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Design Gate

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ymt23
design-gate

Conversation-first visual design gate for AI-driven UI design and implementation. Use when Codex must turn short human direction, screen requirements, concept art, Lazyweb/Mobbin references, or implemented UI screenshots into design direction, visual direction, Evidence Board, Reference Decision Matrix, Visual Fidelity Contract, implementation handoff, rework instruction, pass/fail design review, State Capsule, Self-Review, or version report. Use for Human/CX role separation, internal request intent, parallel Lazyweb/Mobbin reference lanes, required reference research before proposals or contracts, hard fail rules, score caps, and multi-agent handoff/review contracts.

Overview

Publisherymt23
Repositorycx-design-gate
Skill namedesign-gate
Stars
3
Forks
0
Bundled files
23
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.

  • 23 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ymt23 on GitHub. Read the source before you install it.

Installation

Install the Design Gate 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/ymt23/cx-design-gate/tree/main/skills/design-gate
  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/ymt23/cx-design-gate.git /tmp/cx-design-gate
mkdir -p .claude/skills
cp -r /tmp/cx-design-gate/skills/design-gate .claude/skills/design-gate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Gate 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 Gate 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 Gate 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 Gate

Use Design Gate to prevent implementation agents from treating build success or constraint satisfaction as visual success. The skill owns the conversation-first workflow between project-specific requirements, reference material, design direction, implementation handoff, and screenshot-based design review.

Core Rule

Keep project truth outside this skill. Read project documents and project-local calibration packs from the consuming project. Humans should direct outcomes in natural language; Design Gate infers the internal intent and supplies the procedure, schemas, gates, prompts, and generic failure patterns.

Workflow

  1. Accept short Human direction. Do not require Humans to provide request_type, Lazyweb/Mobbin instructions, Evidence Board instructions, or no-mutation constraints.
  2. Infer the internal intent. Accept explicit request_type only as a backward-compatible override.
  3. Resolve requirements, default calibration, constraints, and any linked references into a Project Context Packet.
  4. For design, visual, reference, or contract intents, run the Reference Research Workflow before proposing a screen direction or contract.
  5. If required input is missing, stop with Input blocked. If required reference research is blocked, stop with Reference research blocked.
  6. Build parallel Lazyweb and Mobbin reference lanes, then an Evidence Board and Reference Decision Matrix before converting references into direction or contract outputs.
  7. Apply hard fail rules before assigning a score.
  8. Treat screenshots and visible behavior as stronger evidence than implementation self-reports.
  9. Do not implement unless the user explicitly asks for implementation.
  10. End each response with State Capsule and CXDG Self-Review unless the request is only a terse version check.
  11. Produce one of these outputs, matching the inferred intent:
  • Design Direction
  • Visual Direction
  • Evidence Board
  • Reference Decision Matrix
  • Visual Fidelity Contract
  • Implementer Handoff Prompt
  • Designer Screenshot Review
  • Rework Instruction
  • Calibration Case Draft
  • Version Report

Reference Research Workflow

Use this workflow whenever the user asks for a screen proposal, visual direction, design direction, reference gate, or Visual Fidelity Contract, even if the user does not name an internal request type.

Required sequence:

  1. Read the packet-linked requirements and relevant active calibration cases.
  2. Confirm both Lazyweb and Mobbin MCP reference tools are available.
  3. Search Lazyweb and Mobbin with target-screen queries derived from the requirements.
  4. Keep results in separate lanes:
    • Lazyweb Lane: flow, direction, hypotheses, screen patterns, and A/B-style clues.
    • Mobbin Lane: shipped UI examples, component structure, density, spacing, and transitions.
    • Synthesis Lane: adoption, transformation, rejection, and integrated recommendations.
  5. Build an Evidence Board with preview, source URL or identifier, relevance, and evidence type.
  6. Decide whether concept evidence is needed. Use CX image generation only when the request or requirements ask for mood, polish, decoration, visual direction, or concept art. If not used, state Concept Evidence: not generated and why.
  7. Create a Reference Decision Matrix with adopt, transform, reject, or compare_only for each reference.
  8. Only after the Evidence Board and matrix, produce design directions, visual directions, or a Visual Fidelity Contract.

Do not use calibration cases as a substitute for live Lazyweb/Mobbin reference research. If either Lazyweb or Mobbin is unavailable, missing, unauthenticated, or returns no usable evidence, output Reference research blocked with the missing source and do not produce screen proposals.

For Mobbin, use screen and flow results as static or flow references unless the tool response includes motion evidence. Do not infer video, duration, easing, or animation metadata from Mobbin screen images.

