monte-carlo-analyze-root-cause

@monte-carlo-data⭐ 94 stars

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// RATINGS

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// README

Analyze Root Cause Skill

Investigate data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling.

What it does

Investigates freshness delays, volume anomalies, schema changes, ETL failures, query regressions, and field metric drift
Maps blast radius using table and field-level lineage
Traces bad data upstream to find the source
Correlates changes (query modifications, volume shifts, ETL failures) with incident timeline
Profiles actual data when a database MCP connector is available
Matches findings against a catalog of known root cause patterns

MCP Tools Required

Connect to Monte Carlo's MCP server (integrations.getmontecarlo.com/mcp). The skill uses these tools:

Tool	Purpose
`get_alerts`	Fetch incident/alert details
`search`	Find tables by name
`get_table`	Table metadata and fields
`get_asset_lineage`	Table-level lineage
`get_field_lineage`	Field-level lineage (trace to source column)
`get_table_freshness`	Update/freshness history
`get_table_size_history`	Row count and size history
`get_queries_for_table`	Read/write query history
`get_query_changes`	Detect SQL text modifications
`get_query_rca`	Failed/futile/missed query analysis
`get_change_timeline`	Unified change timeline
`get_etl_issues`	ETL pipeline issues (Airflow, dbt, Databricks) — pass `platform` param
`get_etl_jobs`	Find ETL jobs writing to tables (Airflow, dbt, Databricks) — pass `platform` param
`get_github_prs`	Recent GitHub PRs (via MC's GitHub integration)
`get_jobs_performance`	Job runtime stats, failure rates, trends
`alert_assessment`	Optional ~2-min triage of an incident (HIGH/MEDIUM/LOW confidence + impact)
`run_troubleshooting_agent`	Starts the Troubleshooting Agent (TSA) on an incident; auto-invoked when an incident UUID is present
`get_troubleshooting_agent_results`	Polls TSA results for an incident

Credits: alert_assessment and run_troubleshooting_agent consume Monte Carlo credits the same way the Troubleshooting Agent does when launched from the Monte Carlo UI.

Optional: A database MCP server (Snowflake, BigQuery, Redshift) for direct SQL queries.

Example prompts

"Investigate alert 12345"
"Why is the orders table stale?"
"Row count dropped 50% on analytics.prod.revenue — what happened?"
"Debug this freshness issue on our daily pipeline"
"The dashboard shows yesterday's data — can you find out why?"

Investigation flow

Intake (alert ID or user description)
    ↓
Auto-invoke TSA (if incident UUID + not opt-out + not narrow check)  ─┐
    ↓                                                                  │
Map blast radius (upstream + downstream lineage)                       │ TSA runs
    ↓                                                                  │ async in
Investigate by issue type (freshness / volume / schema / ETL / query)  │ parallel
    ↓                                                                  │
Check upstream causes (walk lineage chain)  ── poll TSA #1 ────────────┤
    ↓                                                                  │
Profile data (if DB connector available)                               │
    ↓                                                                  │
Check code changes (GitHub MCP or MC query changes)                    │
    ↓                                                                  │
Synthesize: root cause + evidence + impact + fix  ── poll TSA #2 ─────┘
                                                    + merge findings

When intake has no incident UUID, when the user explicitly opts out, or when the request is a narrow scoped check (e.g. "is X stale right now?"), TSA is skipped and the manual flow runs alone.

Reference files

File	Description
`references/freshness-investigation.md`	Freshness delay playbook
`references/volume-investigation.md`	Volume anomaly playbook
`references/schema-investigation.md`	Schema change playbook
`references/etl-failure-investigation.md`	ETL failure playbook
`references/query-change-investigation.md`	Query modification playbook
`references/field-anomaly-investigation.md`	Field metric drift playbook
`references/data-exploration.md`	SQL patterns for data profiling
`references/intake-no-incident.md`	Intake flow when no incident ID
`references/common-root-causes.md`	Catalog of known root cause patterns

// HOW IT'S BUILT

KEY FILES

skills/analyze-root-cause/SKILL.mdREADME.md

// REPO STATS

94 stars

// SHARE

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