has been opened directly in your browser.
Select your Snowflake connector in the notebook interface to begin running queries.
*Make sure MC Bridge is running. Let me know if you want tips on how to install this locally*

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.