name: 4d-compression-core version: 1.0.2 description: "把长内容压缩成结构化向量——节省 60-80% Token,保留核心信息" metadata: { "openclaw": { "emoji": "🌀", "requires": { "bins": ["jq", "awk"] }, "triggers": ["压缩", "4d",...
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
安装前可以查看交给 AI 代理的指令内容。
Generate professional advertising images from product URLs using a 4-phase AI pipeline on ComfyDeploy.
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 |
brand-analyzer skill FIRST to generate one. Never submit with "No Brand" when a brand is known.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
COMFY_DEPLOY_API_KEY="$KEY" uv run ~/.clawdbot/skills/ad-ready/scripts/generate.py \
--product-url "https://shop.example.com/product" \
--product-image "/tmp/product-photo.jpg" \
--logo "/tmp/brand-logo.png" \
--reference "/tmp/reference-ad.jpg" \
--model "models-catalog/catalog/images/model_15.jpg" \
--brand-profile "Nike" \
--prompt-profile "Master_prompt_05_Conversion" \
--aspect-ratio "4:5" \
--output "ad-output.png"
COMFY_DEPLOY_API_KEY="$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:
Endpoint: https://api.comfydeploy.com/api/run/deployment/queue
Deployment ID: e37318e6-ef21-4aab-bc90-8fb29624cd15
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 |
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 palettels ~/clawd/ad-ready/configs/Brands/*.json | sed 's/.*\///' | sed 's/\.json//'
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.
| 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:
| 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 |
Models for talent/face reference: ~/clawd/models-catalog/catalog/
Priority: User-provided model > Catalog selection > No model (product-only ad)
| 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) |
Uses ComfyDeploy API key. Set via COMFY_DEPLOY_API_KEY environment variable.
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
name: 4d-compression-core version: 1.0.2 description: "把长内容压缩成结构化向量——节省 60-80% Token,保留核心信息" metadata: { "openclaw": { "emoji": "🌀", "requires": { "bins": ["jq", "awk"] }, "triggers": ["压缩", "4d",...
中文简介正在准备中。当前显示原文说明。
Use cheap, TEE-verified AI models from the 0G Compute Network as OpenClaw providers. Discover available models and compare pricing vs OpenRouter, verify provider integrity via hardware attestation (Intel TDX), manage your 0G wallet and sub-accounts, and configure models in OpenClaw with one workflow. Supports DeepSeek, GLM-5, Qwen, and other models available on the 0G marketplace.
中文简介正在准备中。当前显示原文说明。
Send and receive P2P messages using disposable numbers and PINs. No servers, no accounts. Use for human notifications, approval flows, and agent-to-agent communication.
中文简介正在准备中。当前显示原文说明。
Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user asks about crypto market data, orderbooks, trades, funding rates, or historical prices on Hyperliquid, Lighter.xyz, or HIP-3.
中文简介正在准备中。当前显示原文说明。
Find and complete paid tasks on the 0xWork decentralized marketplace (Base chain, USDC escrow). Use when: the agent wants to earn money/USDC by doing work, discover available tasks, claim a bounty, submit deliverables, check earnings or wallet balance, or set up as a 0xWork worker. Task categories: Writing, Research, Social, Creative, Code, Data. NOT for: posting tasks (use the website), managing the 0xWork platform, or frontend development.
中文简介正在准备中。当前显示原文说明。
Patterns and practices that dramatically accelerate development velocity. Covers parallel execution, automation, feedback loops, workflow optimization, and anti-pattern avoidance. Use when starting projects, planning sprints, optimizing workflows, or onboarding developers.
中文简介正在准备中。当前显示原文说明。