Product Discovery And Framing

Organization

memi-design

product-discovery-and-framing

Frame product opportunities from user, business, technical, and market evidence into testable problem statements, priorities, risks, and success criteria. Use before solution design when the team is unsure what problem to solve or why it matters.

Overview

Publisher	memi-design
Repository	`design-skills`
Skill name	`product-discovery-and-framing`
Stars	7
Forks	0
Bundled files	2
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.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by memi-design on GitHub. Read the source before you install it.

Installation

Install the Product Discovery And 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.
https://github.com/memi-design/design-skills/tree/main/skills/product-discovery-and-framing
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/memi-design/design-skills/tree/main/skills/product-discovery-and-framing

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/memi-design/design-skills.git /tmp/design-skills
mkdir -p .claude/skills
cp -r /tmp/design-skills/skills/product-discovery-and-framing .claude/skills/product-discovery-and-framing

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

Use it in TypingMind

Enable Product Discovery And 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 Product Discovery And 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 Product Discovery And 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.

Product Discovery and Framing

Turn an ambiguous request into an evidence-backed decision frame. Do not use this skill to design screens, run evaluative usability testing, or justify a predetermined feature.

Required inputs

Collect what exists without blocking on perfect research:

decision, deadline, and decision owner;
affected users and current behavior;
business outcome and constraints;
existing research, product data, support evidence, and technical facts;
assumptions already being treated as facts.

Label missing evidence instead of inventing it.

Workflow

State the decision. Write one sentence describing what must be decided and by when.
Build an evidence ledger. Separate observed evidence, stakeholder claims, assumptions, and unknowns. Attach a source and freshness date where possible.
Model the current situation. Describe the actor, context, goal, present workaround, friction, and consequence. Avoid demographic personas without behavioral evidence.
Frame the problem. Use: [Actor] struggles to [goal] when [context], causing [consequence]. Evidence: [sources].
Identify opportunities. Group needs by underlying outcome. Keep solution ideas separate until the opportunity set is visible.
Prioritize. Score opportunities using user impact, strategic value, evidence strength, urgency, effort uncertainty, and reversibility. Show the reasoning, not just a number.
Define hypotheses. Express the expected behavior change, who experiences it, why it should occur, and what evidence would disconfirm it.
Set success criteria. Choose one primary outcome, supporting indicators, guardrails, and a time horizon. Do not use output volume as the outcome.
Choose the next evidence step. Prefer the smallest study, prototype, data query, or technical spike that resolves the highest-risk uncertainty.

Output contract

Return:

decision statement;
evidence and assumptions table;
current-state problem frame;
prioritized opportunity list;
hypothesis and success measures;
constraints and non-goals;
unresolved risks;
recommended next evidence step.

Verification

Confirm that every major claim is sourced or marked as an assumption, the frame does not prescribe a solution, success measures describe user or business change, and the next step could invalidate the preferred direction. If stakeholders disagree, preserve the competing frames and name the evidence needed to choose between them.

Use information-architecture after the problem is framed around findability or structure. Use interaction-design after a workflow opportunity is selected. Use design-measurement-and-experimentation to operationalize success criteria.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB
note.json1 KB

Frequently asked questions

What does the Product Discovery And Framing AI skill do?

Why use Product Discovery And Framing on TypingMind?

Because you install it once and use it with any model. Product Discovery And 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 Product Discovery And Framing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/memi-design/design-skills/tree/main/skills/product-discovery-and-framing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Product Discovery And 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 Product Discovery And Framing?

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

Is the Product Discovery And Framing AI skill free?

Yes. It is published on GitHub by memi-design 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Product Discovery and Framing

Required inputs

Workflow

Output contract

Verification

Bundled files

Frequently asked questions

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