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Jtbd Analysis

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assimovt
jtbd-analysis

Analyze customer motivations using Jobs-to-be-Done and Forces of Progress. Use when asked about jobs-to-be-done, why customers switch products, what job a product is hired for, or when analyzing the forces that drive or resist product adoption.

Overview

Publisherassimovt
Repositoryproductskills
Skill namejtbd-analysis
Stars
71
Forks
6
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 assimovt on GitHub. Read the source before you install it.

Installation

Install the Jtbd 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/assimovt/productskills/tree/main/skills/jtbd-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/assimovt/productskills.git /tmp/productskills
mkdir -p .claude/skills
cp -r /tmp/productskills/skills/jtbd-analysis .claude/skills/jtbd-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jtbd 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 Jtbd 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 Jtbd 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.

Understand why customers "hire" and "fire" products. JTBD (Christensen/Moesta) reframes competition: you're not competing against similar products, you're competing against whatever the customer currently uses to get the job done — including doing nothing.

Job Statement Format

Structure every job as: "When [situation], I want to [motivation], so I can [expected outcome]."

  • "When I finish a customer call, I want to capture key insights quickly, so I can share them with my team before I forget the context."
  • NOT: "Users want better note-taking." (Too vague, no situation, no outcome.)

The situation is critical — the same person has different jobs in different contexts.

Forces of Progress

Four forces determine whether someone switches to your product. Map all four:

DRIVING ADOPTION                    RESISTING ADOPTION
================                    ==================
Push: Pain with current solution    Anxiety: Fear of the new solution
Pull: Attraction of new solution    Habit: Comfort with current way

Push (away from current solution)

What's broken or frustrating about how they do it today? Stronger push = more urgency.

  • "I spend 3 hours every week manually copying data between tools."

Pull (toward your solution)

What's attractive about the new way? This is your value proposition.

  • "Automatic sync means I never copy data again."

Anxiety (about the new solution)

What are they afraid of? Switching costs, learning curves, data migration, reliability.

  • "What if the sync breaks and I lose data?"

Habit (of the current solution)

What keeps them doing it the old way? Muscle memory, sunk costs, "good enough."

  • "I've built all my templates in the current tool over 2 years."

To win: Push + Pull must be stronger than Anxiety + Habit.

If anxiety is high, reduce it (free trials, migration tools, guarantees). If habit is strong, find the trigger moment when the current solution fails hard enough to break inertia.

Conducting JTBD Analysis

  1. Interview recent switchers — people who adopted your product (or a competitor) in the last 90 days. Their memory is fresh.
  2. Map the timeline: First thought → Passive looking → Active looking → Decision → Purchase → First use → Ongoing use.
  3. Identify the trigger event: What specific moment made them start looking? This is your marketing gold.
  4. Map all four forces for each switcher.
  5. Find patterns across 5+ interviews.

Guidelines

  • CRITICAL: NEVER define jobs as features. "I want to export to PDF" is a feature request. The job is "I want to share a polished document with my investors."
  • ALWAYS include the situation/context in job statements. The same person has different jobs at different times.
  • ALWAYS map all four forces — teams tend to focus on Push and Pull while ignoring Anxiety and Habit (the reasons people DON'T switch).
  • NEVER interview only happy customers. Interview people who evaluated you and chose a competitor — they reveal your weaknesses.
  • ALWAYS identify the trigger event. Without a trigger, there's no urgency to switch.

Built on Jobs-to-be-Done (Clayton Christensen) and the Forces of Progress (Bob Moesta). Skills from productskills.

Frequently asked questions

What does the Jtbd Analysis AI skill do?

Analyze customer motivations using Jobs-to-be-Done and Forces of Progress. Use when asked about jobs-to-be-done, why customers switch products, what job a product is hired for, or when analyzing the forces that drive or resist product adoption.

Why use Jtbd Analysis on TypingMind?

Because you install it once and use it with any model. Jtbd 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 Jtbd Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/assimovt/productskills/tree/main/skills/jtbd-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Jtbd 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 Jtbd Analysis?

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

Is the Jtbd Analysis AI skill free?

Yes. It is published on GitHub by assimovt 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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