agentleFS
pratikshadakepratikshadake/claude-product-management-skills/skills/problem-clarity/SKILL.md

problem-clarity

Skill33 starsChanged 9 months agoMITView on GitHub

Evaluates whether a proposed idea addresses a genuine user problem worth solving. Use when assessing new feature ideas, startup concepts, or vague user complaints to determine if the pain justifies building a solution.

Files
SKILL.md

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 for a complete scenario.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.