POD Design from Etsy Listing
Initial Research on Guidelines
The agent begins by familiarizing itself with Amazon's print-on-demand guidelines. This involves performing a web search to locate relevant documentation and using the browse_page action to read the content, ensuring understanding of rules such as content restrictions, design originality, and listing requirements.
This step is crucial to ensure all subsequent actions comply with platform policies, which may include avoiding copyrighted material and ensuring designs meet quality standards.
Input and Data Extraction
The user provides a URL linking to a shirt design on Amazon or Etsy. The agent uses the browse_page action to fetch the webpage content.
It then parses the HTML to extract key elements:
Title, typically found in the tag or specific divs.
Bullet points and description, often located in designated sections of the page.
The main image URL, essential for design analysis.
This parsing requires understanding the HTML structure, which may vary between Amazon and Etsy, necessitating flexible parsing techniques like CSS selectors or XPath.
Trademark Check on Original Listing
To identify potential trademarked elements, the agent performs two checks:
Text Analysis: Extracts keywords and phrases from the title, bullet points, and description. For each, it uses the web_search action to query "[keyword] trademark" to determine if the term is registered, flagging any hits associated with known brands.
Image Analysis: Conducts a reverse image search by navigating to a site like Google Images (Google Images) using browse_page, inputs the image URL, and parses results to see if the image or similar ones are linked to trademarked designs, such as branded logos or patterns.
This step ensures the agent avoids replicating protected elements in the new design.
Keyword Extraction and Image Description
From the extracted text, the agent filters out potential trademarked terms identified in step 3, retaining non-trademarked keywords that describe the design (e.g., "vintage," "graphic tee").
It also analyzes the original image to describe its style, colors, and patterns in text, preparing for image generation. This description might note, for example, "red shirt with abstract geometric patterns."
Generation of New Design
Image Generation: The agent crafts a prompt combining the non-trademarked keywords and image description (e.g., "generate a red shirt with abstract geometric patterns, inspired by vintage style"). It then uses an online text-to-image generation tool, accessible via browse_page, such as DreamStudio (DreamStudio). It interacts with the site to input the prompt, retrieves the generated image URL, and ensures the design is similar but distinct from the original.
Text Generation: Creates new title, bullet points, and description using the keywords, ensuring no trademarked terms are included and aligning with Amazon's guidelines (e.g., clear, descriptive, and non-misleading text).
Final Trademark and Compliance Check
The agent repeats the trademark check on the new text and image:
Searches for each new keyword with "trademark" to ensure no issues.
Performs another reverse image search on the new image to confirm it doesn't resemble any trademarked designs.
It also verifies the new design complies with Amazon's print-on-demand rules, such as avoiding offensive content or ensuring high resolution.
User Approval and Delivery
The agent presents the new design (image and text) to the user for approval, likely through an interface displaying both elements.
Upon approval, it provides options to download the image and copy the text, facilitating integration into the user's workflow, such as listing on Amazon.
Challenges and Considerations
Image Generation Complexity: Interacting with text-to-image tools via browse_page requires simulating user actions, which may be limited by website interfaces. The agent must handle variations in tool functionality, potentially requiring multiple attempts to generate a suitable image.
Trademark Verification: Determining trademark status is complex, as it involves legal nuances beyond simple web searches. The agent's checks (keyword searches, reverse image searches) are rudimentary and may not capture all issues, necessitating user discretion.
Platform Variability: Amazon and Etsy pages have different HTML structures, requiring robust parsing strategies to extract consistent data.
Surprising Detail: Extensive Web Interaction: The need for reverse image searches and image generation via external websites adds significant complexity, as the agent must navigate and parse dynamic web content, a task not typically associated with simple AI automation.</h3>
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<h3>subagent1</h3>
<textarea>SUBAGENT 1: DATAEXTRACTIONSUBAGENT (DataExtractor)
• Final Output: A JSON-like structure with { "title": "...", "bullets": ["..."], "description": "...", "image_url": "..." }.
• How It Works:
1. Receive the single product listing URL as input.
2. Use "#226 - Extract Structured Data From 1x URL" Skill with instruction: "Extract title, bullet points, description, main image URL."
3. Produce the structured text with the requested fields.
SUBAGENT REQUIRED INPUT: {productListingUrl}
SUBAGENT FINAL OUTPUT: [extracted-data]</textarea>
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<h3>subagentX-refined</h3>
<textarea>[subagent1-refined]</textarea>
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<h3>subagentXmermaid</h3>
<textarea>graph TD
A[Start: Receive Product URL] --> B[Data Extraction Phase]
B --> C[Browse Page: Skill #226]
C --> D{Is URL Valid?}
D -->|No| E[Return Error]
D -->|Yes| F[Extract Raw Data]
F --> G[Parse Title]
F --> H[Parse Bullets]
F --> I[Parse Description]
F --> J[Parse Image URL]
G & H & I & J --> K[Formatting Phase]
K --> L[Apply Skill #223]
L --> M[Structure JSON Output]
M --> N[Validate JSON Format]
N --> O{Is JSON Valid?}
O -->|No| P[Fix JSON Structure]
P --> N
O -->|Yes| Q[Return Final JSON]
Q --> R[End: Data Ready]
E --> S[End: Error State]</textarea>
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<h3>https://static.aiz.ac/1741763170-mermaid/mermaid-1.png</h3>
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