Sp New Ui Mock logo

Sp New Ui Mock

Organization
ReScienceLab
sp-new-ui-mock

Design a new screen, flow or component as a self-contained HTML artboard on the prototype canvas, built from the board's existing design tokens rather than invented values. Covers picking or extending the token block, generating a row of screens from one script, iterating against annotated screenshots, and verifying by rendering. Use when asked to mock up a new screen or feature, design variants/proposals to compare, extend an existing board with more states, or turn a spec into artboards.

Overview

PublisherReScienceLab
Repositorysuper-prototyping
Skill namesp-new-ui-mock
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 New Ui Mock 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-new-ui-mock
  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-new-ui-mock .claude/skills/sp-new-ui-mock
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sp New Ui Mock 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 New Ui Mock 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 New Ui Mock 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.

New UI mock

For work with no reference screenshot to copy. If there is one, use sp-clone-prototype instead. Measurement beats invention every time.

Everything renders on the canvas from canvases/<slug>/; see sp-canvas for running it, and its references/layout.md for the folder and layout.json rules.

The app ships the template folder, and sp root prints where. sp, refkit or artgen not found? Run sh <sp-canvas skill dir>/scripts/install.sh (Windows: install.ps1) and follow that skill's references/install.md. A [super-prototyping:notice] line on their stderr carries its own rule: finish the step, then do what it says.

bash
KIT="$(sp root)"
mkdir -p canvases                       # first board in a project
B=canvases/<slug>
cp -r "$KIT/canvases/templates" "$B"
python3 "$B/gen.py"

Everything a run derives goes under $B/scratch/: shots, montages, candidate boards. A -o without a directory writes into whatever the current directory happens to be, which is the user's project root as often as not. The template's parked reference is ref-01-screen.html, and that stays out of git too. Once per project:

bash
for p in 'ref-*.html' '**/assets/refs/' 'scratch/'; do
  grep -qxF "$p" .gitignore 2>/dev/null || echo "$p" >> .gitignore
done

1. Find the tokens before you design

Never invent a palette when the product already has one.

  • Extending an existing board? Reuse its 00-design-tokens.html block verbatim. Copy it byte-identically into the new file; a sandboxed iframe has no shared stylesheet.
  • Cloning a real app's look? Stop and run sp-clone-prototype Phase 1 and Phase 2 first; come back with a measured token block.
  • Genuinely new product, nothing to measure? Copy the shipped template folder, change NAME and the prefix, and pick deliberately: a platform-native stack, a neutral ramp, one accent, one danger. Keep the evidence table and write why in it ("iOS system blue", "brand hex from the logo"). An unexplained hex is a future bug.

A new token is a decision, not a convenience. If a screen needs a colour or a size that is not in the block, either it belongs in the block (add it there, in every file) or the screen is wrong.


2. Ground the content

Readers take the copy, numbers and states in a mockup as product decisions.

  • Read the project's PRD.md first when it has one (the sp-define-product skill writes it). Its users, scope and Screens table, with the states of each screen, are the brief.
  • Take strings from the real source when it exists: localization files, existing screens, the spec. Never from imagination.
  • Use real assets over hand-drawn approximations: the actual icon, the actual logo, the actual empty-state illustration, embedded as a data: URI.
  • Design the unhappy states too: empty, loading, error, long string, longest plausible number. A mock that only shows the happy path hides exactly the layout problems worth finding now.

3. One generator, one row of screens

Same rule as cloning. Write one script that emits every .html in the folder, and edit the script, never the output.

python
TOKENS = ":root{...}"                      # one source of truth
def page(title, extra_css, body): ...      # TOKENS + frame + body

Constraints (from the canvas renderer):

  • Fully self-contained. sandbox="" means no external CSS, JS, fonts or images; data: URIs and inline SVG only.
  • Artboard box is 478 × 980. Overflow clips silently.
  • iPhone frame 393 × 852 pt at 1pt = 1px: 54px status bar, 125 × 36 Dynamic Island, 139 × 5 home indicator.
  • Avoid SF Symbols private-use glyphs; they render as tofu without SF Pro.
  • Accessibility is not a mockup detail to skip: real contrast on real backgrounds, ≥ 44pt tap targets, aria-label on icon-only controls. A mock that fails contrast ships a screen that fails contrast.

Proposals to compare

When the ask is "show me some options", make each proposal a whole artboard in one row, not a fragment:

json
{ "title": "Card layout proposals", "numbered": true,
  "files": [
    { "file": "10-cards-continuity", "label": "Continuity" },
    { "file": "11-cards-structured", "label": "Structured" },
    { "file": "12-cards-reference",  "label": "Reference" }
  ] }

Three is usually the right number. Two reads as a false binary, five as indecision. Make them genuinely different approaches, not three spacing values, and put a one-line rationale at the bottom of each board. Rows align column-for-column, so a second row of the same three under a different state reads as a matrix.


4. Iterate against annotated screenshots

For review, the user marks up a screenshot of the canvas with boxes, arrows and numbers, then pastes it back.

  1. Echo what you read each annotation as, before touching anything.
  2. Change the generator, not the artboard.
  3. Re-run the generator; the canvas swaps in the new board.
  4. Verify that region visually before claiming it is done.

Answer every annotation, including the ones you disagree with. Say so in a line and make the change, or say why you did not and what you did instead.


5. Verify by rendering

bash
refkit shoot "$B"/*.html -o "$B/scratch/shots" --scale 2
refkit montage "$B"/scratch/shots/*.png -o "$B/scratch/board.png" --height 520

Read the montage. Check, in order: nothing clipped; text wraps where you intended; the same element has the same inset on every screen; nothing uses a colour that is not in the token block (grep -o '#[0-9A-Fa-f]\{6\}' on the generated files and compare the set against :root).

Re-render after every pass. A change you have not re-rendered is not done.

Frequently asked questions

What does the Sp New Ui Mock AI skill do?

Design a new screen, flow or component as a self-contained HTML artboard on the prototype canvas, built from the board's existing design tokens rather than invented values. Covers picking or extending the token block, generating a row of screens from one script, iterating against annotated screenshots, and verifying by rendering. Use when asked to mock up a new screen or feature, design variants/proposals to compare, extend an existing board with more states, or turn a spec into artboards.

Why use Sp New Ui Mock on TypingMind?

Because you install it once and use it with any model. Sp New Ui Mock 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 New Ui Mock in TypingMind?

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

Which AI models can use Sp New Ui Mock?

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 New Ui Mock?

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

Is the Sp New Ui Mock 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.

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