SkillsAdd

wdavidturner/product-skills

pmf-survey

Use when asked to "PMF survey", "measure product-market fit", "40% rule", "Sean Ellis test", "Rahul Vohra method", or "how disappointed would you be".

First seen Jan 20, 2026

Installation

$ npx skills add wdavidturner/product-skills --skill pmf-survey
View on skills.sh ↗ GitHub repository ↗

Summary

  • Use when asked to "PMF survey", "measure product-market fit", "40% rule", "Sean Ellis test", "Rahul Vohra method", or "how disappointed would you be".
  • Helps quantify product-market fit and systematically improve it.
  • The PMF Survey framework (created by Sean Ellis, popularized by Rahul Vohra at Superhuman) measures how disappointed users would be without your product and turns that data into a roadmap.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

agent-browser Same topic
vercel-labs/agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, includ…

810.4K installs
azure-diagnostics Same topic
microsoft/azure-skills

Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe …

568.9K installs
azure-validate Same topic
microsoft/azure-skills

Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure…

567.7K installs
azure-aigateway Same topic
microsoft/azure-skills

Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: sem…

566.3K installs
azure-compute Same topic
microsoft/azure-skills

Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compar…

510K installs
supabase-postgres-best-practices Same topic
supabase/agent-skills

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill …

391.6K installs

Also in this package

Other skills from wdavidturner/product-skills · top by installs.

npx skills add wdavidturner/product-skills
thinking-in-bets 56 strategic-narrative 55 okrs 49 hierarchy-of-engagement 44 design-sprint 44 seven-powers 42 hooked-model 41 jobs-to-be-done 41 hierarchy-of-marketplaces 40 opportunity-solution-trees 39

Browse all from wdavidturner/product-skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 20
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,094 B
  • docs SUMMARY.md 422 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 32 installs

SKILL.md

PMF Survey (Product-Market Fit Survey)

What It Is

The PMF Survey is a method to measure and systematically improve product-market fit. The core insight: you can put a number on product-market fit, and you can use that number to write your roadmap.

The key question: "How would you feel if you could no longer use this product?"

  • Very disappointed - "I'd be devastated. I need this."
  • Somewhat disappointed - "I'd be bummed but I'd find something else."
  • Not disappointed - "I wouldn't really care."

Sean Ellis discovered that companies with 40% or more "very disappointed" responses almost always grew successfully, while those under 40% struggled. This benchmark has held across thousands of companies.

Rahul Vohra at Superhuman took this further: he built an engine that uses survey responses to algorithmically generate a roadmap guaranteed to increase PMF score.

When to Use It

Use the PMF Survey when you need to:

  • Quantify product-market fit before making major investment decisions
  • Decide whether to pivot or double down
  • Prioritize your roadmap based on what will actually move the needle
  • Identify your best customer segment (who loves you most)
  • Track PMF over time as you iterate
  • Make the case to investors with data, not gut feeling

When Not to Use It

  • You have fewer than 30 active users (sample too small)
  • Users haven't had enough time to experience value (survey too early)
  • The product is employer-mandated (users had no choice)
  • You want to validate a hypothesis without building (use JTBD instead)

Patterns

Detailed examples showing how to apply the PMF Survey correctly. Each pattern shows a common mistake and the correct approach.

Critical (get these wrong and you've wasted your time)

Pattern What It Teaches
[survey-question-wording](patterns/survey-question-wording.md) Use the exact wording - variations invalidate the benchmark
[who-to-survey](patterns/who-to-survey.md) Only survey users who experienced the core value
[forty-percent-benchmark](patterns/forty-percent-benchmark.md) 40% is a threshold, not a target - understand what it means
[ignoring-somewhat-disappointed](patterns/ignoring-somewhat-disappointed.md) The "somewhat disappointed" segment is your growth engine
[segment-before-action](patterns/segment-before-action.md) You must segment responses before acting on feedback

High Impact

Pattern What It Teaches
[sample-size-myths](patterns/sample-size-myths.md) 40-50 responses is enough - don't wait for statistical perfection
[wrong-timing](patterns/wrong-timing.md) Survey after first value, not after signup
[acting-on-not-disappointed](patterns/acting-on-not-disappointed.md) Stop trying to convert the "not disappointed" users
[main-benefit-filter](patterns/main-benefit-filter.md) Only act on feedback from users who love your core value
[doubling-down-vs-fixing](patterns/doubling-down-vs-fixing.md) Half your time on strengths, half on objections
[high-expectation-customers](patterns/high-expectation-customers.md) Learn your ideal customer profile from users who love you
[pivot-vs-persevere](patterns/pivot-vs-persevere.md) Check for segment-level PMF before deciding to pivot

Medium Impact

Pattern What It Teaches
[tracking-over-time](patterns/tracking-over-time.md) How to measure PMF progress without invalidating comparisons
[follow-up-questions](patterns/follow-up-questions.md) The three questions that unlock the roadmap algorithm
[enterprise-vs-consumer](patterns/enterprise-vs-consumer.md) Adapting the survey for B2B vs B2C contexts

Deep Dives

Read only when you need extra detail.

  • references/pmf-survey-playbook.md: Expanded framework detail, checklists, and examples.

Resources

Articles:

  • How Superhuman Built an Engine to Find Product-Market Fit by Rahul Vohra (First Round Review) - the definitive guide
  • Sean Ellis's original PMF survey methodology

Books:

  • Hacking Growth by Sean Ellis - context on growth and PMF metrics
  • The Lean Startup by Eric Ries - complementary framework for validation

Podcasts:

  • Lenny's Podcast episode with Rahul Vohra - deep dive on the methodology and how Superhuman applied it

Credits:

  • Sean Ellis - Created the original PMF survey question and discovered the 40% benchmark
  • Rahul Vohra - Popularized the methodology and built the "PMF Engine" algorithm for systematically improving the score