ranbot-ai/automated-triage

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-configured monte-carlo-mcp server, 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)

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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)

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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 |

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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:

1. Fetch alerts — decide which alerts to triage and over what time window
2. Initial investigation — score every alert by incident likelihood and potential impact using alert_assessment
3. Deep troubleshooting — run run_troubleshooting_agent on high-signal alerts to get root cause analysis
4. Classify — use the troubleshooting output to classify each alert
5. 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