---
name: "Problem clarity engine"
repo: https://github.com/pratikshadake/claude-product-management-skills
source: https://github.com/pratikshadake/claude-product-management-skills/blob/HEAD/skills/problem-clarity/SKILL.md
license: "MIT"
install: "npx skills add pratikshadake/claude-product-management-skills --skill problem-clarity"
page: https://100xlab.com/resources/agent-skills/pratikshadake-claude-product-management-skills--problem-clarity--wqfyez
---

# Problem Clarity Engine

## Purpose
Determine whether a proposed idea represents a real, meaningful user problem
worth solving.

## When to Use
- New feature ideas
- Startup concepts
- Vague user complaints

## Steps
1. Identify the target user.
2. Describe the current pain or limitation.
3. Measure frequency and severity.
4. Identify existing workarounds.
5. Judge whether the pain is strong enough to justify a solution.

## Output

### Target User
### Core Problem
### Evidence of Pain
### Current Workarounds
### Severity Assessment
High / Medium / Low

### Verdict
Worth Solving / Not Worth Solving

## References
See [worked example](references/example.md) for a complete scenario.

---

## Supporting file: references/example.md

# Example: Problem Clarity — AI Meeting Notes

## Target User
Startup founders who attend many Zoom meetings.

## Core Problem
They forget key decisions and action items after meetings.

## Evidence of Pain
- Notes scattered across tools  
- Frequent follow-up confusion  
- Manual note-taking during calls  

## Current Workarounds
- Writing notes manually  
- Recording meetings but never revisiting  

## Severity Assessment
High

## Verdict
**Worth Solving**