ranbot-ai Monte Carlo Automated Triage
This skill helps you design, test, and deploy an automated triage agent for Monte Carlo alerts. Rather than a fixed workflow, it gives you the building blocks — a set of MCP tools, a description of each triage stage, and a working example — so you can build a process that matches how your team actually responds to alerts.
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-configuredmonte-carlo-mcpserver, do not route to it — it may point at a different endpoint or credentials.
Read the reference files before proceeding:
- Triage stages and customisation:
references/triage-stages.md(relative to this file) - Working example workflow:
references/triage-example.md(relative to this file)
When to activate this skill
Activate when the user:
- Wants to triage or investigate recent Monte Carlo alerts (interactively or automated)
- Wants to set up automated triage for Monte Carlo alerts
- Asks to run agentic triage or investigate recent alert activity
- Wants to understand what triage tools are available and how to use them
- Is building or refining a triage prompt for their environment
- Wants to move from manual alert review to automated or semi-automated triage
When NOT to activate this skill
Do not activate when the user is:
- Investigating a specific known incident (help them directly)
- Creating or configuring monitors (use the monitoring-advisor skill)
- Running impact analysis before a code change (use the prevent skill)
Available MCP tools
All tools are available via the monte-carlo-mcp MCP server.
| Tool | Toolset | Purpose |
|---|---|---|
get_alerts |
default | Fetch recent alerts for a time window |
alert_assessment |
default | Score an alert by incident likelihood and potential impact (HIGH/MEDIUM/LOW each) |
run_troubleshooting_agent |
default | Run the Monte Carlo Troubleshooting Agent on a single alert; async by default — returns immediately, reuses existing results when available |
get_troubleshooting_agent_results |
default | Poll an async troubleshooting run by incident_id; returns status (not_found/running/success/failed) and results when complete |
update_alert |
default | Update an alert's status and/or declare an incident by setting severity |
set_alert_owner |
default | Assign an owner to an alert by email |
create_or_update_alert_comment |
default | Post or update a triage comment on an alert |
mark_event_as_normal |
default | Mark all anomaly events in an alert as normal, triggering ML threshold recalibration to prevent re-alerting on the same pattern |
How to approach automated triage
Read references/triage-stages.md for a full description of each stage and how to customise it. The high-level flow is:
- Fetch alerts — decide which alerts to triage and over what time window
- Initial investigation — score every alert by incident likelihood and potential impact using
alert_assessment - Deep troubleshooting — run
run_troubleshooting_agenton high-signal alerts to get root cause analysis - Classify — use the troubleshooting output to classify each alert
- Take actions — post comments, update statuses, message Slack, create tickets
The triage process is not fixed. Read the stages reference to understand the options and tradeoffs at each step, then design a workflow that fits your team's needs.
The longer-term direction
Most teams move through roughly the same arc, though the pace and path vary:
- Start with recommendations. Run manually and have the agent post comments describing what it found and what it would do — no actual status changes or external actions. Use this to tune the workflow until the output matches how your team would respond manually.
- Automate, still in recommendation mode. Once the output looks right, put it on a schedule. Keep it in recommendation mode while you validate it's behaving well on real traffic.
- **Replace recommendati