Bundled upstream support

Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes.

Monte Carlo Monitoring Advisor Skill

This skill handles all monitoring requests -- coverage analysis, data monitor creation, and AI agent monitoring. It routes to the right reference file based on the user's intent.

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:

• Data monitor creation procedure: references/data-monitor-creation.md (relative to this file)
• Agent monitor creation procedure: references/agent-monitor-creation.md (relative to this file)
• Per-type references: references/data-*.md and references/agent-*.md (relative to this file)

When to activate this skill

Activate when the user:

• Asks about monitoring coverage, data coverage, or coverage gaps
• Wants to understand what's monitored vs. not in their warehouse
• Asks about use cases, use-case criticality, or use-case analysis
• Wants to explore their data estate and find what needs monitoring
• Says things like "what should I monitor?", "where are my coverage gaps?", "show me my use cases"
• Asks about unmonitored tables with anomalies or importance-based prioritization
• Asks to create, add, or set up a monitor (e.g. "add a monitor for...", "create a freshness check on...", "set up validation for...")
• Mentions monitoring a specific table, field, or metric
• Wants to check data quality rules or enforce data contracts
• Asks about monitoring options for a table or dataset
• Requests monitors-as-code YAML generation
• Wants to add monitoring after new transformation logic (when the prevent skill is not active)
• Asks about monitoring AI agents, agent latency, agent token usage, or agent quality
• Wants to set up alerts on agent behavior or execution patterns
• Asks about investigating agent traces or conversations
• Says things like "monitor my agent", "track agent latency", "alert on agent errors"
• Asks about agent evaluation monitors, trajectory monitors, or validation monitors
• Mentions agent observability or agent monitoring

When NOT to activate this skill

Do not activate when the user is:

• Just querying data or exploring table contents
• Triaging or responding to active alerts (use the prevent skill's Workflow 3)
• Running impact assessments before code changes (use the prevent skill's Workflow 4)
• Asking about existing monitor configuration (use get_monitors directly)
• Editing or deleting existing monitors

Prerequisites

• Required: Monte Carlo MCP server (monte-carlo-mcp) must be configured and authenticated
• Optional: A database MCP server (Snowflake, BigQuery, Redshift, Databricks) for SQL profiling of table usage patterns

Available MCP tools

All tools are available via the monte-carlo-mcp MCP server.

Coverage and discovery tools

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