Skill UI

This skill acts as an expert risk manager, providing comprehensive guidance on monitoring portfolio risk, calculating R-multiples, and optimizing position limits. It covers advanced quantitative finance topics such as Value at Risk (VaR), correlation analysis, and stress testing using Monte Carlo simulations. Use it for systematic risk assessment, developing hedging strategies (options/futures), and implementing rigorous risk controls in trading workflows.

An advanced skill for Scrum Masters that uses Python scripts to analyze raw sprint data exported from tools like Jira. It provides comprehensive insights including Monte Carlo velocity forecasting, a multi-dimensional team health score (0-100), and tracking of action items and recurring themes from retrospectives. Ideal for process improvement, sprint planning, and stakeholder reporting.

Monte Carlo Treasury is a risk modeling helper that simulates cash flows and liquidity scenarios for corporate treasuries using Monte Carlo techniques, so finance teams can assess funding needs, stress-test portfolios, and optimize capital buffers.

Leverages Monte Carlo-based simulation to spot potential software vulnerabilities by feeding randomized inputs into modules, tracking failure likelihoods, and highlighting risky code paths for security testing.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

This skill provides building blocks and workflows to programmatically design, test, and deploy automated triage agents for Monte Carlo alerts. Users can fetch, score, and troubleshoot alerts interactively or set up scheduled, reusable workflows, moving from manual review to fully automated incident response.

A systematic skill designed to investigate data incidents, including freshness delays, volume anomalies, schema changes, and ETL failures. It guides the agent through a comprehensive process using Monte Carlo's observability metadata and optional direct data querying to pinpoint the root cause.

This skill integrates with Monte Carlo's observability platform to provide a comprehensive health report for data assets. It checks critical metrics including data freshness, active alerts, monitoring coverage, asset importance, and the health of upstream dependencies. Activates when users inquire about the status, reliability, or quality of a data table.

This skill provides comprehensive monitoring advisory services for data assets and AI agents. It helps users assess data coverage gaps, analyze use-case criticality, and automatically create data quality monitors (table, metric, validation). Furthermore, it enables monitoring of AI agent performance, latency, and observability, ensuring robust data governance across the entire ML lifecycle.