Monte Carlo Instrument-Agent Skill

This skill walks an MC Agent Observability customer through instrumenting a new AI agent in their Python codebase: detect AI libraries → install the Monte Carlo OpenTelemetry SDK + matching instrumentors → generate mc.setup() (with SimpleSpanProcessor when serverless) → propose @trace_with_workflow / @trace_with_task decorator diffs → confirm env vars (only when needed) → verify traces flow via get_agent_metadata.

The skill produces traces. It is not for monitoring or alerting on existing traces — that's monte-carlo-monitoring-advisor. The two skills are sequential: instrument-agent first, monitoring-advisor afterward.

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 file. Use the Read tool (not MCP resources) to access them.

CRITICAL — Never modify any file without explicit user approval

This skill must not modify any file in the customer's codebase without explicit per-file user approval. This rule covers: