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Ad-Ready Pro

作者 Paul de Lavallaz · GitHub ↗ · v1.0.0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install ad-ready-pro
功能描述
Generate professional advertising images from product URLs using the Ad-Ready pipeline on ComfyDeploy. Use when the user wants to create ads for any product by providing a URL, optionally with a brand profile (70+ brands) and funnel stage targeting. Supports model/talent integration, brand-aware creative direction, and multi-format output. Differs from Morpheus (manual fashion photography) — Ad-Ready is URL-driven, brand-intelligent, and funnel-stage aware.
使用说明 (SKILL.md)

Ad-Ready: AI Advertising Image Generator

Generate professional advertising images from product URLs using a 4-phase AI pipeline on ComfyDeploy.

⚠️ CRITICAL: Required Inputs Checklist

Before running ANY ad generation, the agent MUST ensure ALL of these are provided:

Input Required? How to Get It
--product-url ✅ ALWAYS User provides the product page URL
--product-image ✅ ALWAYS Download from the product page, or user provides
--logo ✅ ALWAYS Download from brand website or search online. MUST be an image file
--reference ✅ RECOMMENDED An existing ad whose style we want to clone. Search online or use previously generated images
--brand-profile ✅ NEVER EMPTY Pick from catalog or run brand-analyzer first. NEVER leave as "No Brand" if a brand is known
--prompt-profile ✅ ALWAYS Choose based on campaign objective
--aspect-ratio Default: 4:5 Change if needed for platform
--model Optional Model/talent face from catalog or user-provided

🚨 NEVER Skip These Steps:

  1. Product image — Download the main product photo from the product URL. The scraper is fragile; always provide a product image explicitly.
  2. Brand logo — Download the logo from the brand's official website or search for "{brand name} logo" online. Must be a clean logo image (PNG preferred).
  3. Brand profile — If the brand doesn't exist in the catalog, run brand-analyzer skill FIRST to generate one. Never submit with "No Brand" when a brand is known.
  4. Reference image — Search for an existing ad or visual with a style that matches what we're generating. Can be from previously generated images, the brand's campaigns, or found online. This dramatically improves output quality.

Auto-Preparation Workflow

When the user asks to generate an ad, follow this workflow:

1. User provides: product URL + brand name + objective

2. CHECK brand profile exists:
   → ls ~/clawd/ad-ready/configs/Brands/ | grep -i "{brand}"
   → If not found: run brand-analyzer skill first
   
3. DOWNLOAD product image:
   → Visit the product URL in browser or fetch the page
   → Find and download the main product image
   → Save to /tmp/ad-ready-product.jpg

4. DOWNLOAD brand logo:
   → Search "{brand name} logo PNG" or fetch from brand website
   → Download clean logo image
   → Save to /tmp/ad-ready-logo.png

5. FIND reference image:
   → Search for "{brand name} advertisement" or similar
   → Or use a previously generated ad that has the right style
   → Save to /tmp/ad-ready-reference.jpg

6. SELECT prompt profile based on objective:
   → Awareness: brand discovery, first impressions
   → Interest: engagement, curiosity
   → Consideration: comparison, features
   → Evaluation: deep dive, decision support
   → Conversion: purchase intent, CTAs (most common)
   → Retention: re-engagement
   → Loyalty: brand advocates
   → Advocacy: referral, community

7. RUN the generation with ALL inputs filled

Usage

Full command (recommended):

COMFY_DEPLOY_API_KEY="$KEY" uv run ~/.clawdbot/skills/ad-ready/scripts/generate.py \
  --product-url "https://shop.example.com/product" \
  --product-image "/tmp/product-photo.jpg" \
  --logo "/tmp/brand-logo.png" \
  --reference "/tmp/reference-ad.jpg" \
  --model "models-catalog/catalog/images/model_15.jpg" \
  --brand-profile "Nike" \
  --prompt-profile "Master_prompt_05_Conversion" \
  --aspect-ratio "4:5" \
  --output "ad-output.png"

Auto-fetch mode (downloads product image and logo automatically):

COMFY_DEPLOY_API_KEY="$KEY" uv run ~/.clawdbot/skills/ad-ready/scripts/generate.py \
  --product-url "https://shop.example.com/product" \
  --brand-profile "Nike" \
  --prompt-profile "Master_prompt_05_Conversion" \
  --auto-fetch \
  --output "ad-output.png"

The --auto-fetch flag will:

  • Download the main product image from the product URL
  • Search and download the brand logo
  • Both get uploaded to ComfyDeploy automatically

API Details

Endpoint: https://api.comfydeploy.com/api/run/deployment/queue Deployment ID: e37318e6-ef21-4aab-bc90-8fb29624cd15

