Normalize logo

Normalize

CommunityPopular
fengshao1227
normalize

Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.

Overview

Publisherfengshao1227
Repositoryccg-workflow
Skill namenormalize
Stars
5.9K
Forks
446
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by fengshao1227 on GitHub. Read the source before you install it.

Installation

Install the Normalize 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/fengshao1227/ccg-workflow.git /tmp/ccg-workflow
mkdir -p .claude/skills
cp -r /tmp/ccg-workflow/templates/skills/impeccable/normalize .claude/skills/normalize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Normalize 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 Normalize 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 Normalize 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.

Analyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.

MANDATORY PREPARATION

Invoke /frontend-design — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.


Plan

Before making changes, deeply understand the context:

  1. Discover the design system: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for "design system", "ui guide", "style guide", etc.). Study it thoroughly until you understand:

    • Core design principles and aesthetic direction
    • Target audience and personas
    • Component patterns and conventions
    • Design tokens (colors, typography, spacing)

    CRITICAL: If something isn't clear, ask. Don't guess at design system principles.

  2. Analyze the current feature: Assess what works and what doesn't:

    • Where does it deviate from design system patterns?
    • Which inconsistencies are cosmetic vs. functional?
    • What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?
  3. Create a normalization plan: Define specific changes that will align the feature with the design system:

    • Which components can be replaced with design system equivalents?
    • Which styles need to use design tokens instead of hard-coded values?
    • How can UX patterns match established user flows?

    IMPORTANT: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.

Execute

Systematically address all inconsistencies across these dimensions:

  • Typography: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.
  • Color & Theme: Apply design system color tokens. Remove one-off color choices that break the palette.
  • Spacing & Layout: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.
  • Components: Replace custom implementations with design system components. Ensure props and variants match established patterns.
  • Motion & Interaction: Match animation timing, easing, and interaction patterns to other features.
  • Responsive Behavior: Ensure breakpoints and responsive patterns align with design system standards.
  • Accessibility: Verify contrast ratios, focus states, ARIA labels match design system requirements.
  • Progressive Disclosure: Match information hierarchy and complexity management to established patterns.

NEVER:

  • Create new one-off components when design system equivalents exist
  • Hard-code values that should use design tokens
  • Introduce new patterns that diverge from the design system
  • Compromise accessibility for visual consistency

This is not an exhaustive list—apply judgment to identify all areas needing normalization.

Clean Up

After normalization, ensure code quality:

  • Consolidate reusable components: If you created new components that should be shared, move them to the design system or shared UI component path.
  • Remove orphaned code: Delete unused implementations, styles, or files made obsolete by normalization.
  • Verify quality: Lint, type-check, and test according to repository guidelines. Ensure normalization didn't introduce regressions.
  • Ensure DRYness: Look for duplication introduced during refactoring and consolidate.

Remember: You are a brilliant frontend designer with impeccable taste, equally strong in UX and UI. Your attention to detail and eye for end-to-end user experience is world class. Execute with precision and thoroughness.

Frequently asked questions

What does the Normalize AI skill do?

Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.

Why use Normalize on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fengshao1227/ccg-workflow/tree/main/templates/skills/impeccable/normalize. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Normalize?

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 Normalize?

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

Is the Normalize AI skill free?

Yes. It is published on GitHub by fengshao1227 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

Set up your own AI workspace now

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