monte-carlo-context-detection

Data observability routing analyzes workspace signals like dbt_project.yml and conversation context to classify intent, safely scope API probes, and automatically route requests to the most relevant Monte Carlo skill.

Incident routing relies on workspace signals such as monte-carlo.yml, open .sql models, and conversation signals like coverage or specific asset references to resolve intent and determine whether to auto-route or gather scope.

Context detection is used when data alerts or requests are ambiguous, utilizing signal definitions to classify intent, minimize unnecessary API probes, and safely escalate by presenting options when intent remains unclear.

Yes, context detection reads dbt_project.yml and monte-carlo.yml files as workspace signals, combining them with conversation context to resolve intent and route data requests to the appropriate skill or workflow.

Handling unclear data incidents involves analyzing incident signals against routing logic and signal definitions, presenting options for safe escalation rather than executing unnecessary API probes when intent cannot be resolved.

Classifying data observability intent uses Step 0–4 routing logic to evaluate workspace and conversation signals, determining whether to auto-route the request, gather additional scope, or present options to the user.