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 mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__<tool> (e.g. mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_alerts). Bare tool names used in this skill (get_alerts, search, get_table, …) 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 (use the prevent skill)
• Looking at storage costs (use the storage-cost-analysis skill)
• Exploring pipeline performance without a specific incident (use the performance-diagnosis skill)

Prerequisites

Required: Monte Carlo MCP server (integrations.getmontecarlo.com/mcp) must be configured and authenticated.

Optional but recommended:

• Database MCP server (Snowflake, BigQuery, Redshift, Databricks) — enables direct SQL queries for deeper data investigation. Without this, the skill can still analyze using MC's metadata tools but cannot profile actual data.
• GitHub MCP server — enables searching for recent PRs that may have caused the issue. Without this, the skill falls back to MC's query change detection.

MCP Tools Used

From Monte Carlo MCP server

…