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Measuring Pmf

OrganizationPopular
RefoundAI
measuring-pmf

Help users objectively assess where their product stands on the PMF spectrum by triangulating qualitative feedback, quantitative retention benchmarks, and organic growth signals.

Overview

PublisherRefoundAI
Repositorylenny-skills
Skill namemeasuring-pmf
Stars
1.3K
Forks
170
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Measuring Pmf 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.
  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.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git /tmp/lenny-skills
mkdir -p .claude/skills
cp -r /tmp/lenny-skills/skills/measuring-pmf .claude/skills/measuring-pmf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Measuring Pmf 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 Pmf 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 Pmf 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

Transition from pushing your product to feeling the market pull it out of you.

Help the user with measuring product-market fit using insights from 16 guests and posts across Lenny's Podcast and Newsletter.

How to Help

  1. Diagnose the current stage - Determine where the user is on the journey from pre-product validation to scalable growth.
  2. Apply quantitative benchmarks - Evaluate cohort retention and Sean Ellis scores against industry standards.
  3. Analyze market pull - Identify signals of organic demand versus founder-led momentum.
  4. Iterate based on feedback - Help prioritize roadmap changes that specifically drive toward retention for the most passionate users.

Core Principles

Standardize Quantitative Validation

Jag Duggal: "We rarely scale a project, a product we've launched, until we know the Sean Ellis score and we know that it's hit a threshold that we find really compelling."

Use an objective threshold like the Sean Ellis score to validate fit before scaling. Set a strict target of at least 40 percent of users feeling very disappointed if the product disappeared.

Look for Urgent Feedback

Raaz Herzberg: "We really felt the type of questions change, right? Silly. The call sounded like, again, "How are you pricing this, or when can we start doing a POV?" I think naturally, as human beings, you have a bias to look for affirmation, versus a bias for what you don't want to hear."

True fit is revealed when customer feedback shifts from polite interest to urgent, practical questions about pricing and implementation. Treat generic interest as a negative signal.

Listen Selectively

Rahul Vohra: "You have to deliberately not act on the feedback of many of your early users, and this is at the same time as listening to people intensely and building what people want."

Ignore feedback from most users to focus exclusively on the segment that would be very disappointed without your product. Double down on what that specific cohort loves.

Prioritize Retention Above All

From "How to kickstart and scale a consumer business—Step 5: RETAIN: Iterate until enough people stick around": "Do whatever is required to get to product-market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital—whatever is required."

Iterating toward fit requires an obsessive focus on retention signals. Be prepared to rewrite the product or switch markets if your cohort retention curves do not eventually flatten.

Differentiate Organic Pull

Grant Lee: "And then we'd look at signups, and you'd get that initial spike in signups, and then they sort of flatten out. We were still getting new users every day, but it was clear we didn't have strong word of mouth. There wasn't strong organic virality."

Measure the delta between temporary launch spikes and your long-term organic baseline. True fit is characterized by sustained word-of-mouth growth rather than marketing-driven peaks.

Acknowledge Macro Shifts

Adam Grenier: "Start by assuming you no longer have product market fit, because you had product market fit in a different market. It's a different market now, so you have to start over."

Assume previous product-market fit is lost when entering a significantly changed macroeconomic environment. Shifts in the market can fundamentally alter your customer base and their needs.

Sequence Your Optimization

Todd Jackson: "We've published dozens of articles on the First Round Review, and we have found a very consistent set of patterns, demand satisfaction, and efficiency. But the interesting thing is that you don't go for all three of them from the very beginning."

Finding fit is a sequential journey that starts with demand, moves to satisfaction, and finally focuses on efficiency. Do not try to optimize for efficiency until you have proven satisfaction.

Templates & Frameworks

  • PMF Signal Hierarchy (Pre-Product and Post-Product) (How to know if you've got product-market fit) - A structured taxonomy of product-market fit signals organized into pre-product (2 signals) and post-product (7 signals), sorted from most concrete/measurable to
  • PMF Signal Checklist (A guide for finding product-market fit in B2B) - A list of signals that indicate you're approaching or have achieved product-market fit, compiled from founder interviews.
  • PMF Survey (100+ Users) (What to ask your users about Product-Market Fit) - A three-question survey template for assessing product-market fit when you have 100+ active users
  • PMF Level Benchmarks Dashboard (Todd Jackson) - Quantitative benchmarks for each of the four PMF levels across key metrics
  • Cohort Retention Curve Analysis for PMF (How to know if you've got product-market fit) - A method for determining product-market fit by plotting cohort retention over time and checking if the curve flattens rather than declining to zero.
  • PMF is a Spectrum, Not Binary (A guide for finding product-market fit in B2B) - The mental model that product-market fit is never a yes/no state but a continuous process of finding fit with larger market segments.
  • Five PMF Journey Archetypes (How to kickstart and scale a consumer business—Step 5: RETAIN: Iterate until enough people stick around) - Five patterns (plus one sad one) that describe how consumer companies typically find product-market fit over time
  • 5-Step B2B Product-Market Fit Progression (A guide for finding product-market fit in B2B) - A progressive framework for moving toward product-market fit in B2B, derived from interviews with 20+ successful founders. Each step represents a deeper level o

See references/artifacts.md for the full list with details.

Questions to Help Users

  • "What percentage of your users would be very disappointed if your product stopped existing today?"
  • "Are your cohort retention curves flattening above zero percent, or do they continue to drop over time?"
  • "Is your growth currently being pushed by marketing spend or pulled by organic word-of-mouth?"
  • "When you talk to customers, are they asking polite questions or urgent questions about pricing and implementation?"
  • "How much of your current growth comes from personal relationships versus customers with no connection to the founders?"
  • "Can you identify a specific segment of users who use the product significantly more than others?"

Common Mistakes to Flag

  • Confusing support with demand - Founders often mistake personal encouragement from their network or investor confidence for actual market pull.
  • Scaling before retention flattens - Increasing headcount or marketing spend before you have a stable cohort of retained users leads to high burn and eventual failure.
  • Solving for every user - Trying to satisfy the feedback of every trial user dilutes the product for the core segment that actually finds value.
  • Ignoring the two-year timeline - Startups often give up too early, failing to realize that reaching product-market fit typically requires at least two years of iteration.

Deep Dive

For all 50 sourced insights from 16 guests, see references/guest-insights.md

Related Skills

  • Defining Product Strategy
  • Product Vision
  • Positioning
  • Pricing Strategy

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 Pmf AI skill do?

Help users objectively assess where their product stands on the PMF spectrum by triangulating qualitative feedback, quantitative retention benchmarks, and organic growth signals.

Why use Measuring Pmf on TypingMind?

Because you install it once and use it with any model. Measuring Pmf 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 Pmf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RefoundAI/lenny-skills/tree/main/skills/measuring-pmf. 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 Pmf?

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 Pmf?

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

Is the Measuring Pmf AI skill free?

Yes. It is published on GitHub by RefoundAI 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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