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Product Market Fit Analysis

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manojbajaj95
product-market-fit-analysis

Expert framework for assessing and achieving product-market fit. Combines PMF measurement methodologies, Sean Ellis survey, retention analysis, segment-specific PMF, and post-PMF scaling strategy. Use when determining if product has PMF, measuring user engagement, running PMF surveys, or deciding whether to scale or keep iterating.

Overview

Publishermanojbajaj95
Repositoryclaude-gtm-plugin
Skill nameproduct-market-fit-analysis
Stars
109
Forks
34
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by manojbajaj95 on GitHub. Read the source before you install it.

Installation

Install the Product Market Fit Analysis 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/manojbajaj95/claude-gtm-plugin/tree/main/skills/product-market-fit-analysis
  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/manojbajaj95/claude-gtm-plugin.git /tmp/claude-gtm-plugin
mkdir -p .claude/skills
cp -r /tmp/claude-gtm-plugin/skills/product-market-fit-analysis .claude/skills/product-market-fit-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Market Fit Analysis 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 Product Market Fit Analysis 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 Product Market Fit Analysis 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.

Product Market Fit Analysis

Workspace Context

Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.

Operating Contract

This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.

Comprehensive framework for assessing, achieving, and scaling product-market fit.

Quick Reference

SituationUse This Skill For
Measuring PMFSean Ellis Survey
Retention analysisRetention Curves
PMF validationLeading Indicators
Segment-specific PMFSegment Analysis
Scaling decisionsPost-PMF Strategy

Part 1: What Is PMF?

Definition

Product-market fit is the condition where a product satisfies a strong market demand. It's not binary — it's a spectrum.

Key Insight

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."

PMF Levels

LevelCustomersFocus
Nascent3-5Satisfaction
Developing5-25Demand
Strong25-100Efficiency
Extreme100+Scaling

Part 2: PMF Measurement Frameworks

Sean Ellis "Disappointment" Survey

The Question:

"How would you feel if you could no longer use this product?"

  • Very disappointed
  • Somewhat disappointed
  • Not disappointed

The Benchmark:

40% "very disappointed" = on the right track

Focus on the "very disappointed" segment as the core value indicator.

Retention Curves

Uri Levine's Definition:

"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."

What to look for:

  • Curves that flatten over time (not decaying to zero)
  • "Smile curve" — engagement increases over time (strongest signal)

Key retention points:

  • Day 7 retention
  • Day 30 retention
  • Day 90 retention

Reference Customers

Christian Idiodi:

"The holy grail is really a reference customer - somebody who loves it enough to tell people about it."

MarketTarget References
B2B6-8 reference customers
B2C15-25 reference customers

Part 3: Leading Indicators

Signs of True PMF

IndicatorWhat It Means
"Very disappointed" > 40%Strong core value
Retention curve flatteningProduct creates ongoing value
Customer "pull"Market is pulling product
Outrage during outagesProduct is mission-critical
Customer driving next stepsIntent, not polite interest

Customer "Pull" Signals

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
  • Questions about pricing/timelines
  • Urgency in communication

Outage Response Test

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.


Part 4: Segment-Specific PMF

Key Insight

Karri Saarinen: "The way we think about it is, 'Do we have the fit in specific segments?' and how strong that fit is."

PMF exists in segments, not universally.

Finding Your Segment

Start narrow, then expand:

  1. Find PMF in one segment (e.g., early-stage startups)
  2. Double down where you see natural pull
  3. Expand to adjacent segments

Common Segments

  • Company size / stage
  • Industry vertical
  • Use case / workflow
  • Geography
  • Team size

Part 5: Distribution + PMF

Critical Insight

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:

  1. ✅ Retaining product
  2. ✅ Scalable, built-in distribution

Without both, you don't have true PMF.


Part 6: Pre-PMF Navigation

What to Do Pre-PMF

  • Focus on retention, not acquisition
  • Talk to every customer personally
  • Iterate rapidly on product
  • Find the segment with strongest pull
  • Don't spend on paid acquisition

Warning Signs

  • Growth comes from "launch spikes" not organic
  • Users aren't coming back
  • No customers willing to be references
  • Market is pushing product, not pulling
  • You're guessing why users churn

Part 7: Post-PMF Scaling

Protecting PMF

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."

Scaling Checklist

  • Retention curve has flattened (positive)
  • Have target reference customers
  • Clear segment with strongest fit
  • Built-in distribution mechanism
  • Team structure supports growth

PMF Can Be Lost

Markets shift, competitors improve, user expectations rise. Continuously monitor and protect PMF.


Part 8: Questions to Assess PMF

Diagnostic Questions

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

Part 9: Common Mistakes

What to Avoid

MistakeReality
Confusing launch spikes with PMFSustained organic growth matters
Ignoring retention dataIf they don't come back, no PMF
Scaling too earlyPaid growth before PMF burns cash
Conflating TAM with PMFLarge market ≠ fit within it
Listening to "somewhat disappointed"Focus on "very disappointed"

Part 10: Decision Framework

Should You Scale?

SignalAction
40%+ "very disappointed" + flattening retentionReady to scale
< 40% "very disappointed"Keep iterating
No reference customersBuild them first
No distribution mechanismFind channels first

Scale vs. Iterate Decision

FactorScaleKeep Iterating
PMF survey> 40% very disappointed< 40%
RetentionCurve flatteningDecaying to zero
ReferencesTarget achievedNot yet
DistributionChannels identifiedUnknown

Related Skills

  • growth-strategy: For growth frameworks post-PMF
  • customer-success-and-retention: For retention improvement
  • conversion-rate-optimization: For activation optimization

Frequently asked questions

What does the Product Market Fit Analysis AI skill do?

Expert framework for assessing and achieving product-market fit. Combines PMF measurement methodologies, Sean Ellis survey, retention analysis, segment-specific PMF, and post-PMF scaling strategy. Use when determining if product has PMF, measuring user engagement, running PMF surveys, or deciding whether to scale or keep iterating.

Why use Product Market Fit Analysis on TypingMind?

Because you install it once and use it with any model. Product Market Fit Analysis 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 Product Market Fit Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/manojbajaj95/claude-gtm-plugin/tree/main/skills/product-market-fit-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Product Market Fit Analysis?

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 Product Market Fit Analysis?

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

Is the Product Market Fit Analysis AI skill free?

Yes. It is published on GitHub by manojbajaj95 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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