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Measuring Product Market Fit

Community
cnfeat
measuring-product-market-fit

Help users assess and achieve product-market fit. Use when someone is trying to determine if they have PMF, measuring user engagement and retention, running the Sean Ellis survey, or figuring out if they should scale or keep iterating.

Overview

Publishercnfeat
Repositorytop-pm-skills
Skill namemeasuring-product-market-fit
Stars
55
Forks
6
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cnfeat on GitHub. Read the source before you install it.

Installation

Install the Measuring Product Market Fit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cnfeat/top-pm-skills/tree/main/参考skill/lenny-skills-main%20(2)/lenny-skills-main/skills/measuring-product-market-fit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Measuring Product Market Fit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Measuring Product Market Fit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Measuring Product Market Fit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Measuring Product-Market Fit

Help the user assess and achieve product-market fit using frameworks from 46 product leaders.

How to Help

When the user asks about product-market fit:

  1. Understand their stage - Ask how many customers they have, what their retention looks like, and what signals they're seeing (or not seeing)
  2. Diagnose the situation - Determine if they're confusing vanity metrics with PMF, if they have PMF in a specific segment, or if they're clearly pre-PMF
  3. Apply the right framework - Help them use the Sean Ellis survey, retention curves, or reference customer counts depending on their situation
  4. Guide next steps - Help them decide whether to scale or continue iterating based on the evidence

Core Principles

Use the Sean Ellis "disappointment" survey

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.

Retention is the ultimate metric

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.

PMF is obvious when you have it

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.

PMF is not static - it can be lost

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.

Reference customers validate PMF

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.

PMF exists in segments, not universally

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.

PMF requires distribution, not just retention

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.

PMF is multi-stage, not binary

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.

Look for customer "pull"

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."

A lack of outrage during outages = no PMF

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.

Questions to Help Users

  • "If users couldn't use your product anymore, what percentage would be 'very disappointed'?"
  • "What does your retention curve look like at day 7, 30, and 90?"
  • "Do you have customers willing to be references and tell others about you?"
  • "Is the market pulling the product from you, or are you pushing it on them?"
  • "Are customers driving next steps (asking about pricing, timelines) or just being politely interested?"
  • "What specific segment do you have the strongest fit in?"

Common Mistakes to Flag

  • Confusing launch spikes with PMF - Product Hunt success or press coverage doesn't mean you have PMF. Look for sustained organic growth
  • Ignoring retention data - If users aren't coming back, you don't have PMF regardless of how many you acquire
  • Scaling too early - Paid growth before PMF just burns cash and can damage your brand
  • Conflating TAM with PMF - A large market opportunity doesn't mean you've achieved fit within it
  • Listening to "somewhat disappointed" users - Focus on what makes "very disappointed" users love you, not what would make lukewarm users slightly happier

Deep Dive

For all 64 insights from 46 guests, see references/guest-insights.md

Related Skills

  • Designing Growth Loops
  • Retention & Engagement
  • Conducting User Interviews
  • Startup Pivoting

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Measuring Product Market Fit AI skill do?

Help users assess and achieve product-market fit. Use when someone is trying to determine if they have PMF, measuring user engagement and retention, running the Sean Ellis survey, or figuring out if they should scale or keep iterating.

Why use Measuring Product Market Fit on TypingMind?

Because you install it once and use it with any model. Measuring Product Market Fit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Measuring Product Market Fit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfeat/top-pm-skills/tree/main/参考skill/lenny-skills-main%20(2)/lenny-skills-main/skills/measuring-product-market-fit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Measuring Product Market Fit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Measuring Product Market Fit?

As many as you like. As long as a model supports skills, you can use Measuring Product Market Fit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Measuring Product Market Fit AI skill free?

Yes. It is published on GitHub by cnfeat under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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