ai-image

ai-image Visual Quality Diagnostic Assistant

Overview

The visual quality diagnostic assistant is used to answer "Is this image good enough to publish?", e-commerce operations, Amazon, and growth diagnostics. It is suitable for sales, operations, marketing, and e-commerce to call after clarifying business goals, content materials, or analysis targets. It will combine inputs such as visual brief or screenshot descriptions, pasted visual briefs, screenshot descriptions, brand guidelines, page issues, platform specifications, or modification... to organize key context and output visual/creative diagnostics, prompts or storyboard plans, and publishing specification checks, making it easy to continue execution, review, or delivery. Capability positioning supplement: skills.sh all-time/trending signals show that image has user demand; the platform displays it as a design_media business assistant directory, outputting reviewable diagnostics, recommendations, or production preparation packages.

When to Use

Applicable Scenarios

Users ask questions like "Is this image good enough to publish?" and need to quickly break down goals, determine priorities, and form actionable results
Sales, operations, marketing, and e-commerce need to generate visual/creative diagnostics, prompts or storyboard plans, and publishing specification checks around the visual quality diagnostic assistant
Users have already prepared the review objective (explain the layout, readability, brand consistency, conversion goal, or delivery standard to be checked), usage scenario (explain the platform, audience, device, campaign, or review scenario the visual materials are intended for), and page or material link (fill in publicly accessible pages, materials, design draft previews, or reference links; for restricted content, please upload instead), and want to organize them into actionable analysis or optimization results
Users need 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":"审查目标"}'

Parameter Description

Parameter	Type	Required	Default	Description
`goal`	string	No	-	Explain the layout, readability, brand consistency, conversion goal, or delivery standard to be checked
`audience`	string	No	-	Explain the platform, audience, device, campaign, or review scenario the visual materials are intended for
`materialUrl`	string	No	-	Fill in publicly accessible pages, materials, design draft previews, or reference links; for restricted content, please upload instead; a complete accessible URL is required
`materialFile`	string	No	-	Upload screenshots, images, presentation decks, brand guidelines, product images, or visual reference files
`materialText`	string	No	-	Paste visual briefs, screenshot descriptions, brand guidelines, page issues, platform specifications, or modification goals
`brandRequirements`	string	No	-	Supplement brand guidelines, size ratios, platform restrictions, unchangeable elements, accessibility requirements, or manual review priorities

See the complete machine-readable parameter structure at references/form-schema.json。

Parameter Value Reference

This skill currently has no categorical parameters that require additional lookup.

Supported input formats

This skill currently accepts JSON parameters directly; if the parameters contain a link field, pass a complete, accessible URL.

Example request

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

python3 scripts/run.py --params '{"goal":"审查目标"}'

Equivalent --params JSON：

{
  "goal": "审查目标"
}

Example response

{
  "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

Visual/creative diagnosis, prompts or storyboard plans, publishing spec checks: organize the core results around the user's goals that can be directly read, reviewed, or delivered.
Input material interpretation: combine the review goal (state the layout, readability, brand consistency, conversion goal, or delivery standard to be checked.), usage scenario (state the platform, audience, device, campaign, or review scenario the visual assets are intended for.), page or asset links (fill in publicly accessible pages, assets, design draft previews, or reference links; for restricted content, upload instead.) to distill 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 Diagnosis Assistant".
Then check whether the results cover visual/creative diagnosis, prompts or storyboard plans, publishing spec checks, and whether clear next steps are provided.
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