quantitative-decision-gate

A decision matrix evaluation scores options across weighted dimensions like impact, effort, and risk. It computes a weighted total to automatically select the highest-scoring implementation approach, preventing unquantified choices.

A hard gate for tradeoff analysis prevents the AI from asking the user prematurely by requiring numeric scoring of options. It computes weighted totals and auto-selects the best option unless scores fall within a narrow margin.

You create automatic selection logic by scoring refactoring approaches across weighted dimensions. The system automatically selects the highest-scoring option when the score margin exceeds a defined threshold, bypassing manual selection.

You trigger an escalation during a decision matrix evaluation when the top scores are within a narrow margin or the decision is irreversible. This logic escalates the choice to alternative skills instead of auto-selecting.

The best way to force data-driven decisions is using a quantitative evaluation protocol that requires numeric scoring of library options. It auto-selects the highest-scoring library unless scores are close, triggering an escalation.