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monte-carlo-monitoring-advisor

Analyze data coverage and generate monitors-as-code YAML for warehouses and AI agents.

90|6|Updated Feb 25, 2026
Search Tags:#monitoring#monte-carlo#data-coverage#monitors-as-code#agent-observability#warehouse-monitoring
One-click install
npx skills add https://github.com/monte-carlo-data/mc-agent-toolkit --skill monte-carlo-monitoring-advisor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: monte-carlo-monitoring-advisor
Source: https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/monitoring-advisor
Command: npx skills add https://github.com/monte-carlo-data/mc-agent-toolkit --skill monte-carlo-monitoring-advisor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Analyze data coverage, orchestrate warehouse discovery, and automatically generate monitors-as-code to improve observability for data pipelines and AI agents.

Core Features & Use Cases

  • Discovers warehouses, use cases, and assets to map your data landscape.
  • Analyzes coverage gaps and prioritizes monitors based on anomaly activity and criticality.
  • Generates monitors-as-code YAML for data monitors (metric, validation, and table monitors) and AI agent monitors (metric, trajectory, evaluation, and validation).
  • Guides users through end-to-end monitoring workflows, enabling rapid deployment across editors.

Quick Start

Run the Monitoring Advisor to generate monitors YAML for your first warehouse and deploy with the Monte Carlo CLI.

Frequently Asked Questions about monte-carlo-monitoring-advisor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automatically generate monitors-as-code for warehouse tables?▼

You can generate monitors-as-code by analyzing data coverage to discover warehouses and assets, then outputting YAML for metric, validation, and table monitors to deploy via the CLI.

What is agent observability and how do I monitor AI agents?▼

Agent observability tracks AI agent behavior through metric, trajectory, evaluation, and validation monitors, generated as YAML to evaluate agent performance and pipeline outputs.

How do I identify data coverage gaps across my warehouse?▼

Warehouse discovery maps your data landscape by analyzing tables and assets, prioritizing monitors based on anomaly activity and criticality to highlight coverage gaps.

Can I deploy Monte Carlo monitors using YAML files?▼

Yes, the workflow generates monitors-as-code YAML that you can deploy across editors using the Monte Carlo CLI for both data and AI agent monitoring.

What types of data monitors can I create for warehouse observability?▼

You can create metric monitors, validation monitors, and table monitors as YAML configurations to improve observability for data pipelines and warehouse tables.

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