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Astra Frontend Design

Community
Enixes
astra-frontend-design

Design, implement, or visually refine a web interface when the visible experience is a primary deliverable. Use for greenfield UI, redesigns, reference matching, and visual polish; skip backend-only work and behavior-only frontend changes.

Overview

PublisherEnixes
Repositoryastra-frontend-design
Skill nameastra-frontend-design
Stars
3
Forks
0
Bundled files
13
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.

  • 13 bundled files

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

  • Open source

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

Installation

Install the Astra Frontend 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/Enixes/astra-frontend-design/tree/main
  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/Enixes/astra-frontend-design.git \
  .claude/skills/astra-frontend-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Astra Frontend 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 Astra Frontend 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 Astra Frontend 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.

Astra Frontend Design

Build a coherent, distinctive interface that serves the product, fits the codebase, and survives inspection in a real browser. Carry the requested frontend work through implementation and visual verification unless the user asks only for a plan or critique.

The user's brief and the repository's established product constraints take precedence over this skill. Treat the guidance here as judgment criteria, not a house style. A familiar pattern is acceptable when it is the right choice for the subject and workflow.

Route the task

Read only the references the current task needs.

TaskReference
Landing page, portfolio, editorial, brand, or expressive siteart-direction.md
Dashboard, SaaS product, admin surface, form, or operational toolproduct-ui.md
Existing interface redesign or modernizationredesign.md
Screenshot, Figma frame, image, or named site as a referencereference-to-code.md
Prominent animation, scroll behavior, or interaction choreographymotion-and-interaction.md
Review, polish, accessibility audit, or final verificationquality-gates.md
A reusable prompt rather than implementationprompt-recipes.md
Compare skill quality across models or revisionsevaluation.md

Establish the design truth

Inspect the target route, nearby components, global styles, assets, and dependency manifest before changing visible UI. Reuse the project's framework, component primitives, tokens, icon family, and interaction conventions unless the task explicitly calls for a new direction.

Infer a compact design read from the available context:

  • subject, audience, and the screen's primary job;
  • visual character and one memorable idea;
  • information density and motion level;
  • content, behavior, brand, and accessibility constraints that must survive.

Proceed on reasonable assumptions. Ask one focused question only when different answers would materially change the product, brand, or interaction model. Do not stop after describing what could be built when the request authorizes implementation.

For a multi-screen product or ongoing redesign, preserve durable product facts separately from visual direction. If the repository lacks an equivalent, use PRODUCT.template.md and DESIGN.template.md as starting points. Do not create process documents for a small one-off change unless they will prevent real drift.

Shape before styling

Decide hierarchy, content order, responsive behavior, and interaction states before choosing decorative details. Give each surface one clear visual thesis. Spend expressive complexity where it supports that thesis and keep the rest disciplined.

Generated-looking conventions are warning signs only when they are unexamined defaults. Examples include repeated equal cards, decorative gradients, universal pill shapes, an eyebrow above every heading, arbitrary monospace labels, interchangeable copy, and motion on every element. Keep any of them when the brief genuinely calls for them; otherwise choose a more specific solution grounded in the subject.

Use truthful content. Do not invent customer names, testimonials, performance claims, precise metrics, or product capabilities. Clearly mark sample data when realistic data is needed to demonstrate a state.

Implement the real experience

  • Build the useful screen first. Do not substitute a marketing shell when the user asked for an app, tool, game, or dashboard.
  • Make expected controls and flows functional. Include relevant loading, empty, error, success, disabled, and overflow states.
  • Prefer real project assets or relevant images over decorative placeholders. Create or retrieve assets only when they materially improve the result and the available tools permit it.
  • Check the dependency manifest before importing a package. Add a dependency only when it provides needed capability that the current stack does not already offer.
  • Keep semantic HTML, keyboard access, visible focus, readable contrast, text reflow, reduced-motion behavior, and responsive layout in the implementation rather than in a detached checklist.

Finish with a visual loop

Run the smallest relevant build, lint, and behavior checks. Start the app when needed, capture the actual interface at representative desktop and mobile widths, and inspect the pixels rather than trusting the source code. Check hierarchy, framing, content fit, alignment, contrast, interaction feedback, and console errors. Fix visible or functional defects and repeat the affected checks until no material issue remains.

Use the project's existing browser or end-to-end tooling when available. Do not add a large test stack solely to take screenshots. Calibrate verification to the change: a token tweak needs less coverage than a responsive page or animated interaction.

In the handoff, lead with what is now usable. Mention the chosen visual direction and verification evidence briefly; do not narrate every design deliberation unless the user asks.

If paired with agent orchestration

Keep this skill's design and implementation judgment with the capable frontend implementer. On a bounded, browser-testable build, a smaller coordinator may give one Astra worker the original product brief and references, then test the running result and return screenshots plus concrete defects to the same worker thread. The coordinator should not replace the worker's visual judgment with a prescriptive layout recipe. Use direct Astra or an Astra-led plan when quality is paramount, the work is ambitious, or acceptance is difficult to test. Delegate only if the user and environment permit it; allocate essential checks explicitly.

Research basis

This skill is an original Astra-oriented synthesis. For provenance and the adaptation decisions, see research-basis.md.

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 Astra Frontend Design AI skill do?

Design, implement, or visually refine a web interface when the visible experience is a primary deliverable. Use for greenfield UI, redesigns, reference matching, and visual polish; skip backend-only work and behavior-only frontend changes.

Why use Astra Frontend Design on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Enixes/astra-frontend-design/tree/main. 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 Astra Frontend 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 Astra Frontend Design?

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

Is the Astra Frontend Design AI skill free?

It is published on GitHub by Enixes. Check the repository for licensing terms. 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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