For detailed input and output shape, use references/io-contract.md, references/request-types.md, templates/evidence-board.md, references/reference-decision-matrix.md, templates/design-direction-output.md, templates/state-capsule.md, and templates/self-review.md.

Role Split

Use the detailed role model in references/role-model.md when a task involves multiple CX roles or Human handoff.

Default roles:

  • Human: Director and final judge.
  • Project Agent: Collects project truth and creates the Project Context Packet.
  • Design Gate / Designer CX: Reviews visible UI against contract and hard gates.
  • Architect CX: Converts requirements and references into contracts.
  • Implementer CX: Builds from the contract and returns screenshot/build evidence.
  • MCP services: Provide references only; they do not decide adoption or visual pass.

Required Inputs

If the task lacks readable requirement sources, ask the Project Agent or Human for them, or produce a packet request using templates/project-context-packet.md.

For normal use, accept the conversation-first input in references/io-contract.md. Do not require the Human or Project Agent to prebuild live reference research, Evidence Board, Reference Decision Matrix, request_type, or no-mutation constraints.

Minimum Project Context Packet fields:

  • project_id
  • target_screen
  • request_type
  • screen_requirements
  • design_principles
  • implementation_scope
  • reference_assets
  • non_negotiable_rules
  • release_constraints
  • active_calibration_cases

See references/project-context-packet.md for the full schema.

Layer Gate

Always distinguish persistent controls from scroll content:

  • Persistent Control Layer: tab bars, nav bars, toolbars, floating CTAs, HUDs, sticky action bars.
  • Scroll Content Layer: cards, lists, grids, carousels, feeds, form sections, inventory/item grids.

Persistent controls must not deform scroll-content item internals. If overlap must be managed, prefer scroll padding, safe area, z-index, hit-area rules, or review-scoped acceptance. Do not shrink card text, alter item dimensions, or distort content layout to avoid an overlay.

Hard Fail First

Before scoring, apply references/hard-fail-rules.md. If a hard fail applies, say so directly and do not call the result pass.

Common hard fails:

  • Primary text is unreadable, clipped, hidden, or too compressed at screenshot scale.
  • Grid/list/card item display is broken even if constraints technically fit.
  • Persistent controls deform content item internals.
  • Screenshot contradicts the implementer's self-report.
  • Build/test success is used as visual pass evidence.

Score Caps

If the user requests scoring, apply references/score-cap-rules.md. Score caps limit optimistic scoring when important evidence is missing or partially broken.

Resources

  • references/role-model.md: Human/CX/MCP responsibilities and forbidden judgments.
  • references/io-contract.md: Conversation-first input and fixed output contract.
  • references/request-types.md: Internal intent labels and outputs.
  • references/project-context-packet.md: Context packet schema and intake rules.
  • references/reference-decision-matrix.md: Required Lazyweb/Mobbin reference research and adoption decisions.
  • references/visual-fidelity-contract.md: Contract structure for image/reference to UI implementation.
  • references/hard-fail-rules.md: Non-negotiable visual fail conditions.
  • references/score-cap-rules.md: Score cap rubric.
  • references/calibration-case-schema.md: Project-local calibration case schema.
  • references/multi-agent-orchestration.md: Multi-agent handoff/review/rework contract.
  • templates/state-capsule.md: Conversation state handoff shape.
  • templates/self-review.md: CXDG guardrail checklist.
  • templates/version-report.md: Plugin version and CHANGELOG report shape.
  • templates/*.md: Prompt and artifact templates.
  • ../../examples/generic/*.md: Anonymized pass/fail calibration patterns.

Output Discipline

Use concise, evidence-led judgments. Separate:

  • observed screenshot result
  • implementation claim
  • contract requirement
  • pass/fail decision
  • next action

When the screenshot and implementation report disagree, prefer the screenshot.

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 Gate AI skill do?

Conversation-first visual design gate for AI-driven UI design and implementation. Use when Codex must turn short human direction, screen requirements, concept art, Lazyweb/Mobbin references, or implemented UI screenshots into design direction, visual direction, Evidence Board, Reference Decision Matrix, Visual Fidelity Contract, implementation handoff, rework instruction, pass/fail design review, State Capsule, Self-Review, or version report. Use for Human/CX role separation, internal request intent, parallel Lazyweb/Mobbin reference lanes, required reference research before proposals or co...

Why use Design Gate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ymt23/cx-design-gate/tree/main/skills/design-gate. 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 Gate?

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 Gate?

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

Is the Design Gate AI skill free?

Yes. It is published on GitHub by ymt23 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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