# Hidden Assumption Finder

## Purpose

Find user, business, product, technical, and market assumptions hidden inside a brief, feature idea, or stakeholder request.

## When To Use

Use this skill when a team is treating a brief, feature request, product idea, or stakeholder opinion as if it is already true.

## When Not To Use

Do not use this skill to create final requirements, UI screens, visual concepts, or validated findings. Treat the output as a discovery aid that needs human review.

## Inputs Needed

- Project brief, idea, feature request, or stakeholder statement.
- Known users and business goals.
- Any evidence currently available.
- Constraints, risks, or areas of uncertainty.

If some information is missing, say "unknown" rather than guessing.

## Instructions

You are helping a UX designer during product discovery. Apply Assumption mapping to the provided context.

Follow these steps:

1. Restate the request in plain language.
2. Extract only the known facts from the provided context.
3. Identify assumptions hidden inside the request.
4. Group assumptions by user, business, product or technical, and market context.
5. Rate each assumption by uncertainty and potential impact.
6. Recommend which assumptions should be tested first.
7. Avoid treating AI-generated assumptions as validated evidence.

## Output Format

Return the output using this structure:

### 1. Plain-Language Summary

### 2. Known Context

### 3. Assumptions or Risks

### 4. Missing Information

### 5. Recommended Output

### 6. Questions To Ask Next

### 7. Best Next Step

## Quality Criteria

A strong output should be specific to the provided context, separate facts from assumptions, avoid premature solutions, and help the designer decide what to learn next.

## Example Input

The sales team wants us to add an AI recommendation widget to the dashboard because customers are not discovering relevant reports. They believe this will increase engagement and reduce support questions.

## Example Output

Use the requested output format and keep evidence, assumptions, and recommendations clearly separated.