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Analysis & spreadsheets48k · #3628

monte-carlo-monitor-creation

Has your agent create data monitors using Monte Carlo. It generates code so monitors can be easily added to the development process.

What it does

This tool helps you create 'monitors' to check your data quality. It generates code that can be used to set up these checks automatically.

When to use it

  • ✓When you want to create a new data quality check.
  • ✓When you want to set up automatic alerts for data issues.
  • ✓When you need to generate code to add data checks to an automated process.

Example of what to ask your AI

“Create a monitor to check if the 'email' column in 'my_db.my_table' is never null.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/monte-carlo-monitor-creation/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/monte-carlo-monitor-creation/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
View on GitHub ↗Raw file
How it works

The tool first understands what you want to monitor and what data is needed. Then, it uses specific tools to generate code (YAML) that describes this check, without applying it directly.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-monitor-creation

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/monte-carlo-monitor-creation/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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

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)

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.

Procedure

Follow these steps in order. Do NOT skip steps.

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

…

FAQ

What are 'monitors'?

Monitors are automatic checks that alert you to data quality problems, like missing or incorrect data.

What is 'YAML'?

YAML is a file format used to configure things, like these data checks. It's like a list of instructions.

Does this tool change my data?

No, it only creates the code to set up the monitors. You need to apply this code separately for the checks to work.

Details

Original description by the author

Guides creation of Monte Carlo monitors via MCP tools, producing monitors-as-code YAML for CI/CD deployment.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#3628
License
MIT
Updated
10/15/2026

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