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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<h1>First, pre-entered info...</h1>
<br>
<h3>final-output2</h3>
<textarea>The final output of this entire process is a new T-shirt design (as an image file, such as PNG) combined with new listing text (title, bullet points, description).
1. What format should the final deliverables be in? (ZIP file with image + text file)
2. Should the new design maintain any specific elements from the original (like color scheme, theme) or be completely different while staying in the same niche? the new design should have same theme, same text, same niche
3. What resolution/dimensions are required for the final image? 300 dpi resolution with 4500 pixels wide by 5400 pixels tall
4. How many variations of the design should be generated for user selection? 12
5. Should the agent provide a confidence score for trademark compliance? yes
6. Should the workflow generate multiple listing copy variations for A/B testing? yes
7. Does the user need a detailed report of the trademark checking process for documentation? no
8. Should the agent provide category/niche recommendations for listing optimization? no</textarea>
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<h3>subagents-extracted2</h3>
<textarea>OVERVIEW OF SUBAGENTS
1) DataExtractionSubagent (DataExtractor)
2) TrademarkAnalysisSubagent (BrandGuard)
3) TShirtDesignSubagent (DesignGenerator)
4) ListingCreationSubagent (ListingCreator)
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]
SUBAGENT 2: TRADEMARKANALYSISSUBAGENT (BrandGuard)
• Final Output: A concise report on trademark risks in text and a note on whether the image might contain trademark elements.
• How It Works:
1. Receive the extracted keywords/phrases from the text data.
2. Use "#224 - Oracle Ask A Question" Skill to check if these terms are trademarked by searching "[term] trademark."
3. Receive the original image URL and use "#176 - Analyze An Image With GPT Vision & Return Text" Skill to identify visible trademarked logos or brand references.
4. Summarize any flagged trademark risks in a short text report.
SUBAGENT REQUIRED INPUT: [extracted-data]
SUBAGENT FINAL OUTPUT: [trademark-analysis]
SUBAGENT 3: TSHIRTDESIGNSUBAGENT (DesignGenerator)
• Final Output: One newly generated T-shirt design image (PNG/JPG).
• How It Works:
1. Take trademark-safe keywords and style guidelines.
2. Use "#223 - Powerful LLM Prompt-to-Text Response" to refine or create a T-shirt design prompt avoiding flagged trademarks.
3. Use "#222 - Make Image (Especially With Text)" Skill with the generated prompt to produce a new T-shirt design image.
4. Return the single image file (URL to the PNG/JPG) as the output.
SUBAGENT REQUIRED INPUT: [extracted-data], [trademark-analysis]
SUBAGENT FINAL OUTPUT: [new-shirt-design]
SUBAGENT 4: LISTINGCREATIONSUBAGENT (ListingCreator)
• Final Output: Revised listing text plus a final compliance note.
• How It Works:
1. Take the safe keywords and guidelines.
2. Use "#190 - Write or rewrite text based on instructions" Skill to create a new title, bullet points, and description.
3. Use "#223 - Powerful LLM Prompt-to-Text Response" to confirm the listing's compliance with Amazon's rules, providing a final note on any issues.
4. Return the revised listing text as { "title": "...", "bullets": [...], "description": "..." } along with the compliance note.
SUBAGENT REQUIRED INPUT: [extracted-data], [trademark-analysis]
SUBAGENT FINAL OUTPUT: [revised-listing-text]
1. URL of the original shirt design (Amazon or Etsy) https://www.amazon.com/Dog-Treat-Dealer-Owner-T-Shirt/dp/B0CZYWZCM7/ref=sr_1_5?crid=211NDX6URH0UU&dib=eyJ2IjoiMSJ9.bcy8SS7ImScyYsPp16bMZc8uuPN9tK-UpHlHSdY2K6zD_PTBie1OVWjYwsf_8h7GymjEHlqaBBc0mP0SIyQCSu6Q0pDgDiY9Q0sCOv-2dAdQ8g5_wnqKDEXekf00i5xstTOWrEtEj4poXNBtRbB7HXnO0_pfnnBi11mCq9LkojHohkg7DtLj2xjcslTmGUPWe3li617Zpso51_b19mpv-7Mw_oIB29OsYy09t6mqzCXIUyhdCk9fqJcLXj_lXGCWv4wSYdSLQ7jzxwAeOgCh_m7jyEsysMUhXN36PaUdLbnkdGrOqRwQ8Y76ig3QN34LEpA4_7LKMccOfAA3Q06xXaIgzmlBmQj4LM_y84ACf9yenjJdNgNr6i9byXUD8ax0_bBFCOsRLQIlqjIoIig5LLmonOLzbl2es3HRoR5z0bCOXMyNj7rGxtOmbt2VXbFH.v4QfKyq1vqZZFwkovzVLFzP_KWhvjp1ZoDnL3vOP11A&dib_tag=se&keywords=funny+dog+tshirts&qid=1741585555&sprefix=funny+dog+tshirt%2Caps%2C121&sr=8-5
2. Target marketplace (Amazon POD specifically)
3. Preferred color scheme (if any) no
4. Design style preferences (minimalist, detailed, etc.) follow the design from amazon link
5. Any specific elements to avoid. NO
6. Target audience/demographic. Boys, Girls, Women, Men, Kids
7. Price point (to align marketing copy) 15.97
8. Preferred image generation service API key/access ideogram.com API key key penEs2TUqS98IfthOgjX4QLs4IjPRzBLgPp4MOPLsXE7isV6ivlT2OMX7RVYhUjvGPI1ALf0tQko-3uVKnw1AA
What length and complexity are expected for the new title, bullet points, and descriptions? Are there any specific format or style templates to follow? title should be 80 characters, bullet points each should be 200 characters, and description should be 1000 characters.
