The problem

Critical business decisions are often stalled by hidden biases, unstated assumptions, and subjective judgments disguised as hard facts. Founders and operators struggle to identify why a decision feels distorted or where evidence is actually missing from the reasoning process.

What it does

• Identifies and ranks input variables by their actual impact on the final choice.
• Surfaces hidden assumptions and defines specific conditions that would prove them wrong.
• Pins subjective judgment calls to specific roles to prevent accountability gaps.
• Maps evidence strength to distinguish between data-driven conclusions and gut feel.
• Detects cognitive distortions like sunk cost bias, recency bias, and political interference.

Why this beats prompting it yourself

Generic prompts often result in surface-level pros and cons lists that ignore the underlying power dynamics and cognitive biases. This skill uses a structured diagnostic framework to force the disclosure of falsification conditions and overweighting flags that standard reasoning models overlook.

Use cases

• Auditing a stalled product roadmap decision to find the hidden blocker.
• Evaluating a high-stakes hire where internal politics might be overriding data.
• Mapping the risk of a new market entry based on weak evidence or assumptions.
• Analyzing why a specific project budget keeps ballooning despite "data-driven" planning.

Known limitations

Requires the user to provide specific context regarding stakeholders and goals. It cannot audit a decision without access to the inputs and perceived constraints of the organization.