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Design Style Picker

CommunityPopular
daymade
design-style-picker

Batch-generate and compare visual design directions so a user can choose the style they actually want. Use when the user says they cannot describe an abstract visual style, asks for many style options, wants to choose from generated UI/design-system images, rejects outputs as too colorful/too dead/too generic, or needs an existing UI/design system evolved without discarding current assets.

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill namedesign-style-picker
Stars
1.4K
Forks
219
Bundled files
2
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.

  • 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 daymade on GitHub. Read the source before you install it.

Installation

Install the Design Style Picker 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.
  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/daymade/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/daymade-codex/design-style-picker .claude/skills/design-style-picker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Style Picker 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 Design Style Picker 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 Design Style Picker 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.

Design Style Picker

Purpose

Use this skill to turn vague taste into concrete visual choices. The goal is not to guess one final design; it is to generate a structured set of options that exposes the user's taste boundary quickly.

Core Rule

Do not ask the user to describe an abstract style if they already said they cannot. Generate comparable visual evidence, let them pick, then implement from the selected references.

Workflow

  1. Restate The Real Target

    • Say what the user is actually choosing: design-system style, business app surface, landing page, deck, component library, etc.
    • Separate the primary artifact from validation samples. If the task is a design system, business screens are optional validation samples, not the main deliverable.
    • Preserve any existing UI, assets, tokens, layout, brand cues, and domain context unless the user explicitly asks to discard them.
  2. Collect Existing Assets First

    • Inspect the current rendered UI or screenshots.
    • Read design tokens, CSS variables, component names, key images, brand/domain references, and existing screenshots.
    • Treat current assets as the starting vocabulary. Do not generate unrelated "fresh" concepts over them.
  3. Generate A Matrix, Not Minor Variants

    • Use at least two axes when taste is unclear:
      • Vertical ladder: one dimension changes by large steps, such as color intensity 20/35/50/65/80.
      • Horizontal directions: different organization strategies, such as data-driven color, brand spine, warm product imagery, scenario modules, or governance-led layout.
    • Make options visibly different. If two images look like siblings, regenerate one with a clearer contrast.
    • Prefer batch generation. The user is waiting for selection, not watching one slow image at a time.
  4. Use Color As A System

    • "Less colorful" does not mean black-and-white. It usually means fewer competing focal points.
    • Keep the product palette alive, but assign color roles:
      • Broad zones and section bands for architecture.
      • Data visualization and evidence systems for multi-color semantics.
      • Brand/risk colors for rare, high-signal emphasis.
      • Neutral components for routine UI.
    • Include explicit upper-bound samples when the user is tuning color: safe, middle, high, and overload boundary.
  5. Review Before Presenting

    • Open generated images yourself.
    • Mark which are likely too dead, too colorful, too generic, too business-system-like, or closest to the target.
    • Present file paths and a short decision note for each useful candidate.
  6. Implement From Selected Images

    • Extract principles, not pixels: color roles, layout density, focal hierarchy, component treatment, image use, governance/data placement.
    • Fuse selected references explicitly. Example: "Use H02 for color placement and V04 for palette intensity."
    • Keep implementation scoped to the existing UI unless the user asks for a new artifact.
    • Run rendered visual QA after implementation.

Prompt Pattern

When generating images, include:

text
This is an evolution of the existing UI/design system, not a replacement.
Preserve these assets: <tokens, imagery, sections, components, brand cues>.
Axis: <vertical ladder or horizontal direction>.
Variant name: <clear label>.
Color/visual rule: <specific budget or organization method>.
Primary focal point: <one thing>.
Avoid: <known rejected styles from the user>.

Lessons To Preserve

  • A user saying "not colorful" may mean "no dozens of equal-weight small color chips", not "remove all color".
  • A user saying "more weight" may mean visual authority and hierarchy, not dark-mode control room.
  • For design-system work, do not replace the system with a business dashboard. Business screens can validate style, but should not become the answer.
  • Always create deliberate boundary samples. They make "too much" visible and speed up selection.
  • After selection, fuse the chosen references and name what each contributes.

References

  • Read references/selection-playbook.md when running a full style-selection session or when the user gives taste corrections during image exploration.

Bundled files

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

Frequently asked questions

What does the Design Style Picker AI skill do?

Batch-generate and compare visual design directions so a user can choose the style they actually want. Use when the user says they cannot describe an abstract visual style, asks for many style options, wants to choose from generated UI/design-system images, rejects outputs as too colorful/too dead/too generic, or needs an existing UI/design system evolved without discarding current assets.

Why use Design Style Picker on TypingMind?

Because you install it once and use it with any model. Design Style Picker 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 Design Style Picker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/daymade-codex/design-style-picker. 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 Design Style Picker?

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 Design Style Picker?

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

Is the Design Style Picker AI skill free?

Yes. It is published on GitHub by daymade 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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