Amazon POD Design Generator
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.
here is the API you need to use for image recognition which is from chatgpt vision 2.1 and this is the code snippet
curl --request POST \
--url https://chatgpt-vision1.p.rapidapi.com/matagvision21 \
--header 'Content-Type: application/json' \
--header 'x-rapidapi-host: chatgpt-vision1.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd' \
--data '{"messages":[{"role":"user","content":[{"type":"text","text":"what'\''s in the image"},{"type":"image","url":"https://raw.githubusercontent.com/rphrp1985/mycodes/main/matagimage.png"}]}],"web_access":false}'
this is the code snippet and API for real time amazon URL or ASIN extractor
curl --request GET \
--url 'https://real-time-amazon-data.p.rapidapi.com/product-details?asin=B07ZPKBL9V&country=US' \to
--header 'x-rapidapi-host: real-time-amazon-data.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd'
here is the search trademark to make sure the listing text or image is not trademarked and we can use it for creating a new listing. I am providing you the snippet and API
curl --request GET \
--url https://uspto-trademark-search-api.p.rapidapi.com/google/namesearch/1/10 \
--header 'x-rapidapi-host: uspto-trademark-search-api.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd'
for ideogram API with snippets
curl -X POST https://api.ideogram.ai/remix \
-H "Api-Key: penEs2TUqS98IfthOgjX4QLs4IjPRzBLgPp4MOPLsXE7isV6ivlT2OMX7RVYhUjvGPI1ALf0tQko-3uVKnw1AA" \
-H "Content-Type: multipart/form-data" \
-F image_request='{
"prompt": "",
"aspect_ratio": "ASPECT_1_1",
"image_weight": 95,
"magic_prompt_option": "OFF",
"model": "V_2",
"num_images": 6,
"style_type": "DESIGN",
"negative_prompt": "no t-shirt mockup, no blur, no low quality, no background"
}' \
-F image_file=@"Screenshot tshirt.png;type=image/png"
here is ai-image-upscaler snippet and API
curl --request POST \
--url https://ai-image-upscaler1.p.rapidapi.com/v1 \
--header 'Content-Type: multipart/form-data' \
--header 'x-rapidapi-host: ai-image-upscaler1.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd' \
--form image=a-black-t-shirt-with-a-colorful-text-des_CPtyFhCdQIykXQB_CnJAkQ_R6V4EV_eSbqgOrHZfnkO2A.png
here is a background removal with its API and code snippets:
curl --request POST \
--url https://ai-background-remover.p.rapidapi.com/image/matte/v1 \
--header 'Content-Type: multipart/form-data' \
--header 'x-rapidapi-host: ai-background-remover.p.rapidapi.com' \
--header 'x-rapidapi-key: 0dea502dd6mshefa43cb00153aedp1b2cdejsn596b802861cd' \
--form image=in-a-vivid-circular-illustration-the-phr_xcVsx4evRsWPOcmSvE2rqg_MVOd8NgcQn2G0exmjbLACw.png
I need all image to be saved with transparent background and .png format. it also needs to be 300 dpi 4500 x 5400 pixels
here is merch on demand policy guidelines
Merch by Amazon Use Policy: Key Aspects for Design Content
Merch by Amazon has a Content Policy that outlines what types of designs are permitted on their platform. It's essential to adhere to this policy to avoid having your designs rejected or your account suspended. Here are the critical points:
No Infringement of Intellectual Property Rights:
Copyright: You cannot use copyrighted material in your designs without proper authorization. This includes images, text, logos, and characters from books, movies, TV shows, video games, etc.
Trademark: You cannot use trademarks in your designs without authorization. This includes brand names, slogans, and logos. You need to conduct thorough trademark research before creating designs. Using a trademark checker API is recommended.
Patent: You cannot use patented material in your designs without permission.
Right of Publicity: You cannot use the likeness, image, or name of a celebrity or public figure without permission.
No Offensive, Abusive, or Illegal Content:
Hate Speech: Designs that promote hatred, discrimination, or violence against individuals or groups based on race, ethnicity, religion, gender, sexual orientation, disability, etc., are prohibited.
Violence: Designs that promote or glorify violence are not allowed.
Illegal Activities: Designs that promote or condone illegal activities (e.g., drug use, terrorism) are prohibited.
Obscene Content: Designs that are sexually explicit, promote child exploitation, or are otherwise obscene are not allowed.
Bullying/Harassment: Designs that target individuals for harassment or bullying are prohibited.
Accurate and Honest Representation:
Misleading Claims: Designs should not make false or misleading claims about products or services.
Deceptive Practices: Designs should not engage in deceptive practices, such as creating fake reviews or using misleading advertising.
Content Guidelines Specific to Events:
For designs related to current events (e.g., holidays, political events), you must ensure they do not exploit, trivialize, or lack reasonable sensitivity toward tragic or controversial events.
Amazon Brand Assets:
You cannot use Amazon's trademarks, logos, or other brand assets without express permission.
