monte-carlo-performance-diagnosis

# Monte Carlo Performance Diagnosis Skill This skill helps diagnose data pipeline performance issues using Monte Carlo's cross-platform observability data. It works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes. > **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: - Tiered investigation approach: `references/investigation-tiers.md` (relative to this file) - Query analysis patterns: `references/query-analysis.md` (relative to this file) ## When to activate this skill Activate when the user: - Asks about slow pipelines, jobs, or queries - Wants to find expensive or costly queries - Mentions performance regressions or degradation - Asks "why is this pipeline slow?" or "what's using the most compute?" - Wants to compare performance over time or find bottleneck tasks - Asks about failed or futile query patterns...