ui-ux-pro-max

ui-ux-pro-max Visual Quality Diagnostic Assistant

Overview

The Visual Quality Diagnostic Assistant is used to answer "Is this image good enough to publish?", UI design, UX review, and design systems. It is suitable for product, operations, technical, and software use after clarifying business goals, content materials, or analysis targets. It combines inputs such as product processes and interface descriptions, pasted user flows, page copy, product background, current problem hypotheses, design review needs, or users... to organize key context and output summaries, diagnostic conclusions, action recommendations, and reusable deliverables, making it easy to continue execution, review, or delivery.

When to Use

Applicable Scenarios

The user asks questions such as "Is this image good enough to publish?" and needs to quickly break down goals, determine priorities, and form executable results
Product, operations, technical, and software need to generate summaries, diagnostic conclusions, action recommendations, and reusable deliverables around the Visual Quality Diagnostic Assistant
The user has already prepared experience goals (explaining the desired goals for task completion, conversion, retention, efficiency, trust, or satisfaction.), users and scenarios (explaining target users, task scenarios, usage frequency, device environment, proficiency, and key concerns.), product page or prototype links (providing publicly accessible product pages, prototype previews, design descriptions, competitor flows, or case links.), and wants to organize them into executable analysis or optimization results
The user needs to turn materials related to the Visual Quality Diagnostic Assistant into clear conclusions, priorities, and next steps

How to Call

Call the AI Skills API directly through the exported Python runner:

Command Examples

Basic Call

python3 scripts/run.py --params '{}'

Call with Common Parameters

python3 scripts/run.py --params '{"goal":"体验目标"}'

Parameter Description

Parameter	Type	Required	Default	Description
`goal`	string	No	-	Explain the desired goals for task completion, conversion, retention, efficiency, trust, or satisfaction
`audience`	string	No	-	Explain target users, task scenarios, usage frequency, device environment, proficiency, and key concerns
`materialUrl`	string	No	-	Provide publicly accessible product pages, prototype previews, design descriptions, competitor flows, or case links; an accessible complete URL is required
`reviewDepth`	string	No	`标准诊断`	Choose quick expert review, standard UI/UX audit, or deep roadmap; optional values:`快速体检`、`标准诊断`、`深度改稿`
`materialFile`	string	No	-	Supports multi-screen flow screenshots, prototype export images, design descriptions, PDFs, walkthrough records, or experience feedback documents
`materialText`	string	No	-	Paste user flows, page copy, product background, current problem hypotheses, design review needs, or user feedback summaries
`targetPlatform`	string	No	-	Choose Web, iOS, Android, Mini Program, desktop, multi-platform responsive, or other product carriers
`brandRequirements`	string	No	-	Supplement brand style, component library, interaction specifications, compliance restrictions, business constraints, and information that must be retained

For the complete machine-readable parameter structure, see references/form-schema.json。

Parameter Value Reference

The current skill has no categorical parameters that require additional lookup.

Supported Input Formats

The current skill directly accepts JSON parameters; if the parameters contain link fields, please pass complete, accessible URLs.

Example Request

The example parameters below can be passed directly to scripts/run.py, and the runner will send them to the AI Skills API.

python3 scripts/run.py --params '{"goal":"体验目标"}'

Equivalent --params JSON：

{
  "goal": "体验目标"
}

Example Return Result

{
  "success": true,
  "data": {
    "message": "示例结果请以技能真实返回结构为准。"
  },
  "meta": {
    "executionTime": 842,
    "cached": false
  }
}

Deliverables

Summary, diagnostic conclusions, action recommendations, and reusable deliverables: organize the core results around the user's goals that can be directly read, reviewed, or delivered.
Interpretation of input materials: combine the experience goals (explain the goals for task completion, conversion, retention, efficiency, trust, or satisfaction that you hope to optimize.), users and scenarios (explain the target users, task scenarios, usage frequency, device environment, proficiency, and key concerns.), and product page or prototype links (fill in publicly accessible product pages, prototype previews, design descriptions, competitor flows, or case links.) to extract key context and the basis for judgment.
Next steps: provide priorities, execution recommendations, or a content framework that can be further developed.

Suggestions for Using the Results

First determine whether the output answers the user's core questions about the "Visual Quality Diagnostic Assistant".
Then check whether the results cover the summary, diagnostic conclusions, action recommendations, and reusable deliverables, and whether clear next steps are given.
If the input materials are limited, it is recommended to have the user supplement the goals, audience, constraints, or original materials before running.

Preparation Before Running

AISKILLS_BASE_URL: default https://ai-skills.ai
AISKILLS_API_KEY: required, used for authenticated calls
AISKILLS_TENANT_ID: default default