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Reference Driven Design

Community
dev-ik
reference-driven-design

Build an evidence-backed visual reference brief before UI concept or polish work. Use when a product needs a modern visual direction, category-aware references, comparative design analysis, or protection from generic AI-generated styling without copying a specific brand.

Overview

Publisherdev-ik
Repositoryai-design-rules
Skill namereference-driven-design
Stars
3
Forks
0
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 dev-ik on GitHub. Read the source before you install it.

Installation

Install the Reference Driven Design 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/dev-ik/ai-design-rules/tree/main/skills/reference-driven-design
  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/dev-ik/ai-design-rules.git /tmp/ai-design-rules
mkdir -p .claude/skills
cp -r /tmp/ai-design-rules/skills/reference-driven-design .claude/skills/reference-driven-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reference Driven Design 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 Reference Driven Design 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 Reference Driven Design 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.

Reference-Driven Design Skill

Role

Translate current product and platform references into an abstract, traceable visual direction.

Mission

Improve visual judgment without turning taste, trends, or one admired product into unsupported design rules.

When To Use

  • A user asks for a more modern, polished, premium, expressive, or product-specific interface.
  • A new surface needs a visual direction before implementation.
  • Existing UI feels generic but the workflow and object model are already clear.
  • A prompt needs reference categories and explicit anti-copy constraints.

When Not To Use

  • Product direction, user goal, or object model is unresolved.
  • The task is a narrow visual bug with an existing token and component answer.
  • No source, screenshot, product behavior, or graph context is available; record a research gap first.

Inputs

  • Product goal, primary action, audience, and platform.
  • Current UI or implementation.
  • Existing components, tokens, and product design direction.
  • Focused graph context from npm run context.
  • Supplied references or traceable current product and platform evidence.

Process

  1. Resolve graph context for the target task.
  2. Classify the surface by product type, workflow density, platform, and emotional tone.
  3. Select two or three relevant reference families, not a single brand to imitate.
  4. For each source, separate observed behavior from interpretation.
  5. Extract reusable abstractions:
    • content and functional layers;
    • hierarchy and density;
    • typography and shape roles;
    • material and color meaning;
    • state and motion behavior;
    • responsive and input adaptation.
  6. Map recommendations to existing research, rules, patterns, and tokens.
  7. Mark unsupported ideas as observations or research needs instead of instructions.
  8. Define what must not be copied: branding, proprietary assets, distinctive composition, or platform-inappropriate effects.
  9. Hand the brief to visual-designer, motion-designer, or prompt-architect.

Output Format

md
## Reference Brief
- Product category:
- User goal:
- Platform and density:
- Evidence date:

## Reference Matrix
| Source | Observed behavior | Reusable abstraction | Limit |
| --- | --- | --- | --- |

## Visual Direction
- Content layer:
- Functional layer:
- Focal emphasis:
- Typography and shape:
- State and motion:
- Responsive adaptation:

## Graph Traceability
- Research:
- Rules:
- Patterns:
- Missing evidence:

## Anti-Copy Boundary
- Do not copy:
- Platform constraints:

## Handoff
- Next skill:
- Reason:

Quality Checklist

  • Recommendations come from observable source behavior.
  • At least two reference families are compared when the task is exploratory.
  • Product purpose determines expression level and density.
  • Content and primary action remain dominant.
  • Platform-specific materials have fallbacks.
  • The brief names unsupported ideas as gaps.
  • No specific brand composition or proprietary asset is copied.

Related Research

  • RESEARCH-00009 Expressive System UI In 2026
  • RESEARCH-00010 Motion As State Continuity
  • RESEARCH-00011 Contextual And Observable Agentic Interfaces

Related Rules

  • RULE-00008 / VIS-001 Semantic Tokens Only
  • RULE-00014 / VIS-002 Keep Expression Subordinate To Content
  • RULE-00015 / UX-004 Use Motion To Explain State Continuity

Handoff To Other Skills

  • Use product-designer if product direction or the primary action is unclear.
  • Use design-evidence-researcher when current evidence is missing.
  • Use visual-designer to convert the brief into hierarchy and token decisions.
  • Use motion-designer when continuity or interaction feedback needs movement.
  • Use prompt-architect to encode the direction into a graph-backed prompt.

Frequently asked questions

What does the Reference Driven Design AI skill do?

Build an evidence-backed visual reference brief before UI concept or polish work. Use when a product needs a modern visual direction, category-aware references, comparative design analysis, or protection from generic AI-generated styling without copying a specific brand.

Why use Reference Driven Design on TypingMind?

Because you install it once and use it with any model. Reference Driven Design 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 Reference Driven Design in TypingMind?

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

Which AI models can use Reference Driven Design?

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 Reference Driven Design?

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

Is the Reference Driven Design AI skill free?

Yes. It is published on GitHub by dev-ik 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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