Problem Framing

Organization

WellApp-ai

problem-framing

Frame problems using JTBD Job Stories, HMW questions, and persona validation

Overview

Publisher	WellApp-ai
Repository	`Well`
Skill name	`problem-framing`
Stars	342
Forks	48
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 WellApp-ai on GitHub. Read the source before you install it.

Installation

Install the Problem Framing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/WellApp-ai/Well.git /tmp/Well
mkdir -p .claude/skills
cp -r /tmp/Well/cursor-rules/skills/problem-framing .claude/skills/problem-framing

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Problem Framing 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 Problem Framing 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 Problem Framing 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.

Problem Framing Skill

Frame problems effectively using Jobs-to-be-Done, How Might We questions, and persona validation from Notion.

When to Use

At the start of DIVERGE loop (Ask mode)
When exploring a new feature or problem space
Before ideation to ensure clear problem definition

Instructions

Phase 1: Job Story Definition

Create a Job Story in this format:


When [situation/context],
I want to [motivation/action],
So I can [expected outcome/benefit].

Example:


When I'm managing multiple client workspaces,
I want to switch between them quickly,
So I can respond to urgent requests without losing context.

Phase 2: How Might We (HMW) Question

Reframe the problem as an opportunity question:


How might we [opportunity that addresses the job story]?

Guidelines:

Start broad, then narrow if needed
Avoid suggesting solutions in the question
Focus on the user's goal, not the feature

Example:


How might we help users navigate between workspaces seamlessly?

Phase 3: Persona Lookup (Notion MCP)

Fetch relevant personas from Notion database:

Search for personas database:


API-post-search with query "Personas" or "User Personas"

Query the database:


API-query-data-source with database_id from search results

Get persona details:


API-retrieve-a-page + API-get-block-children for each relevant persona

Extract these fields:

Name
Role / Job Title
Goals (what they want to achieve)
Pain Points (what frustrates them)
Context (environment, constraints)

Phase 4: Three Dimensions Check

Validate the problem addresses all three job dimensions:

Dimension	Question	Example
Functional	What task are they completing?	"Switch between workspaces"
Emotional	How do they want to feel?	"In control, not overwhelmed"
Social	How do they want to be perceived?	"Responsive, professional"

Output Format

After running this skill, output:

markdown
## Problem Framing

### Job Story
When [situation],
I want to [motivation],
So I can [outcome].

### How Might We
How might we [opportunity]?

### Relevant Personas

| Persona | Role | Goals | Pain Points |
|---------|------|-------|-------------|
| [Name] | [Role] | [Goals] | [Pain Points] |

### Three Dimensions

| Dimension | Definition |
|-----------|------------|
| Functional | [Task] |
| Emotional | [Feeling] |
| Social | [Perception] |

Invocation

Invoke manually with "use problem-framing skill" or follow Ask mode DIVERGE loop which references this skill's phases.

Related Skills

design-context - Run after problem-framing
competitor-scan - Research how others solve this problem

Frequently asked questions

What does the Problem Framing AI skill do?

Frame problems using JTBD Job Stories, HMW questions, and persona validation

Why use Problem Framing on TypingMind?

Because you install it once and use it with any model. Problem Framing 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 Problem Framing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WellApp-ai/Well/tree/main/cursor-rules/skills/problem-framing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Problem Framing?

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 Problem Framing?

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

Is the Problem Framing AI skill free?

Yes. It is published on GitHub by WellApp-ai 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.

View all

Finishing A Development Branch

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CommunityPopular

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

Receiving Code Review

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CommunityPopular

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…

Requesting Code Review

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CommunityPopular

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Systematic Debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Problem Framing Skill

When to Use

Instructions

Phase 1: Job Story Definition

Phase 2: How Might We (HMW) Question

Phase 3: Persona Lookup (Notion MCP)

Phase 4: Three Dimensions Check

Output Format

Invocation

Related Skills

Frequently asked questions

Related AI Skills

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