hidden decision risk finder

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.