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eachlabs-product-visualseachlabs 产品视觉效果

Agent Skill

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

总安装

2,637

周安装

111

GitHub Stars

12

下载量

924
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:eachlabs-product-visuals(eachlabs 产品视觉效果)
来源仓库:https://github.com/eachlabs/skills
仓库路径:skills/eachlabs-product-visuals
安装命令:
npx skills add https://github.com/eachlabs/skills --skill eachlabs-product-visuals
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/eachlabs/skills --skill eachlabs-product-visuals

简介

用于辅助产品视觉设计、UI 方案生成和视觉规范检查。

  • 适合整理页面结构、优化布局和配色,提升界面一致性。
  • 需结合品牌系统和用户任务使用,避免堆砌装饰元素。
  • 涉及真实页面改动时应通过预览检查文本溢出和对齐问题。
  • eachlabs-product-visuals 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

EachLabs Product Visuals

Generate professional product photography, e-commerce visuals, and product videos using EachLabs AI models.

Authentication

Header: X-API-Key: <your-api-key>

Set the EACHLABS_API_KEY environment variable. Get your key at eachlabs.ai.

Recommended Models

E-Commerce Specific

TaskModelSlug
Product photoshootProduct to Photoshootproduct-to-photoshoot
Product on modelProduct Photo to Modelshootproduct-photo-to-modelshoot
Color variationsProduct Colorsproduct-colors
Food photographyFood Photosfood-photos
Background removalProduct Background Removerproduct-backround-remover
Image upscalingProduct Photo Upscalerproduct-photo-upscaler
Home scene placementProduct Home Viewproduct-home-view
Product shotBria Product Shotbria-product-shot
Product shootProduct Shootproduct-shoot
Product arc shotEachlabs Product Arc Shoteachlabs-product-arc-shot-v1
Product zoom inEachlabs Product Zoom Ineachlabs-product-zoom-in-v1

General Purpose

TaskModelSlug
Product photographyGPT Image v1.5gpt-image-v1-5-text-to-image
Background replacementGPT Image v1.5 Editgpt-image-v1-5-edit
Product editingFlux 2 Turbo Editflux-2-turbo-edit
Multi-angle viewsQwen Image Editqwen-image-edit-2511-multiple-angles
Background removalRembg Enhancerembg-enhance
Background removalEachlabs BG Removereachlabs-bg-remover-v1
Image upscalingEachlabs Upscaler Proeachlabs-image-upscaler-pro-v1
Ad inpaintingSDXL Ad Inpaintsdxl-ad-inpaint
Custom product styleZ Image Trainerz-image-trainer
Product videoPixverse v5.6 Image-to-Videopixverse-v5-6-image-to-video

Prediction Flow

  1. Check model GET https://api.eachlabs.ai/v1/model?slug=<slug> — validates the model exists and returns the request_schema with exact input parameters. Always do this before creating a prediction to ensure correct inputs.
  2. POST https://api.eachlabs.ai/v1/prediction with model slug, version "0.0.1", and input matching the schema
  3. Poll GET https://api.eachlabs.ai/v1/prediction/{id} until status is "success" or "failed"
  4. Extract output URL from response

Workflows

Product on White Background

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "gpt-image-v1-5-edit",
    "version": "0.0.1",
    "input": {
      "prompt": "Place this product on a clean white background with soft studio lighting and subtle shadows",
      "image_urls": ["https://example.com/product.jpg"],
      "background": "opaque",
      "quality": "high"
    }
  }'

Lifestyle Scene Generation

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "gpt-image-v1-5-edit",
    "version": "0.0.1",
    "input": {
      "prompt": "Place this coffee mug on a cozy wooden desk in a modern home office with warm morning light, lifestyle photography",
      "image_urls": ["https://example.com/mug.jpg"],
      "quality": "high"
    }
  }'

Multi-Angle Product Views

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "qwen-image-edit-2511-multiple-angles",
    "version": "0.0.1",
    "input": {
      "image_urls": ["https://example.com/product.jpg"],
      "horizontal_angle": 45,
      "vertical_angle": 15,
      "zoom": 5
    }
  }'

Generate multiple angles by running separate predictions with different horizontal_angle values (0, 45, 90, 135, 180, 225, 270, 315 for a full 360).

Product with Transparent Background

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "gpt-image-v1-5-edit",
    "version": "0.0.1",
    "input": {
      "prompt": "Remove the background from this product image",
      "image_urls": ["https://example.com/product.jpg"],
      "background": "transparent",
      "output_format": "png"
    }
  }'

Product Video from Image

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "pixverse-v5-6-image-to-video",
    "version": "0.0.1",
    "input": {
      "image_url": "https://example.com/product-studio.jpg",
      "prompt": "Slow cinematic camera rotation around the product with dramatic studio lighting",
      "duration": "5",
      "resolution": "1080p"
    }
  }'

Prompt Tips for Products

  • Specify lighting: "soft studio lighting", "dramatic side lighting", "natural window light"
  • Mention surface: "marble surface", "wooden table", "clean white background"
  • Include shadows: "soft shadows", "reflection on surface"
  • Add context: "lifestyle setting", "in-use shot", "flat lay arrangement"
  • For batch catalog shots, maintain consistency with similar prompts

Batch Processing

For catalog-scale processing, create multiple predictions in parallel by sending separate POST requests for each product. Poll each prediction independently.

Parameter Reference

See the eachlabs-image-generation and eachlabs-video-generation references for complete model parameters.

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

32.89%
按下载量换算304

Claude

32.09%
按下载量换算297

Cursor

17.33%
按下载量换算160

Gemini CLI

8.93%
按下载量换算83

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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