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.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
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。
One-stop pipeline for e-commerce product images: category detection → style matching → scene compositing → text overlay → quality check → platform adaptation → batch delivery.
# 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
| 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.py | 7 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 |
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）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brand_config_template.json 新建自己的品牌（含 Logo 路径/色系/保障条）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）No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Handle ActiveCampaign contact and marketing tasks through Rube MCP, a connection for agent tools. Find or create contacts, manage tags and subscriptions, enroll contacts in existing automated workflows, and create follow-up tasks.
Research competitors’ live Meta and Google ads to identify opening hooks, offers, calls to action, and landing pages. Receive a sourced creative brief and up to three test ideas adapted to your product.
Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
The English summary is being prepared. Showing the original description.
Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
The English summary is being prepared. Showing the original description.
Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
The English summary is being prepared. Showing the original description.
Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.
The English summary is being prepared. Showing the original description.