Agent skill · data analytics · sickn33
monte-carlo-analyze-root-cause
Curated upstream guidance for Monte Carlo Analyze Root Cause; use when the workflow matches the user goal.
Why this skill is useful
Adds executable commands for root cause analysis using Monte Carlo's MCP tools, enabling deep data investigation capabilities.
What it needs
Requires monte-carlo installed locally. Requires monte-carlo account access. About 9k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Bundled upstream support Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes. When to Use Use when the user goal matches this upstream workflow. Monte Carlo Root Cause Analysis Skill This skill helps investigate data incidents — freshness delays, volume anomalies, schema changes, field metric drift, and ETL failures — by guiding the agent through a systematic investigation using Monte Carlo's MCP tools. It combines observability metadata with optional direct data querying to find the root cause. Monte Carlo tool routing (required): Always call Monte Carlo MCP tools through this plugin's bundled server, whose fully-qualified tool names are mcppluginmc-agent-toolkitmonte-carlo-mcp<tool> (e.g. mcppluginmc-agent-toolkitmonte-carlo-mcpgetalerts). Bare tool names used in this skill (getalerts, search, gettable, …) refer to that bundled server. If the session also has a separately-configured monte-carlo-mcp server, do not route to it — it may point at a different endpoint or credentials. Reference files live next to this skill file. Use the Read tool (not MCP resources) to access them: Investigation playbooks by issue type: references/<type>-investigation.md Data exploration patterns: references/data-exploration.md Intake when no incident ID: references/intake-no-incident.md Common root cause catalog: references/common-root-causes.md When to activate this skill Activate when the user: Mentions a Monte Carlo alert, incident, or anomaly Asks "why is this table stale?" or "why did row count drop?" Wants to investigate a data quality issue Asks about freshness, volume, or schema problems Mentions pipeline failures (Airflow, dbt, Databricks) Says things like "debug this alert", "investigate this incident", "root cause analysis" When NOT to activate this skill Do not activate when the user is: Creating monitors (use the monitoring-advisor skill) Running impact assessments before code changes …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/monte-carlo-analyze-root-cause