Specific Amazon print-on-demand rules that the user wants to prioritize. here are the URL for the amazon rules https://merch.amazon.com/resource/201858630
https://merch.amazon.com/resource/6GWVH5S3QRFAJ59
1. Content Extraction Pipeline:
- HTML parsing for Amazon
- HTML parsing for Etsy
- Image URL extraction
- Text content extraction
2. Trademark Verification System:
- Text-based trademark checking
- Image-based trademark checking
- Brand registry database checking
- Common phrase verification
3. Design Generation Pipeline:
- Prompt engineering
- Image generation
- Quality control
- Format verification
4. Listing Copy Generation:
- Title optimization
- Bullet point creation
- Description writing
- Keyword optimization</textarea>
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<h3>subagentnumber</h3>
<textarea>[subagentnumber]</textarea>
<h1>Then, agent response...</h1>
<br><br>
<h3>subagent1refined-new-json-core</h3>
<textarea>I'll add the G) TASKFLOW SUMMARY section to SUBAGENT 1 (DataExtractor), maintaining all previous sections and adding the new detailed taskflow:
--------------
SUBAGENT 1 - COMPLETE SUMMARY:
A) SUBAGENT SUMMARY
A data extraction agent that processes Amazon/Etsy product listings to extract and structure key product information.
B) FINAL TASK OUTPUT
A JSON-like structure in this exact format:
{
"title": "...",
"bullets": ["..."],
"description": "...",
"image_url": "..."
}
C) SUBAGENT INPUT
{productListingUrl}
E) SUBAGENT TASK SUMMARY
1) Use Skill #226 - Extract Structured Data From 1x URL
• Input: {productListingUrl}, plus instruction for extraction
• Output: Structured text with extracted information
2) Use Skill #223 - Powerful LLM Prompt-to-Text Response
• Input: Output from Step 1, plus JSON formatting directive
• Output: Final JSON-like structure
F) SILOS
• Silo 1: Data Extraction
- Step 1: (#226) Crawl and extract structured data
• Silo 2: JSON Formatting
- Step 2: (#223) Format data into JSON structure
G) TASKFLOW SUMMARY
Task 1:
- ID: #226
- Title task: Extract Structured Product Data from URL
- Description: Crawls the provided Amazon/Etsy product URL to extract title, bullet points, description, and main product image URL using specialized parsing techniques for e-commerce platforms
- Input(s) involved: [productListingUrl]
- Output token name: [raw_structured_data]
Task 2:
- ID: #223
- Title task: Format Extracted Data into Standardized JSON Structure
- Description: Takes the raw structured data and transforms it into a clean, standardized JSON format with specific keys for title, bullets, description, and image URL
- Input(s) involved: [raw_structured_data]
- Output token name: [formatted_json_output]
These tasks form a sequential pipeline where the output token [raw_structured_data] from Task 1 becomes the input for Task 2, ultimately producing the [formatted_json_output] as the final deliverable.</textarea>
<br>
<h3>subagent1mermaid</h3>
<textarea>
<!DOCTYPE html>
<html>
<head>
<title>Mermaid Diagram Example
graph TD
Start[Start: Receive Product URL] --> URLValidation{URL Valid?}
URLValidation -->|No| ErrorHandler[Return Error Message]
URLValidation -->|Yes| Task1[Task #226: Extract Structured Data]
Task1 --> ParseTitle[Parse Product Title]
Task1 --> ParseBullets[Parse Bullet Points]
Task1 --> ParseDescription[Parse Description]
Task1 --> ParseImage[Extract Image URL]
ParseTitle --> ValidateTitle{Title Valid?}
ParseBullets --> ValidateBullets{Bullets Valid?}
ParseDescription --> ValidateDesc{Description Valid?}
ParseImage --> ValidateImage{Image URL Valid?}
ValidateTitle -->|No| RetryTitle[Retry Title Extraction]
ValidateBullets -->|No| RetryBullets[Retry Bullets Extraction]
ValidateDesc -->|No| RetryDesc[Retry Description Extraction]
ValidateImage -->|No| RetryImage[Retry Image Extraction]
RetryTitle --> ValidateTitle
RetryBullets --> ValidateBullets
RetryDesc --> ValidateDesc
RetryImage --> ValidateImage
ValidateTitle -->|Yes| CombineData[Combine Extracted Data]
ValidateBullets -->|Yes| CombineData
ValidateDesc -->|Yes| CombineData
ValidateImage -->|Yes| CombineData
CombineData --> Task2[Task #223: Format JSON Structure]
Task2 --> ValidateJSON{JSON Format Valid?}
ValidateJSON -->|No| RetryJSON[Retry JSON Formatting]
RetryJSON --> Task2
ValidateJSON -->|Yes| Output[Return Final JSON Output]
ErrorHandler --> End[End Process]
Output --> End