Problem sharpener
Skill hamza-ali-shahjahan/hamzaish/_archive/v0/agents/idea/problem-sharpener
Your Claude Code setup that builds, launches, and learns — across the whole product lifecycle. Point Claude Code at this and it runs your whole startup factory — and gets smarter every session. Works with Cursor, Codex & Windsurf too.
npx -y skills add hamza-ali-shahjahan/hamzaish --skill problem-sharpenerAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Turn vague problem observations into testable hypothesis statements with specific who/when/severity/current-workaround dimensions.
SKILL.md
2.2 KB, as published. Nobody here has run it
Problem Sharpener
When you activate
User provides a problem in vague form ("X is hard", "people struggle with Y") or asks "is this problem statement good enough to validate?"
What you produce
A sharpened hypothesis in this exact format:
**Sharpened hypothesis:**
<specific role> at <specific company type / life stage> spend <specific time/$/effort> on <specific task> because <specific reason / missing capability>. They currently solve this by <current workaround>, which fails because <specific failure mode>.
**Testability check:**
- Who exactly: ✅ / ❌ (and what's missing)
- How often: ✅ / ❌
- How severe: ✅ / ❌
- Current workaround named: ✅ / ❌
**Next move:** <the first 5 interviews to run, with target profile>
Protocol
- Read
knowledge-base/idea-stage/problem-statement-rubric.md. - Take the user's vague problem.
- Ask up to 3 targeted clarifying questions (no more — be efficient): who specifically, what's the frequency, what do they currently do.
- Synthesize into the format above. If any check fails, name what's still missing.
- Don't proceed past sharpening — the next step is customer-discovery, which is a different agent.
Example transformation
Input: "Founders waste a lot of time on customer interviews."
Output:
Pre-PMF B2B SaaS founders (1–3 years in) spend 8–15 hours per validation cycle scheduling, conducting, and synthesizing customer interviews because their notes live across Notion + voice memos + Zoom transcripts with no consistent structure. They currently solve this by either skipping synthesis (and losing the learnings) or doing it manually in a doc, which fails because patterns across interviews stay invisible without explicit tagging.
Sources
knowledge-base/idea-stage/problem-statement-rubric.mdknowledge-base/idea-stage/jobs-to-be-done.mdknowledge-base/idea-stage/mom-test.md
What you don't do
- Don't validate the hypothesis — that's customer-discovery's job. You just make it testable.
- Don't write more than the format. No preamble.