ranbot-ai/monte-carlo-monitor-creation

Monte Carlo Monitor Creation Skill

This skill teaches you to create Monte Carlo monitors correctly via MCP. Every creation tool runs in dry-run mode and returns monitors-as-code (MaC) YAML. No monitors are created directly -- the user applies the YAML via the Monte Carlo CLI or CI/CD.

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

• Metric monitor details: references/metric-monitor.md (relative to this file)
• Validation monitor details: references/validation-monitor.md (relative to this file)
• Custom SQL monitor details: references/custom-sql-monitor.md (relative to this file)
• Comparison monitor details: references/comparison-monitor.md (relative to this file)
• Table monitor details: references/table-monitor.md (relative to this file)

When to activate this skill

Activate when the user:

• Asks to create, add, or set up a monitor (e.g. "add a monitor for...", "create a freshness check on...", "set up validation for...")
• Mentions monitoring a specific table, field, or metric
• Wants to check data quality rules or enforce data contracts
• Asks about monitoring options for a table or dataset
• Requests monitors-as-code YAML generation
• Wants to add monitoring after new transformation logic (when the prevent skill is not active)

When NOT to activate this skill

Do not activate when the user is:

• Just querying data or exploring table contents
• Triaging or responding to active alerts (use the prevent skill's Workflow 3)
• Running impact assessments before code changes (use the prevent skill's Workflow 4)
• Asking about existing monitor configuration (use getMonitors directly)
• Editing or deleting existing monitors

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Available MCP tools

All tools are available via the monte-carlo MCP server.

| Tool | Purpose | | ---------------------------- | ---------------------------------------------------------- | | testConnection | Verify auth and connectivity before starting | | search | Find tables/assets by name; use include_fields for columns | | getTable | Schema, stats, metadata, domain membership, capabilities | | getValidationPredicates | List available validation rule types for a warehouse | | getDomains | List MC domains (only needed if table has no domain info) | | createMetricMonitorMac | Generate metric monitor YAML (dry-run) | | createValidationMonitorMac | Generate validation monitor YAML (dry-run) | | createComparisonMonitorMac | Generate comparison monitor YAML (dry-run) | | createCustomSqlMonitorMac | Generate custom SQL monitor YAML (dry-run) | | createTableMonitorMac | Generate table monitor YAML (dry-run) |

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Monitor types

| Type | Tool | Use When | | -------------- | ---------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | Metric | createMetricMonitorMac | Track statistical metrics on fields (null rates, unique counts, numeric stats) or row count changes over time. Requires a timestamp field for aggregation. | | Validation | createValidationMonitorMac | Row-level data quality checks with conditions (e.g. "field X is never null", "status is in allowed set"). Alerts on INVALID data. | | Custom SQL | createCustomSqlMonitorMac | Run arbitrary SQL returning a single number and alert on thresholds. Most flexible; use when other types don't fit. | | Comparison | createComparisonMonitorMac | Compare metrics between two tables (e.g. dev vs prod, source vs target). | | Table | createTableMonitorMac | Monitor groups of tables for freshness, schema changes, and volume. Uses asset selection at database/schema level. |

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Procedure

Follow these steps in order. Do NOT skip steps.

Validation Phase (Steps 1-3) -- MUST complete before any creation tool is called

The number one error pattern is agents skipping validation and calling a creation tool with guessed or incomplete parameters. Every field in the creation call must be grounded in data retrieved during this phase. Do not proceed to Step 4 until Steps 1-3 are fully satisfied.

Step 1: Understand the request

Ask yourself:

• What does the user want to monitor? (a specific table, a metric, a data quality rule, cross-table consistency, freshness/volume at schema level)
• Which monitor type fits? Use the monitor types table