monte-carlo-incident-response

name	monte-carlo-incident-response
description	Orchestrate incident response — triage, root cause, remediate, prevent recurrence. USE WHEN active alerts, data broken, stale, pipeline failure, or investigate and fix a data incident.
when_to_use	Invoke when the user has an active data incident to handle — alerts firing, a table looks stale or broken, a pipeline failed, or they want to investigate root cause on a named table. Example triggers: "my orders table is stale, figure out why", "I have an unresolved alert on X, help me investigate", "alerts are firing — what should I do?", "investigate the most critical alert". Covers the full workflow: triage (classify/prioritize alerts) → root cause analysis (lineage, freshness history, query changes) → remediation → prevent recurrence. Do NOT invoke for coverage or "what should I monitor" requests (use proactive-monitoring instead) or for creating a specific monitor on a known table (use monitoring-advisor).
bucket	Agent-routing
version	1.0.0

Monte Carlo Incident Response Workflow

This workflow orchestrates the full lifecycle of a data incident by sequencing existing Monte Carlo skills. It does not contain investigation or remediation logic itself — each step loads the relevant skill's SKILL.md which has the actual instructions.

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.

When to activate this workflow

Activate when:

• Context detection routes here (active alerts detected + incident intent)
• User invokes /mc-incident-response
• User asks to "respond to an incident", "handle this alert", "triage and fix"
• User describes a data quality problem: "data is broken", "table is stale", "alert firing"

When NOT to activate this workflow

• User wants to create monitors or check coverage without an active incident — use proactive monitoring workflow
• User is editing a dbt model — defer to prevent skill (auto-activates via hooks)
• User wants to check table health without an incident context — use asset-health directly
• A skill is already active and handling the user's request

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Workflow Steps

Step 1 (conditional): Triage — when user has multiple/unknown alerts
Step 2: Root Cause Analysis — the core investigation
Step 3: Remediation — fix or escalate
Step 4 (optional): Prevent Recurrence — add monitoring

Determine entry point

Before starting, determine which step to enter based on the user's context:

• User has no specific alert ("I have alerts firing", "what's going on?") → Start at Step 1: Triage
• User has a specific alert ID or table ("alert ABC-123", "stg_payments is stale") → Skip to Step 2: Root Cause Analysis
• User knows the root cause ("the ETL job failed, help me fix it") → Skip to Step 3: Remediation
• Alert is an agent-monitor alert (alert_types starting with "Agent ", or the user's issue is about an AI agent) → for the investigation, read ../troubleshoot-agent-traces/SKILL.md instead of ../analyze-root-cause/SKILL.md; the remediation and monitoring steps still apply
• Ambiguous → Ask: "Do you have a specific alert or table you want to investigate, or should I check your recent alerts first?"

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Step 1: Triage (conditional)

Skill: Read and follow ../automated-triage/SKILL.md

Goal: Fetch recent alerts, score them by confidence and impact, identify which ones need investigation.

When to run: Only when the user doesn't already have a specific alert or incident to investigate. This step helps narrow down "I have alerts" into "these specific alerts need attention."

Scope MCP calls tightly. On large accounts, broad queries return hundreds of results, overflow the tool-result token limit, spill to disk, and force chunk reads — burning user tokens and exhausting the turn budget. Minimum scoping for tools this workflow touches:

• get_alerts → time filter (created_after, default last 7 days) + at least one of warehouse, table_names, severity
• search → needed to resolve a table name to its MCON (get_table requires MCON). Always pass limit (e.g. 5), the table name as query, and filter by warehouse_uuid or database/schema. warehouse_types alone is too broad. If multiple matches return: (1) auto-pick the match whose warehouse_display_name matches the user's named warehouse — do NOT stop to ask; (2) failing that, prefer the is_key_asset: true match; (3) only ask the user when none of these resolve it
• get_monitors → filter by mcons or warehouse_uuid

If scope is missing, ask the user before calling: "Which warehouse?", "How far back — today, this week?", "Any specific severity?".

Transition to Step 2: Once high-priority alert(s) are identified, tell the user:

"I've identified [N] high-priority alerts. Let me investigate the root cause of [specific alert/table]. Moving to root cause analysis."

Then proceed to Step 2 with the identified alert context.

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Step 2: Root Cause Analysis

Skill: Read and follow ../analyze-root-cause/SKILL.md

Goal: Investigate why the issue occurred — trace lineage, check ETL changes, analyze query modifications, profile data.

This is the core step. Most workflow entries start here.

Investigate linearly — do not re-call tools. Walk through the investigation once: (1) find the table, (2) fetch its alerts and freshness, (3) check lineage, (4) check recent queries/ETL. Call each tool at most once per table. If a tool result is insufficient, move to the next signal rather than re-calling with different params — burning turns on redundant calls exhausts the budget before the root cause is reached.

Transition to Step 3: When the root cause is identified (or the investigation reaches its limit), summarize findings and tell the user:

"Root cause identified: [summary]. Would you like me to help remediate this, or is the investigation sufficient?"

If the user wants to proceed, move to Step 3. If they say "that's enough", stop.

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Step 3: Remediation

Skill: Read and follow ../remediation/SKILL.md

Goal: Fix the issue using available tools, or escalate with full context if the fix requires actions outside the agent's capability.

Transition to Step 4: After remediation is complete (fix applied or escalation documented), offer prevention:

"The issue has been [fixed/escalated]. The root cause was [X]. Want me to help add a monitor to detect this type of issue earlier next time?"

If the user says yes, move to Step 4. If no, the workflow is complete.

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Step 4: Prevent Recurrence (optional)

Skill: Read and follow ../monitoring-advisor/SKILL.md

When loading monitoring-advisor for this step, frame the request as direct monitor creation — not coverage analysis. The user already knows what they want to monitor (the thing that just broke). Example framing:

"Based on the incident, I recommend adding a [freshness/volume/validation] monitor on [table]. Let me create the monitor configuration."

Goal: Add or update a monitor to catch this class of issue in the future.

Do not force this step. It is optional — offer it after remediation, and respect if the user declines.

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Orchestration Rules

• Users can enter at any step. The entry point section above determines where to start.
• Each step loads the actual skill's SKILL.md via relative path. This workflow does not replicate skill logic — it sequences it.
• Context carries forward through conversation naturally. Alert IDs, table names, root cause findings from earlier steps are available to later steps without explicit state passing.
• No state tracking or hooks. This is purely prompt-driven sequencing.
• User can exit anytime. If they say "that's enough" or "stop", respect it immediately.
• Do not skip back. The workflow moves forward. If the user wants to re-investigate after remediation, they can start a new workflow or invoke a skill directly.