Framework-based assessment of product-market fit using signals from 46 product leaders.
Works with
Apply the Sean Ellis \"very disappointed\" survey as a leading PMF indicator, targeting 40% threshold before long-term retention data is available
Diagnose stage through retention curves, reference customer counts, and customer pull signals; distinguish between vanity metrics and genuine PMF evidence
Recognize PMF across four levels (nascent to extreme) with segment-specific fit; understand that P
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmeasuring-product-market-fitExecute the skills CLI command in your project's root directory to begin installation:
Fetches measuring-product-market-fit from refoundai/lenny-skills and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate measuring-product-market-fit. Access via /measuring-product-market-fit in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Help the user assess and achieve product-market fit using frameworks from 46 product leaders.
When the user asks about product-market fit:
Sean Ellis: "How would you feel if you could no longer use this product? Very disappointed, somewhat disappointed, or not disappointed. If 40% say 'very disappointed,' you're on the right track." This is a leading indicator of PMF before long-term retention data is available. Focus on the "very disappointed" segment as your core value indicator.
Uri Levine: "Product market fit has one metric. Retention. If you create value, they will come back. If they're not coming back, you're not creating value." Look for retention curves that flatten over time rather than decaying to zero. The "smile curve" - where engagement increases over time - is the strongest signal.
Matt MacInnis: "Product market fit is something where you absolutely know it when you see it. Therefore if you don't absolutely know it, you don't have it." If there's doubt, you likely don't have it. Look for the market pulling the product out of your hands.
Casey Winters: "Protecting what you've built is increasingly important once you build scale. You might fall out of product market fit in a year or five years if you're not continually making your product better." Markets shift, competitors improve, and user expectations rise.
Christian Idiodi: "The holy grail is really a reference customer - somebody who loves it enough to tell people about it. I want 6-8 references for B2B, 15-25 for B2C as an indication of PMF." Don't launch publicly until you have secured the target number of references from early users.
Karri Saarinen: "The way we think about it is, 'Do we have the fit in specific segments?' and how strong that fit is." Find PMF in one segment first (e.g., early-stage startups) before expanding. Double down where you see natural pull.
Casey Winters: "If you have a product that retains well and you can't find more users for it, I don't think that's product market fit." True PMF requires both a retaining product AND a scalable, built-in distribution mechanism.
Todd Jackson: "There's essentially four levels: nascent, developing, strong, extreme." Level 1 (3-5 customers), Level 2 (5-25 customers), Level 3 (25-100 customers), Level 4 (100+ customers). Sequence focus: satisfaction at Level 1, demand at Level 2, efficiency at Level 3.
Raaz Herzberg: "We felt the questions change - 'How are you pricing this? When can we start a POV?' That's real intent." True pull is characterized by customers driving next steps, not just saying "this is interesting."
Jeff Weinstein: "During those 20 minutes our customers weren't furious. That was the signal we did not have product market fit." If your product goes down and nobody notices or complains, you haven't solved a mission-critical problem.
For all 64 insights from 46 guests, see references/guest-insights.md
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in measuring-product-market-fit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: measuring-product-market-fit is focused, and the summary matches what you get after install.
I recommend measuring-product-market-fit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: measuring-product-market-fit is the kind of skill you can hand to a new teammate without a long onboarding doc.
measuring-product-market-fit fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added measuring-product-market-fit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend measuring-product-market-fit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: measuring-product-market-fit is focused, and the summary matches what you get after install.
measuring-product-market-fit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: measuring-product-market-fit is focused, and the summary matches what you get after install.
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