What does this skill do, and when should you use it?
A skill from the taste-skill repository, dedicated to generating mobile app UI concepts as images. It produces no code, only images. The skill emphasizes art-directed, non-template output and includes internal design dials (e.g., visual density, art direction) to guide generation. It mandates presenting UI inside a phone mockup by default, keeps content as the hero, and supports iOS, Android, or cross-platform modes. It covers a wide range of categories like onboarding, auth, home, social, fintech, health, and more.
The skill instructs the AI model to generate mobile screen images by following strict design rules. It infers the app category, platform mode, and number of screens from user prompts. It applies predefined design dials (such as generous spacing, high readability, and controlled palettes) and enforces principles like mandatory phone mockup framing, clean first screens, respect for safe areas, and multi-screen consistency. It actively rejects common AI tells (e.g., fake fintech dashboards, card clutter, tiny text) and uses an internal checklist to refine output quality.
- Product designers mocking up visually polished mobile app concepts.
- Developers previewing an app's visual direction before implementation.
- Freelancers presenting mobile UI concept demos to clients.
- Founders quickly iterating on mobile app visual concepts without coding.
- AI-assisted workflows where this skill generates reference images for a coding agent to implement.
How do you install this skill?
- The skill's core function relies on external image generation services that may be unreachable or slow in mainland China; verify your environment.
- The skill documentation is in English only; no Chinese version or localization notes are provided, which may hinder Chinese users.
- Static review cannot verify actual output quality; conduct small-scale testing before deployment.
- Network access
Install via the skills CLI: npx skills add https://github.com/Leonxlnx/taste-skill --skill "imagegen-frontend-mobile". Alternatively, copy the SKILL.md file from skills/imagegen-frontend-mobile/ into your project.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/Leonxlnx/taste-skill.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/imagegen-frontend-mobile" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Design a 5-screen iOS-native premium fitness app
Provide the SKILL.md content to your AI agent (e.g., attach it or paste it into the conversation). Then describe your desired mobile app concept, for instance: "Design a 5-screen iOS-native premium fitness app". The skill's rules will guide the image generation. Note: this skill only describes the design direction; you need an image-capable model to produce the actual images.
What are this skill's strengths and limitations?
- Tailored to mobile UI with multiple design dials for non-generic, art-directed results.
- Enforces phone mockup framing and consistent visual systems, leading to coherent flow sets.
- Explicitly rejects common AI tells (fake dashboards, card overload, tiny text) to avoid slop output.
- The skill does not actually generate images; it only guides the model. You must use an image-capable model (e.g., ChatGPT Images).
- It only provides documentation and rules, not a functional image-generation engine.
- The documentation does not specify which models or platforms can directly trigger image generation.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| imagegen-frontend-mobile — Mobile App Visual Generation Skill this page | 63 · Recommended | ★ 94k | 4d ago | MIT |
| Codex Image Studio ✓ OpenAI · Official | 55 · Use with care | ★ 28k | 3mo ago | — |
| Superdesign Design Skill | 53 · Use with care | ★ 647 | 1mo ago | MIT |
| imagegen-frontend-web: Frontend Website Reference Generator | 47 · Use with care | ★ 94k | 4d ago | MIT |
| Slides Grab HTML Skill | 60 · Recommended | ★ 1.2k | 1mo ago | MIT |
How did FollowSkills review this skill?
The skill only generates images, does not write code, does not request extra permissions or access external services, and its data flow is clear (text prompt in, image output out). No malicious content, covert exfiltration, or destructive behavior found. The skill's description matches its functionality. Deductions: as part of a skill package, there is no explicit documentation of execution environment isolation or rollback mechanisms for the skill itself; publisher identity is unverified but this is not penalized per rules.
The skill documentation is detailed with many instructions and rules, but static review cannot verify the repeatability and stability of these rules in actual image generation. The skill depends on external image generation models, whose output variability may affect reliability. Deductions: lack of test cases, example outputs, or reproducible experimental evidence to confirm key-path stability.
The skill's scope is clearly defined (mobile app screens and flows), with explicit lists of applicable and non-applicable scenarios. Trigger conditions are clear; users can invoke via keywords and context. However, the skill is primarily in English with no Chinese support; and its core function depends on external image generation services that may be unreachable in mainland China, affecting environmental fit. Deductions: limited language support and dependency on overseas services.
The skill documentation is well-structured with sections, lists, and clear instructions. It provides configuration parameters (e.g., DESIGN_VARIANCE) and trigger methods. However, installation instructions, dependency notes, version history, and maintenance responsibility are mainly in the repository README and CHANGELOG, not in the skill file itself; the skill lacks version info and changelog. Deductions: the skill itself lacks version and history information, relying on repository-level docs.
The skill documentation describes expected output standards and rules in detail, which theoretically can guide high-quality mobile screen generation. But static review cannot verify actual effectiveness; there are no example outputs or user feedback. Deductions: no evidence of output quality and usability; requires execution verification.
The skill's rules and output standards lack verifiable evidence; there are no test suites or execution logs. The repository has no CI configuration or test suite to verify skill behavior. Deductions: key claims cannot be independently verified; lack of third-party evidence.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →