Sp Define Product logo

Sp Define Product

Organization
ReScienceLab
sp-define-product

Work out with the user what their product is before anything is drawn, and write it down as PRD.md at the root of their project, where the canvas shows it as a tab, ending in a short PRD whose screen inventory sp-new-ui-mock designs from. Use when a user starts a new product or project, says they have an idea, asks for a PRD, spec or product brief, or asks for screens when nobody has said who they are for or what problem they solve.

Overview

PublisherReScienceLab
Repositorysuper-prototyping
Skill namesp-define-product
Stars
91
Forks
4
Bundled files
Instructions only
LicenseApache-2.0
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 ReScienceLab on GitHub. Read the source before you install it.

Installation

Install the Sp Define Product 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/ReScienceLab/super-prototyping/tree/main/skills/sp-define-product
  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/ReScienceLab/super-prototyping.git /tmp/super-prototyping
mkdir -p .claude/skills
cp -r /tmp/super-prototyping/skills/sp-define-product .claude/skills/sp-define-product
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sp Define Product 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 Sp Define Product 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 Sp Define Product 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.

Define the product

Before a screen, one page that says what the product is for. It is a conversation with the user, and the page is its record: PRD.md at the root of their project, beside canvases/. The canvas shows it as a tab before the project's canvases and updates it when it is rewritten, so the user watches it fill in. The user can edit it on that tab too, so read the file again before rewriting it.

It is optional. Offer it when someone starts from an idea; never make it a gate. A user who wants screens now gets screens, and the PRD can come later.


1. Interview, do not fill a template

The user knows things you do not, and a template filled from a one-line idea is invented content that reads like decisions.

  • Ask one or two questions at a time, in the user's language. Offer lettered options (A. B. C. and "or tell me") so a reply can be one letter, and say which you would pick and why.
  • Problem before solution. Get these, in this order, before any feature:
    1. Who, specifically, has the problem. One person you could picture, not "users".
    2. What they do today instead, and what it costs them. A real moment beats a statistic.
    3. Why now, or why this person is the one to build it.
  • Then the solution: the one thing it must do well, and what it will not do.
  • Then success: how anyone would know it worked, as something observable.
  • Push back once on a vague answer ("everyone", "it's faster", "an AI that...") with a sharper question. Do not push back twice; write TBD.
  • Stop when every section in §2 is 🟢 or explicitly 🔴 TBD. For most ideas that is five to eight exchanges. Do not drag it out to be thorough.

Write the file after the first answers, not at the end. Update it as each answer lands, so the tab is the conversation's running state.


2. What PRD.md holds

Keep it to one screen of reading.

markdown
# <Product name>

> One sentence: who it is for, what it lets them do, and why that matters.

## Problem
The person, the moment, what they do today, what it costs them.

## Users
Primary user, and anyone secondary. What each one is trying to get done.

## Value
*Why this over what they do now, and the one thing it must do well.*

## Scope
**In:** the few things the first version does.

**Out:** what it deliberately does not, and why.

## Success
*Observable signs it worked: a behaviour, a number, a thing a user says.*

## Screens
| Screen | Purpose | States |
|---|---|---|
| Home | ... | empty, loaded, error |

The flows between them, one line each.

## Open questions
- ... (TBD, who decides)

## Riskiest assumptions
1. ... and the cheapest way to test it.

Keep a status line per section while it is being written (🟢 settled, 🟡 partial, 🔴 TBD) and drop the markers once it is settled. Never fill a 🔴 with a plausible guess: a TBD is honest and tells the user what is left to decide, a guess gets built.

The Screens table is what sp-new-ui-mock designs from. Each row is a board sp-new-ui-mock can make, and States are the unhappy states it must design too: empty, loading, error, the longest plausible content.


3. Close it out

  1. Red-team it. Name the assumptions that would sink the product if wrong, and the cheapest test of each. Write them under Riskiest assumptions. Keep it short and specific.
  2. Read the one-sentence summary back to the user and ask if it is right.
  3. Offer the next step: mock the first screen from the Screens table with sp-new-ui-mock, or clone a reference app with sp-clone-prototype when the user named one as "like X".

Revisiting it

When a design decision on the canvas changes scope, a screen or an answer, update PRD.md in the same turn and say what changed. When the user asks for screens and a PRD.md exists, read it first: its users, copy and states ground the mock.

Frequently asked questions

What does the Sp Define Product AI skill do?

Work out with the user what their product is before anything is drawn, and write it down as PRD.md at the root of their project, where the canvas shows it as a tab, ending in a short PRD whose screen inventory sp-new-ui-mock designs from. Use when a user starts a new product or project, says they have an idea, asks for a PRD, spec or product brief, or asks for screens when nobody has said who they are for or what problem they solve.

Why use Sp Define Product on TypingMind?

Because you install it once and use it with any model. Sp Define Product 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 Sp Define Product in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ReScienceLab/super-prototyping/tree/main/skills/sp-define-product. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sp Define Product?

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 Sp Define Product?

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

Is the Sp Define Product AI skill free?

Yes. It is published on GitHub by ReScienceLab under the Apache-2.0 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

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