ComfyDeploy Input Variables

These are the exact variable names the ComfyDeploy deployment expects:

Variable Type Description
product_url string Product page URL to scrape
producto image URL Product image (uploaded to ComfyDeploy)
model image URL Model/talent face reference
referencia image URL Style reference ad image
marca image URL Brand logo image
brand_profile enum Brand name from catalog
prompt_profile enum Funnel stage prompt
aspect_ratio enum Output format

4-Phase Pipeline (How It Works Internally)

Phase 1: Product Scraping

  • Gemini Flash visits the product URL
  • Extracts: title, description, features, price, images
  • ⚠️ Image scraping is the most fragile part — always provide product images manually

Phase 2: Campaign Brief Generation (CRITICAL)

  • Uses Brand Identity JSON + Product Data → 10-point brief
  • Everything downstream depends on brief quality
  • Brief covers: strategic objective, central message, visual tone, product role, photographer, art direction, environment, textures, signature

Phase 3: Blueprint Generation

  • Master Prompt (per funnel stage) + Brief + Product JSON + Keyword Bank + Format
  • Gemini Flash generates complete Blueprint JSON
  • Covers: scene, production, graphic design, lighting, composition, materials, CTA

Phase 4: Image Generation

  • Nano Banana Pro (Imagen 3.0) generates the final image
  • Uses Blueprint JSON + all reference images (product, talent, logo, style ref)

Supporting Reference Nodes

  • pose_ref → enforce a specific pose (replicated exactly)
  • photo_style_ref → replicate photographic style (⚠️ can be too literal, being optimized)
  • location_ref → replicate location and color palette

Brand Profiles

Existing catalog (70+ brands):

ls ~/clawd/ad-ready/configs/Brands/*.json | sed 's/.*\///' | sed 's/\.json//'

Creating new brand profiles:

Use the brand-analyzer skill:

GEMINI_API_KEY="$KEY" uv run ~/.clawdbot/skills/brand-analyzer/scripts/analyze.py \
  --brand "Brand Name" --auto-save

This generates a full Brand Identity JSON and saves it to the catalog automatically.

Prompt Profiles (Funnel Stages)

Profile Stage Best For
Master_prompt_01_Awareness Awareness Brand discovery, first impressions
Master_prompt_02_Interest Interest Engagement, curiosity
Master_prompt_03_Consideration Consideration Comparison, features
Master_prompt_04_Evaluation Evaluation Deep dive, decision support
Master_prompt_05_Conversion Conversion Purchase intent, CTAs
Master_prompt_06_Retention Retention Re-engagement, loyalty
Master_prompt_07_Loyalty Loyalty Brand advocates
Master_prompt_08_Advocacy Advocacy Referral, community

How to choose:

  • Most ads → Conversion (purchase intent)
  • New product launches → Awareness
  • Retargeting → Consideration or Evaluation
  • Existing customers → Retention or Loyalty

Aspect Ratios

Ratio Use Case
4:5 Default. Instagram feed, Facebook
9:16 Stories, Reels, TikTok
1:1 Square posts
16:9 YouTube, landscape banners
5:4 Alternative landscape

Model Catalog

Models for talent/face reference: ~/clawd/models-catalog/catalog/

Priority: User-provided model > Catalog selection > No model (product-only ad)

Known Limitations

  1. Product image scraping is fragile — always provide product images manually when possible
  2. photo_style_ref can be too literal — the style reference may be replicated too closely
  3. Some websites block scraping — Armani works well, others may return incorrect data
  4. Auto 4-Format is alpha — bugs and edge cases exist
  5. Gemini hallucinations — occasional issues in complex reasoning steps

Ad-Ready vs Morpheus

Feature Ad-Ready Morpheus
Input Product URL (auto-scrapes) Manual product image
Brand intelligence 70+ brand profiles None
Funnel targeting 8 funnel stages None
Creative direction Auto-generated from brief Pack-based (camera, lens, etc.)
Best for Product advertising campaigns Fashion/lifestyle editorial photography
Control level High-level (objective-driven) Granular (every visual parameter)

Source Repository

API Key

Uses ComfyDeploy API key. Set via COMFY_DEPLOY_API_KEY environment variable.

