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Value Proposition

CommunityPopular
phuryn
value-proposition

Design a detailed value proposition using a 6-part JTBD template — Who, Why, What before, How, What after, Alternatives. Use when creating a value proposition, analyzing customer value delivery, or articulating why customers should choose your product.

Overview

Publisherphuryn
Repositorypm-skills
Skill namevalue-proposition
Stars
26.7K
Forks
2.8K
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 phuryn on GitHub. Read the source before you install it.

Installation

Install the Value Proposition 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/phuryn/pm-skills/tree/main/pm-product-strategy/skills/value-proposition
  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/phuryn/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/pm-product-strategy/skills/value-proposition .claude/skills/value-proposition
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Value Proposition 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 Value Proposition 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 Value Proposition 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.

Value Proposition

Metadata

  • Name: value-proposition
  • Description: Generate a detailed value proposition using a 6-part template with JTBD framing. Includes practical examples for designing compelling customer value.
  • Triggers: value proposition, value prop, customer value, JTBD value, value map

Instructions

You are a product strategist designing a clear value proposition for $ARGUMENTS.

Your task is to develop a comprehensive value proposition that articulates the customer value delivered by the product.

Input Requirements

  • Product description and features
  • Target customer segment and their problems
  • Competitive alternatives and current solutions
  • Customer insights or market data

Value Proposition Template

6-Part Structure

1. Who

  • Who is this value proposition for?
  • What customer segment are we addressing?
  • What are their characteristics and constraints?

2. Why (Problem)

  • What is the customer's core problem or need?
  • What's the Job to Be Done (JTBD)?
  • What desired outcomes are they trying to achieve?

3. What Before

  • What is the customer's current situation?
  • What are they using today to solve this problem?
  • What friction or pain exists in the current approach?

4. How (Solution)

  • How does the product solve the problem?
  • What specific features or capabilities deliver value?
  • Why is this solution better than alternatives?

5. What After

  • What is the improved outcome or future state?
  • How does the customer's life/work change?
  • What becomes possible that wasn't before?

6. Alternatives

  • What other solutions could customers use?
  • Why would they choose us instead?
  • What's the switching cost or friction from alternatives?

Example: Canva

  • Who: Non-designers who need to create marketing graphics
  • Why: They need professional-looking designs but can't hire designers or use complex tools
  • What Before: Using PowerPoint, Photoshop (too complex), or hiring expensive designers
  • How: Drag-and-drop templates, built-in design elements, AI design assistance, intuitive interface
  • What After: Create professional designs in minutes, launch campaigns faster, save design costs
  • Alternatives: Photoshop (complex), Fiverr (slow, expensive), Canva competitors (fewer templates, harder UX)

Output Process

  1. Identify and profile the target customer segment
  2. Define the core problem and JTBD
  3. Describe the current state and friction points
  4. Articulate how the product solves the problem
  5. Envision the improved outcome
  6. Compare against competitive alternatives
  7. Create a concise value prop statement (1-2 sentences)
  8. Develop a positioning statement for marketing use

Domain Context

This template vs Strategyzer's Value Proposition Canvas: Strategyzer's canvas (by Alexander Osterwalder) is widely used but has structural limitations. This 6-part JTBD template (by Paweł Huryn and Aatir Abdul Rauf) addresses them:

  • Customer first: This template starts with the customer (Who/Why) and works toward the solution. Strategyzer's canvas places the product on the left, which often leads teams to start with their solution rather than the customer's problem.
  • One segment at a time: This template is designed for one segment per pass. Strategyzer's canvas encourages mapping multiple products/services simultaneously, which dilutes focus.
  • Explicit alternatives: Section 6 (Alternatives) forces you to name what customers would use without you and articulate why you're better. Strategyzer's canvas has no equivalent — you don't directly confront substitutes.
  • Simpler structure: "What before → How → What after" is easier to fill out than separating Customer Jobs, Pains, and Gains on one side and Pain Relievers, Gain Creators, and Products on the other. The separation often creates confusion about where things go.
  • Actionable output: The final Value Proposition Statement is ready for marketing, sales, and onboarding. Strategyzer's canvas doesn't produce a reusable statement.

Use Strategyzer's Value Proposition Canvas when you need a detailed pains/gains decomposition for a mature product with complex customer needs. Use this 6-part template for clarity, speed, and actionable output.

Notes

  • Jobs to Be Done (JTBD) framework focuses on the progress the customer is trying to make, not demographics
  • Value propositions are segment-specific; you may have different value props for different customer groups
  • The stronger your value prop, the easier marketing, sales, and product decisions become
  • Test value props with real customers before finalizing
  • Use a Value Curve (Blue Ocean Strategy) to visually compare your offering against competitors across key factors

Templates


Further Reading

Frequently asked questions

What does the Value Proposition AI skill do?

Design a detailed value proposition using a 6-part JTBD template — Who, Why, What before, How, What after, Alternatives. Use when creating a value proposition, analyzing customer value delivery, or articulating why customers should choose your product.

Why use Value Proposition on TypingMind?

Because you install it once and use it with any model. Value Proposition 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 Value Proposition in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phuryn/pm-skills/tree/main/pm-product-strategy/skills/value-proposition. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Value Proposition?

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 Value Proposition?

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

Is the Value Proposition AI skill free?

Yes. It is published on GitHub by phuryn 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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