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Design System Evolution

Community
kemiljk
design-system-evolution

Decide how an existing design system should evolve: reuse a component, add a variant, change its API, or introduce a new pattern. Use when real design or code consumers expose a mismatch and a bounded compatibility or migration decision is needed.

Overview

Publisherkemiljk
Repositoryskills
Skill namedesign-system-evolution
Stars
64
Forks
3
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 kemiljk on GitHub. Read the source before you install it.

Installation

Install the Design System Evolution 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/kemiljk/skills/tree/main/skills/design-system-evolution
  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/kemiljk/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/design-system-evolution .claude/skills/design-system-evolution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design System Evolution 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 System Evolution 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 System Evolution 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 System Evolution

Evolve a system through representative use. The goal is a clearer shared model that supports real consumers, with an understandable cost of adoption.

Establish the pressure to change

Inspect the named pattern, existing component and token definitions, documentation, and representative consumers. Include contrasting uses when they reveal different content, behaviour, platform, or density requirements. Record what is shared, what differs, and which differences are intentional. State the sample; do not imply every consumer was inspected.

Identify the problem before proposing consolidation: repeated workarounds, an API that cannot express a valid use, inconsistent meanings, or a new product requirement. Visual similarity alone is insufficient evidence that two things should share a component. A one-off layout difference may belong in composition rather than a new system primitive.

Preserve working conventions and unrelated components. A proposal request produces a proposal. Implement and migrate only the authorised scope; do not turn a screen correction into a library rewrite.

Choose the smallest useful system change

OptionPrefer whenCheck before choosing
Reuse as-isThe existing contract supports the taskIs the apparent mismatch actually content or misuse?
Compose existing piecesThe arrangement varies, but the parts retain their meaningsCan the layout live outside the shared primitive?
Add a property or variantA meaningful, coherent difference recurs or meets a concrete new requirementAre allowed combinations understandable and valid?
Change the shared contractThe current model obstructs supported use across consumersWhich defaults, behaviours, and consumers would change?
Add a distinct patternResponsibility or interaction differs materiallyIs a separate name clearer than expanding the old abstraction?

Do not use a fixed occurrence count as permission to abstract. One consequential requirement can justify a system change; many similar screenshots can still conceal different responsibilities.

Prefer properties that express intent over a collection of styling switches. Avoid flags that allow nonsensical combinations or expose implementation details to every consumer. Allow purposeful variation rather than forcing all cards, forms, or dialogs into one visual template.

Make the compatibility decision explicit

Map the design model to its code counterpart when both are available: variant meanings to properties, variables to semantic tokens, composition to component boundaries, and states to behaviour. The structures need not be identical; their promises should agree.

For each consequential change, account for default behaviour, existing content, interaction and accessibility contracts, themes, and platform-specific uses where affected. Distinguish source compatibility from unchanged visual or runtime behaviour. A retained property name does not guarantee the same result.

Keep migration proportional. Identify consumers to update, the old-to-new mapping, any temporary compatibility path, and the condition for removing it. Do not add indefinite aliases or deprecations when an atomic change to a small internal system is clearer. Do not promise atomic migration of consumers outside the available scope.

Example: project cards

This is a hypothetical decision. Three cards share a title, summary, and destination, but differ in metadata placement. First investigate a common content structure with composable metadata. A selectable card that supports bulk actions may need a different interaction contract from a navigation card. Do not merge them solely because both have a thumbnail and border.

Verify and deliver

Check representative consumers before and after the change, including a contrasting variant, realistic content, and relevant layout sizes. Exercise affected input, focus, and state behaviour in a working implementation. Check design instances and overrides when editing a library. Run meaningful compatibility or behaviour checks for changed code contracts.

Match claims to evidence: source inspection establishes API usage, a frame establishes design structure, and a running sequence establishes observed behaviour. Report unavailable consumers and checks. For any reported bug, supply the concrete consumer use case, prerequisites and reproduction steps, expected and actual behaviour, and impact; label unexecuted concerns as hypotheses.

Deliver the decision, rationale, affected consumers, compatibility implications, migration plan or completed scoped migration, and verification. Success is a system that serves its consumers more clearly, not merely fewer components or a more uniform screenshot.

Frequently asked questions

What does the Design System Evolution AI skill do?

Decide how an existing design system should evolve: reuse a component, add a variant, change its API, or introduce a new pattern. Use when real design or code consumers expose a mismatch and a bounded compatibility or migration decision is needed.

Why use Design System Evolution on TypingMind?

Because you install it once and use it with any model. Design System Evolution 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 System Evolution in TypingMind?

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

Which AI models can use Design System Evolution?

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 System Evolution?

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

Is the Design System Evolution AI skill free?

Yes. It is published on GitHub by kemiljk 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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