安全使用建议
Before installing, note these specific concerns: (1) The script requires a COMFY_DEPLOY_API_KEY (Bearer token) but the skill metadata does not list it — you will need to provide that credential; only supply it if you trust the ComfyDeploy endpoint and this skill's author. (2) The skill will read ~/clawd/ad-ready/configs/Brands to find brand profiles and may prompt you to run a brand-analyzer skill; review any local brand files the skill will access. (3) In auto-fetch mode the agent will scrape product pages and search/download logos and reference images from the web, then upload images and inputs to api.comfydeploy.com — do not use auto-fetch with proprietary or sensitive product pages you do not want sent to a third party. (4) The script contains a hard-coded logo service URL with an embedded token — that is unexpected and should be reviewed. (5) There's no install spec for dependencies; run in a controlled/test environment first and inspect network traffic or run offline if you need to verify behavior. If you still want to use it, request the skill author/source/homepage, confirm the COMFY_DEPLOY_API_KEY handling, and consider running it with a limited/test API key and on non-sensitive assets.
功能分析
Type: OpenClaw Skill Name: ad-ready-pro Version: 1.0.0 The skill is classified as suspicious due to its broad use of high-risk capabilities, even though they appear aligned with the stated purpose. The `SKILL.md` explicitly instructs the AI agent to execute shell commands (`ls | grep`) and download files from arbitrary URLs to `/tmp`. The `scripts/generate.py` then performs extensive network requests, including fetching brand logos from external services (`logo.clearbit.com`, `img.logo.dev`), scraping product pages, and uploading user-provided or auto-fetched images to `api.comfydeploy.com`. While these actions are necessary for the skill's functionality, the ability to download and write arbitrary content to the filesystem, combined with reliance on external third-party content sources and the agent's shell execution capabilities, presents a significant attack surface for potential supply chain attacks or prompt injection leading to unintended file writes or data exposure, even if no explicit malicious intent is observed.
能力评估
Purpose & Capability
The skill's stated purpose (generate ads via ComfyDeploy) aligns with the included script and SKILL.md. However the package metadata declares no required env vars or config paths while both the instructions and generate.py clearly rely on a COMFY_DEPLOY_API_KEY and on a local brand catalog at ~/clawd/ad-ready/configs/Brands. That mismatch is an incoherence: the skill will need credentials and access to the user's home config area but the metadata doesn't advertise that.
Instruction Scope
SKILL.md instructs the agent to fetch product pages, scrape and download product images and brand logos, search the web for reference images, save files in /tmp, and then upload images/inputs to an external API (ComfyDeploy). These actions go beyond pure 'image generation' surface — they involve arbitrary network fetches and uploading potentially proprietary images to a third-party endpoint. The instructions also explicitly tell the agent to run other skills (brand-analyzer) and to search the web for logos, which increases the scope of what will be accessed and transmitted.
Install Mechanism
This is an instruction-only skill with a bundled Python script; there is no install spec. The script lists dependencies (httpx, beautifulsoup4) in comments but doesn't declare installation steps. Lack of an install spec is low risk by itself but means runtime failures or unexpected ad-hoc installation attempts could occur.
Credentials
Metadata declares no required environment variables, but SKILL.md and the script both require COMFY_DEPLOY_API_KEY (used as a Bearer token for uploading files and queuing runs). The script also reads the user's home directory for brand profiles (BRANDS_DIR). There is a hard-coded third-party logo service URL including an embedded token (img.logo.dev?token=pk_X-1ZO13GSgeOoUrIuJ6GMQ) which may be unnecessary or unexpected. These environment and credential uses are not documented in the metadata and therefore not proportionate.
Persistence & Privilege
The skill does not request always:true and does not declare modifications to other skills or system-wide settings. Autonomous invocation is allowed (default), which is normal — but combined with the other concerns (automatic fetching and uploading) it increases the potential blast radius.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install ad-ready-pro
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ad-ready-pro 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Brand-aware advertising image generator. 4-phase pipeline via ComfyDeploy.
元数据
Slug ad-ready-pro
版本 1.0.0
许可证
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Ad-Ready Pro 是什么?

Generate professional advertising images from product URLs using the Ad-Ready pipeline on ComfyDeploy. Use when the user wants to create ads for any product by providing a URL, optionally with a brand profile (70+ brands) and funnel stage targeting. Supports model/talent integration, brand-aware creative direction, and multi-format output. Differs from Morpheus (manual fashion photography) — Ad-Ready is URL-driven, brand-intelligent, and funnel-stage aware. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 1754 次。

如何安装 Ad-Ready Pro?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install ad-ready-pro」即可一键安装,无需额外配置。

Ad-Ready Pro 是免费的吗?

是的,Ad-Ready Pro 完全免费(开源免费),可自由下载、安装和使用。

Ad-Ready Pro 支持哪些平台?

Ad-Ready Pro 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Ad-Ready Pro?

由 Paul de Lavallaz(@pauldelavallaz)开发并维护,当前版本 v1.0.0。

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