Content Policy Enforcement:
Amazon has the right to review and remove any design that violates their content policy.
Repeated violations can lead to account suspension or termination.
Amazon may also cooperate with law enforcement in cases of illegal activity.
Responsibility:
You are solely responsible for ensuring your designs comply with Amazon's content policy.
Practical Steps to Ensure Compliance
Research, Research, Research: Before creating any design, thoroughly research potential trademark and copyright issues.
Use Trademark Search Tools: Utilize tools like the USPTO website or trademark checker APIs to identify potential conflicts.
Read the Fine Print: Carefully review the terms of any images, fonts, or other design elements you use to ensure you have the necessary licenses.
Err on the Side of Caution: If you're unsure whether a design might violate the content policy, it's best to avoid using it.
Stay Updated: Amazon's content policy may change, so it's essential to stay informed of any updates.
extract file as ZIP file
1. What format should the final design package be delivered in? (ZIP file, individual files, etc.) Deliver as ZIP file package.
2. Should the agent provide multiple design variations or just one final design? agent should provide 2 design variations
3. What specific dimensions/formats are needed beyond the mentioned 4500x5400px PNG? the ratio of each image should be 1x1
4. Should the agent provide a compliance report detailing trademark checks performed? the compliance report detail should provide which keywords are trademarked and needs to be changed, but this should not stop creating the listing.
5. Does the user need the listing copy in a specific format (CSV, txt, etc.)? I need the listing copy in CSV and txt format.
6. Should color variations of the final design be generated? NO
7. Are there specific marketplaces/niches to focus on or avoid? NO
8. Should the agent maintain a log of all trademark checks for future reference? YES
b) Required Inputs:
1. Source URL (Amazon/Etsy) of the original design to analyze. Example of URL https://www.amazon.com/Disney-Timon-Achin-Graphic-T-Shirt/dp/B07HYY5RK7/?_encoding=UTF8&pd_rd_w=uF2B1&content-id=amzn1.sym.a58c4a6b-f441-4c49-bfc9-49006c8fd49e&pf_rd_p=a58c4a6b-f441-4c49-bfc9-49006c8fd49e&pf_rd_r=9D9VS3FGPRZN4P5H5KPE&pd_rd_wg=vM57m&pd_rd_r=350324e3-5280-4bb0-8ce0-b10b2b7dd409&ref_=pd_hp_d_atf_dealz_cs&customId=B0752XJYNL&customizationToken=MC_Assembly_1%23B0752XJYNL&th=1
example of ASIN: B07HYY5RK7
2. Target marketplace (if different from source)
3. Preferred design style/theme (if any specific preferences)
4. Color preferences or restrictions. NO
5. Any specific keywords to include/avoid. avoid the word T-shirt in the title. Avoid for example: great for THE NAME OF ANY HOLIDAY. or this is perfect for any THE NAME OF HOLIDAY.
6. Budget constraints for API calls (as multiple APIs are being used) provide a report of how many API calls have been used, by adding total of each call for the month. A month should be considered from the day it starts for the next 30 days.
7. Any specific niche-targeting preferences T-shirt
8. The preferred text style for listings (casual, professional, etc.) is similar to the design URL provided.
c) Task Segmentation Suggestions:
I believe this task would benefit from being segmented into three main operational silos:
1. Content Analysis & Compliance Silo:
- Trademark verification system
- Copyright check system
- Amazon policy compliance checker
- Text content analyzer
2. Design Generation Silo:
- Image analysis system
- Design generation system
- Image optimization pipeline (resolution, background removal, upscaling)
- Quality control system
3. Listing Creation Silo:
- Keyword research and optimization
- Description generator
- Bullet point creator
- Title optimizer
listing TITLE should not be greater then; 60 characters
2 BULLET POINTS each should not be greater then: 200 characters
listing Description should not be greater then: 1000 characters</h3>
<br>
<h1>First, pre-entered info...</h1>
<br>
<h3>final-output2</h3>
<textarea>• One high-resolution PNG image file (transparent background, 300 dpi, 4500 × 5400 pixels) representing the new t-shirt design.
• A text file containing the finalized listing details (title, bullet points, description) that do not include trademarked or infringing terms and comply with Amazon's guidelines.</textarea>
<br>
<h3>subagents-extracted2</h3>
<textarea>OVERVIEW OF SUBAGENTS
Subagent 1: "Listing Data Extractor"
Subagent 2: "Keyword & Image Analysis"
Subagent 3: "Design Generator & Processor"
Subagent 4: "Listing Text Creator"
SUBAGENT 1: "Listing Data Extractor"
• Final Output: Structured data containing the original title, bullet points, description, and main image URL.
• Skills & Process:
1) #225 (if Amazon) or #226 (if Etsy/other) – Extract structured data from the provided URL (title, bullet points, description, main image).
2) Directly provide a single structured output (no additional summarization skill needed, as it only returns one cohesive object).
