monte-carlo-instrument-agent

# 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 appro...