agentleFS
jiawood2006jiawood2006/hermes-skills/skills/ecommerce-material-studio/SKILL.md

ecommerce-material-studio

Skill2 starsChanged 3 months agoMITView on GitHub

电商素材工坊(中英双语)。用户需要生成电商主图、详情图、场景图、产品图合成、品牌叠加时使用。自动识别品类→匹配风格→场景感知合成→统一文字→自动质检→多平台适配→标准化交付。E-commerce product image studio: category detection, style matching, scene-aware compositing, text overlay, quality check, multi-platform adaptation, batch delivery.

  • Reads credentials
  • Installs packages

1 other project uses a version of this, 1 of them edited.

Adopted by

Most recently taken up by

  • Other versions1 project uses a version of this.

    Word for word · 0

    None found.

    Wrote their own version · 0

    None found.

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository8 other files it gives its agents.

The file

Files
SKILL.md

电商素材工坊 / E-commerce Material Studio

生成电商产品素材(主图/详情图/场景图)的一站式工具链。输入产品图片 → 自动完成品类识别、风格匹配、场景合成、文字叠加、质检、多平台适配、批量交付。

v2 新增:AI 场景融合合成器(scripts/ai_compositor.py)——用 Qwen-Image-Edit 图生图把产品真实融入使用场景(AI 重绘光影/透视,非 PIL 贴图),支持卖点→场景自动匹配。

One-stop pipeline for e-commerce product images: category detection → style matching → scene compositing → text overlay → quality check → platform adaptation → batch delivery.

⚠️ 本技能只做图,不做视频(scripts/ 里 0 处视频代码,已验证)。 视频侧在 ai-video-ad-production 技能 —— 整机 = 它的 scripts/make_ad.py(一条命令出片: 提交→轮询→抽帧核验→装配30s/15s→交付)。 两边共用同一份产品档案:本技能 references/product_profiles.json(例:haier_hsq1_shaver)。 生图脚本读 image 段;视频读 video + video_lock 段。改档案一处,图/视频同时生效。

何时使用 / When to use

  • 用户需要生成电商主图/详情图(如"帮我做一套剃须刀主图")
  • 需要产品图合成到场景(白底图 → 场景图)
  • 需要品牌叠加(Logo/保障条/卖点文字/徽章)
  • 需要多平台尺寸适配(淘宝/快手/抖音/拼多多/京东等 7 平台)
  • 需要批量生成多个产品素材

使用流程 / Workflow

# 0. 环境依赖(一次性)
pip install Pillow numpy scipy

# 1. 品类识别:输入产品图 → 识别品类/风格
python3 scripts/category_detector.py --image product.png

# 2. 风格匹配:品类+价位+平台+品牌 → 推荐模板
python3 scripts/style_matcher.py --category 个护电器 --sub-category 剃须刀 --price 169 --platform kuaishou

# 3. 场景感知合成(核心):库调用(SceneAwareCompositor 是 Python 库,非 CLI)
python3 -c "
from PIL import Image
from scripts.scene_aware_compositor import SceneAwareCompositor
c = SceneAwareCompositor()
scene = Image.open('scene.jpg'); product = Image.open('product.png')
result = c.composite(scene_image=scene, product_image=product, scene_type='lifestyle_bathroom', position=(0.5, 0.45))
result.save('result.png')
"

# 4. 统一文字叠加(处理 plan.json 里所有文字层)
python3 scripts/text_engine.py --plan output/plan.json --brand langke --scene-tone dark

# 5. 自动质检(读取 plan.json + 检查成品图)
python3 scripts/quality_check.py --plan output/plan.json

# 6. 多平台尺寸适配
python3 scripts/platform_adapter.py --input-dir ./output --platforms kuaishou --output-dir ./platform_output

# 7. 标准化交付打包(自动生成使用指南+清单+zip)
python3 scripts/delivery_packager.py --project-dir ./output --product-name "示例产品"

# 8. 批量处理(多产品,断点续传)
python3 scripts/batch_processor.py --input products.json --output-dir ./batch --prepare

# 9. AI 场景融合(v2,需 SILICON_FLOW_API_KEY)
python3 scripts/ai_compositor.py product.png --selling-point "90天续航" -o output/ai_scene.png
#    卖点→场景模板:口袋mini/90天续航/动力/防水/便携(自动匹配专属场景)
#    或自定义场景:--scene "自定义场景描述"
#    预览 prompt 不调用:--dry-run

⚠️ AI 融合铁律(来自 siliconflow-image-api 实战):

  • 产品必须写死:--product-desc 写清产品部件特征(如"深色磨砂圆柱机身,顶部三头浮动刀头"),否则 AI 改画
  • 禁止只画场景不画产品:prompt 已内置"产品必须是画面绝对主角"锁定句
  • 卖点→场景绑定:每个卖点有专属场景(含视觉证据),不写抽象纹理背景
  • 生成后必须 vision/人眼校验:产品保真?场景贴卖点?不贴合重新生成

模块清单 / Modules

模块功能依赖
category_detector.py品类识别(色调/材质→子品类)Pillow
style_matcher.py风格匹配(品类+价位+平台+品牌→模板)无
brand_loader.py品牌配置加载(多品牌/Logo选择)无
text_engine.py统一文字引擎(z-index/避让/对比度)Pillow
quality_check.py自动质检(分辨率/可读性/Logo/完整/重叠)Pillow
preference_memory.py偏好记忆(跨项目复用风格)无
batch_processor.py批量处理(断点续传/重试/报告)无
platform_adapter.py7 平台尺寸适配(resize/crop/压缩)Pillow
delivery_packager.py交付打包(使用指南+清单+zip)Pillow
layout_engine.py布局引擎(物理尺寸→像素比例)无
scene_aware_compositor.py场景感知合成(参照物尺度/透视/景深)Pillow+numpy+scipy

数据文件 / Data (references/)

  • category_templates.json — 12 品类场景模板库(推荐场景/prompt/配色/文字风格)
  • product_profiles.json — 产品档案库(示例:example_shaver)
  • brand_profiles/langke.json — 示例品牌配置(朗科=示例品牌,非真实)
  • brand_config_template.json — 新建品牌模板
  • user_preferences.json — 偏好记忆库(模板)

注意事项 / Notes

  • 字体:text_engine.py 需要中文字体(macOS: /System/Library/Fonts/PingFang.ttc,Linux: NotoSansCJK,Windows: msyh.ttc)——按需修改 FONT_PATHS_BOLD/FONT_PATHS_REGULAR 常量
  • 品牌配置:用 brand_config_template.json 新建自己的品牌(含 Logo 路径/色系/保障条)
  • 合成模式:scene_aware_compositor.py 支持场景感知模式(自动算尺度)和兼容模式(固定 scale)
  • 参考数据中的"朗科/LangKe"为示例品牌,可直接替换为自己的品牌配置

💛 免费使用 · 自愿支持 / Free with optional support

本技能完全免费使用。 觉得好用、帮到你了,可以自愿扫码支持(金额随意,一杯咖啡即可):

Image: 支付宝收款码

支持过我的人,后续 Pro 版/批量服务有优惠。 想提需求、反馈问题,欢迎到 Gitee 仓库提 Issue:https://gitee.com/tao6677/useful-tools

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.