SUBAGENT REQUIRED INPUT: {listing-url}
SUBAGENT FINAL OUTPUT: [extracted-listing-data]
SUBAGENT 2: "Keyword & Image Analysis"
• Final Output:
– A single structured data object containing:
1) A safe list of non-trademarked keywords.
2) A descriptive summary of the original shirt design (colors, style, and patterns).
• Skills & Process:
1) #176 – Analyze the main image to describe its colors, style, and patterns; produce "ImageDescription."
2) #223 – Identify and remove trademarked/infringing terms from the listing text; produce "SafeKeywords."
3) #223 (Powerful LLM Prompt-to-Text Response) – Summarize "ImageDescription" and "SafeKeywords" into one unified "Subagent2Output," to keep everything as a single final output file.
SUBAGENT REQUIRED INPUT: [extracted-listing-data]
SUBAGENT FINAL OUTPUT: [safe-keywords-and-image-description]
SUBAGENT 3: "Design Generator & Processor"
• Final Output: A new PNG image (transparent background, 300 dpi, 4500 × 5400).
• Skills & Process:
1) #222 – Generate a new design using the safe keywords and the style description from Subagent 2. The design should have a transparent background where possible.
2) #191 – Resize the generated image to 4500 × 5400 pixels at 300 dpi.
(No additional summarizing skill is needed here, as this subagent's final output is a single PNG file.)
SUBAGENT REQUIRED INPUT: [safe-keywords-and-image-description]
SUBAGENT FINAL OUTPUT: [generated-tshirt-image]
SUBAGENT 4: "Listing Text Creator"
• Final Output: A text file containing the new title, bullet points, and description, free of infringing terms.
• Skills & Process:
1) #190 – Draft Amazon-compliant listing text (title, bullet points, description) using the safe keywords.
2) #223 (Powerful LLM Prompt-to-Text Response) – Combine the newly drafted title, bullet points, and description into one cohesive text output, and confirm there are no trademarked terms remaining.
SUBAGENT REQUIRED INPUT: [safe-keywords-and-image-description]
SUBAGENT FINAL OUTPUT: [final-listing-text]
</textarea>
<br>
<h3>subagentnumber</h3>
<textarea>[subagentnumber]</textarea>
<h1>Then, agent response...</h1>
<br><br>
<h3>subagent1refined-new-json-core</h3>
<textarea>I'll create the G) TASKFLOW SUMMARY section for SUBAGENT 1 based on the skills/tasks outlined in the previous sections.
G) TASKFLOW SUMMARY
1. Domain Analysis Task
- ID: #223
- Title task: Analyze URL Domain Type
- Description: Analyzes the provided URL to determine if it's an Amazon listing or another marketplace, setting up the appropriate extraction path
- Input(s): {listing-url}
- Output token name: [domain-type]
2. Amazon Data Extraction Task
- ID: #225
- Title task: Extract Amazon Product Listing Data
- Description: Crawls Amazon product page to extract structured data including title, bullet points, description, and main product image URL
- Input(s): {listing-url}, [domain-type]
- Output token name: [raw-amazon-data]
3. Non-Amazon Data Extraction Task
- ID: #226
- Title task: Extract Non-Amazon Product Listing Data
- Description: Extracts structured data from non-Amazon marketplace URLs, including title, bullet points, description, and main product image URL
- Input(s): {listing-url}, [domain-type]
- Output token name: [raw-other-data]
4. Data Structuring Task
- ID: #223
- Title task: Structure Extracted Listing Data
- Description: Converts raw extracted data into standardized JSON format with specified fields (title, bullet_points, description, main_image_url)
- Input(s): [raw-amazon-data] OR [raw-other-data]
- Output token name: [extracted-listing-data]
The complete workflow follows this sequence:
{listing-url} → [domain-type] → ([raw-amazon-data] OR [raw-other-data]) → [extracted-listing-data]
Each task builds upon the previous one, ultimately producing a structured data object containing all necessary listing information for subsequent subagents to process.</textarea>
<br>
<h3>subagent1mermaid</h3>
<textarea>
<!DOCTYPE html>
<html>
<head>
<title>Mermaid Diagram Example
graph TD
A[Input: listing-url] --> B[Domain Analysis Task]
B --> C{Is Amazon?}
C -->|Yes| D[Amazon Data Extraction]
C -->|No| E[Non-Amazon Data Extraction]
D --> F[Raw Amazon Data]
E --> G[Raw Other Data]
F --> H[Data Structuring Task]
G --> H
H --> I[Final Output: extracted-listing-data]
subgraph Data Fields
I --> J[title]
I --> K[bullet_points]
I --> L[description]
I --> M[main_image_url]
end
subgraph Task IDs
B -.->|#223| N[Domain Check]
D -.->|#225| O[Amazon Crawler]
E -.->|#226| P[Generic Crawler]
H -.->|#223| Q[JSON Formatter]
end