How to Create a Value Proposition Using the 6-Part JTBD Template

The phuryn/pm-skills repository provides a declarative AI skill that guides you through the 6-part JTBD framework via the /value-proposition slash command, generating structured markdown output ready for product documentation.

To create a value proposition using the 6-part JTBD template, product teams can leverage the pm-product-strategy plugin from the phuryn/pm-skills repository. This open-source implementation encodes the Jobs-to-be-Done framework into an AI-native workflow that ensures customer-centric positioning. The template forces explicit consideration of user context, desired outcomes, and competitive alternatives through six structured components.

Understanding the 6-Part JTBD Template Structure

The value-proposition skill in pm-product-strategy/skills/value-proposition/SKILL.md defines a rigorous six-part framework that maps directly to how customers actually evaluate products.

The Six Components

Each value proposition must answer these six questions in sequence:

1. Who – The specific user segment and their characteristics
2. Why – The underlying job to be done or motivation driving the purchase
3. What Before – The current state, pain points, and existing workarounds
4. How – Your product's unique mechanism or approach to solving the job
5. What After – The measurable outcome and transformed state after adoption
6. Alternatives – Explicit naming of competitors and substitute solutions

This structure creates narrative continuity by forcing a causal story from current pain (What Before) through your solution (How) to the improved reality (What After). Unlike traditional canvases, the Alternatives section requires explicit competitive positioning.

Implementation in the pm-skills Repository

The repository implements this template through a layered architecture that separates skill definition from execution.

Skill Definition Layer (SKILL.md)

The core logic resides in pm-product-strategy/skills/value-proposition/SKILL.md. This file contains the complete template definition, input requirements, and comparative analysis against other frameworks like Strategyzer's Value Proposition Canvas (lines 78-85). It specifies the exact markdown format for the final output, ensuring consistency across all generated propositions.

Command Wrapper Layer (value-proposition.md)

The user-facing interface is defined in pm-product-strategy/commands/value-proposition.md. This wrapper exposes the skill as a slash command (/value-proposition) that gathers context including product descriptions and uploaded documents. It formats the AI's output into the standardized 6-part structure.

Plugin Registration

The skill automatically registers in AI assistants through the main README.md entry under "Value-Proposition". This declarative approach means Claude Code, Claude Cowork, OpenCode, and compatible agents can load the workflow without custom code execution.

How to Use the Value-Proposition Command

Interactive Invocation

Trigger the workflow in any Claude-compatible chat interface:

/value-proposition

The AI agent will initiate a structured dialogue to capture necessary context:

What does the product do?
Who is the target user segment?
What alternatives do they currently use?

Generated Output Format

The skill produces a structured markdown block suitable for direct inclusion in product briefs or pitch decks. Here is sample output for an AI writing assistant targeting non-native English speakers:

## Value Proposition: AI Writing Assistant

### For non‑native English speakers who need professional‑grade copy:

1. **Who**: Language learners & small‑business owners with limited editing budgets.  
2. **Why**: They need clear, persuasive content quickly, but lack native fluency and editing resources.  
3. **What Before**: Manual editing, costly freelance writers, or generic translation tools that produce awkward phrasing.  
4. **How**: An AI‑powered editor that suggests tone‑appropriate phrasing, corrects grammar, and adapts style to the intended audience.  
5. **What After**: Users publish polished copy in minutes, improve conversion rates, and reduce reliance on external writers.  
6. **Alternatives**: Traditional copy‑editing services (expensive), generic grammar checkers (limited context).  

**Value Proposition Statement**  
*For non‑native English speakers who need professional copy, the AI Writing Assistant is an intelligent editor that delivers native‑level language and contextual tone in seconds. Unlike expensive copy‑editing services, it provides instant, affordable, and brand‑consistent output.*

Programmatic Usage

For documentation pipelines or CI/CD workflows, invoke via CLI:

# Assuming Claude CLI is installed and the plugin is added

claude ask "/value-proposition" "AI writing assistant for non‑native speakers"

The command returns the same structured markdown block, ready for inclusion in README.md files or internal wikis.

Why Choose the 6-Part JTBD Framework?

According to the phuryn/pm-skills source code, this template offers three distinct advantages over traditional positioning frameworks:

• Customer-first orientation – Starting with Who and Why anchors the proposition in actual user needs rather than product features
• Explicit alternatives – Forcing identification of competitors and workarounds eliminates the common blind spot of ignoring substitutes
• Actionable structure – The "What Before → How → What After" sequence creates a causal narrative that sales and marketing teams can communicate without translation

Summary

• The 6-part JTBD template (Who, Why, What Before, How, What After, Alternatives) provides a customer-centric framework for positioning
• The phuryn/pm-skills repository implements this as a declarative AI skill in pm-product-strategy/skills/value-proposition/SKILL.md
• Use the /value-proposition slash command to generate structured markdown output suitable for product docs and marketing collateral
• The template explicitly requires competitive analysis (Alternatives), a step often omitted in other frameworks
• Output works across Claude Code, Claude Cowork, OpenCode, and compatible AI assistants without code execution

Frequently Asked Questions

What is the difference between the 6-part JTBD template and Strategyzer's Value Proposition Canvas?

The 6-part JTBD template explicitly requires the Alternatives section and forces a causal narrative through "What Before → How → What After," while Strategyzer's canvas focuses on pain/gain mapping without mandatory competitive analysis. According to the SKILL.md file (lines 78-85), the JTBD approach is preferable when you need to communicate the transformation story rather than just feature-benefit mapping.

Can I customize the 6-part template for specific industries?

Yes. The skill definition in pm-product-strategy/skills/value-proposition/SKILL.md serves as the base template. You can fork the repository and modify the SKILL.md file to add industry-specific prompting or additional sections, then register your custom version through the plugin architecture.

Does the value-proposition command work with AI assistants other than Claude?

Yes. The repository uses a declarative plugin format that is compatible with any AI assistant supporting the phuryn/pm-skills ecosystem, including OpenCode and other Claude-compatible tooling. The command wrapper in pm-product-strategy/commands/value-proposition.md uses standard markdown specifications that translate across platforms.

How do I integrate this into my existing product documentation workflow?

Invoke the CLI command claude ask "/value-proposition" followed by your product context, then pipe the output to your documentation files. The generated markdown uses standard ATX headings that render correctly in GitHub, Notion, Confluence, and static site generators without formatting conversion.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}