monte-carlo-prevent

Monte Carlo Prevent Skill

This skill brings Monte Carlo's data observability context directly into your editor. When you're modifying a dbt model or SQL pipeline, use it to surface table health, lineage, active alerts, and to generate monitors-as-code without leaving Claude Code.

Reference files live next to this skill file. Use the Read tool (not MCP resources) to access them:

• Full workflow step-by-step instructions: references/workflows.md (relative to this file)
• MCP parameter details: references/parameters.md (relative to this file)
• Troubleshooting: references/TROUBLESHOOTING.md (relative to this file)

When to activate this skill

Do not wait to be asked. Run the appropriate workflow automatically whenever the user:

• References or opens a .sql file or dbt model (files in models/) → run Workflow 1

• Mentions a table name, dataset, or dbt model name in passing → run Workflow 1

• Describes a planned change to a model (new column, join update, filter change, refactor) → STOP — run Workflow 4 before writing any code

• Adds a new column, metric, or output expression to an existing model → run Workflow 4 first, then ALWAYS offer Workflow 2 regardless of risk tier — do not skip the monitor offer

• Asks about data quality, freshness, row counts, or anomalies → run Workflow 1

• Wants to triage or respond to a data quality alert → run Workflow 3

Present the results as context the engineer needs before proceeding — not as a response to a question.

When NOT to activate this skill

Do not invoke Monte Carlo tools for:

• Seed files (files in seeds/ directory)
• Analysis files (files in analyses/ directory)
• One-off or ad-hoc SQL scripts not part of a dbt project
• Configuration files (dbt_project.yml, profiles.yml, packages.yml)
• Test files unless the user is specifically asking about data quality

If uncertain whether a file is a dbt model, check for {{ ref() }} or {{ source() }} Jinja references — if absent, do not activate.