Cut out news website XY

I want to research and build a database of news websites, take screenshots of the sites, and then cut out x/y co-ordinates of the most important, updated d and/or newsworthy parts of the website (eg above the fold, there might be a "TOP NEWS" container which is at x=100,y300 to x=500,y=900). I will enter a single keyword topic, then the Agent will research news websites on the topic, and generate a shortlist of possible URLs (eg "10x top news websites about xyz"). That shortlist will then be filtered down by taking a screenshot of each website and looking to see if there is a rectangular x/y shape (eg a "TOP NEWS" or "LATEST" container) that can be crawled. This container should be in as aspect ratio shape of between 1:3 and 3:1. We will then crop out the x/y co-ordinates of this image and save it to our database. We will then categorise all of these websites (perhaps by looking at the similarweb traffic rank, keyword research for relevance, and perhaps crawling the text content of the page, then passing all this info to an LLM to give the site a final title, description and perhaps 1-5 related keywords and 6-20 secondary keywords). Finally, this database, containing the original full screenshot image, the x/y cropped image, final title, descrirption, related keywords (comma separated) and secondary keywords (comma separated) will be saved in a CSV file, ready to be exported.

Sub Agent #1 | Sub Agent #2 | Sub Agent #3

final-output-comps..1

Based on the task description you provided, here are the answers to your questions:

a) The final output of this task would be a CSV file containing the following information for each relevant news website:
   - Original full screenshot image (likely in PNG or JPEG format)
   - Cropped image of the "TOP NEWS" or "LATEST" container (also in PNG or JPEG format)
   - Final title of the website
   - Description of the website
   - Related keywords (1-5, comma-separated)
   - Secondary keywords (6-20, comma-separated)

b) The core components/outputs needed to generate the final output are:
   1. A database or list of news websites relevant to the given keyword topic. This could be a simple text file containing the URLs of the websites.
   2. Screenshots of each website in the database. These would be image files (PNG or JPEG) capturing the entire visible portion of the website.
   3. Cropped images of the "TOP NEWS" or "LATEST" containers from each website. These would also be image files (PNG or JPEG) containing only the relevant rectangular portion of the website, with an aspect ratio between 1:3 and 3:1.
   4. Metadata for each website, including:
      - Final title (text)
      - Description (text)
      - Related keywords (1-5, comma-separated text)
      - Secondary keywords (6-20, comma-separated text)
      This metadata would likely be generated using a combination of web scraping (for relevance and content analysis) and LLM processing (for generating the final title, description, and keywords).
   5. A script or program to combine all the above components into a single CSV file. This script would need to:
      - Load the original full screenshot image and cropped image for each website
      - Load the metadata (title, description, keywords) for each website
      - Write all this information to a CSV file, with one row per website and columns for each piece of data (full image filename, cropped image filename, title, description, related keywords, secondary keywords)

So in summary, the core components are the initial database of websites, the screenshot images (full and cropped), the metadata for each website, and a script to combine everything into a final CSV file.

final-output-comps..2

## Understanding the Final Output and Components

### a) Final Output

The final output of this automated workflow would be a **CSV file** containing a structured database of information about news websites relevant to a specific keyword topic. Each row in the CSV would represent a single news website and contain the following information:

* **Original Screenshot:** The full screenshot of the news website homepage. (Image file)
* **Cropped Image:** The cropped image of the "TOP NEWS" or similar section based on identified x/y coordinates. (Image file)
* **Title:** A concise and informative title for the news website based on its content and focus.
* **Description:**  A brief description summarizing the news website's purpose and target audience.
* **Related Keywords:** A comma-separated list of 1-5 keywords that are highly relevant to the website's content.
* **Secondary Keywords:** A comma-separated list of 6-20 additional keywords that describe the website's content in more detail.

This CSV file could be used for various purposes, including:

* **Content aggregation platform:** To automatically curate and display relevant news articles based on user interest.
* **News analysis tool:** To track trends and identify patterns in news coverage across different websites.
* **Competitive research:** To understand the landscape of news websites within a specific niche.

### b) Components and Outputs

To achieve the final output, the workflow can be broken down into several key components:

**1. Research and Shortlisting:**

* **Input:** A single keyword topic provided by the user.
* **Output:** A list of URLs for the top 10 news websites related to the keyword. This could be achieved by utilizing search engine APIs or web scraping techniques.

**2. Screenshot and Cropping:**

* **Input:** The list of URLs from the previous step.
* **Outputs:** 
    * Full-page screenshots of each website's homepage. 
    * Cropped images of the "TOP NEWS" or relevant sections based on identified x/y coordinates. This would require image processing libraries and potentially object detection algorithms to locate the desired content area.

**3. Website Categorization:**

* **Inputs:** 
    * The full screenshot and cropped image for each website.
    * Data from SimilarWeb or similar tools for traffic ranking.
    * Keyword analysis for relevance to the original topic.
    * Text content extracted from the website.
* **Outputs:** 
    * A title for the website.
    * A brief description.
    * A list of 1-5 related keywords.
    * A list of 6-20 secondary keywords.
    * This step would likely involve using an LLM to analyze the various inputs and generate human-readable text summaries and keywords.

**4. Database Creation and Export:**

* **Inputs:** All the outputs from the previous steps for each website.
* **Output:** A CSV file containing the structured database as described in section a).

**Additional Considerations:**

* **Error Handling:** The workflow should include mechanisms to handle potential errors, such as websites being unavailable or failing to load correctly.
* **Scalability:** The workflow should be designed to accommodate large numbers of websites and potentially be parallelized for efficiency.
* **Data Storage:**  Consider where the images and CSV file will be stored and how they will be accessed for future use.

By understanding these components and outputs, you can effectively design and implement an AI agent-driven workflow to achieve your desired outcome.

final-output-comps GPT..3

a) The final output of this task is a CSV file that stores a well-categorized database of select news websites relevant to a specific keyword topic. This database contains the original screenshot of each website, a separate image that is the cropped screenshot based on specified x/y coordinates, the final title of the site, a description, a list of related keywords (comma-separated), and a list of secondary keywords (comma-separated).

b) The final output requires several direct components and processes:

1. **Keyword Input**: The user-provided keyword that serves as the basis for researching and filtering relevant news websites.

2. **Internet Research and URL Generation**: An AI capable of researching and generating a URL shortlist based on the given keyword input. This includes identifying and prioritizing top news websites relevant to the keyword.

3. **Website Screenshot Capture**: An automated tool or AI capability to take and store screenshots of each website on the generated shortlist.

4. **Image Analysis and Cropping**: AI capacity for image analysis to identify a rectangular x/y shape that fits within specified parameters (a "TOP NEWS" or "LATEST" container, a specific aspect ratio between 1:3 and 3:1), then crop out the x/y coordinates of this image for storage in the database.

5. **Website Analysis and Metadata Generation**: The ability for AI to research and categorize each website based on traffic rank, keyword relevancy, and textual content. Natural language processing capabilities are likely needed here to analyze site content and identify keywords.

6. **Use of LLM for Metadata Titling and Categorization**: The AI's interaction with a Language Model (like GPT-3 or a similar tool) to give each site a final title, description, and a set of related and secondary keywords based on the site analysis findings.

7. **Database and CSV File Creation**: Data structuring and csv file creation to compile and categorize all the data collected (website URL, full screenshot, cropped screenshot, final title, description, related keywords, secondary keywords) into a well-organized CSV file ready for export.

Together, these components form a pipeline for automated news website research, categorization, and documentation based on a given keyword topic.

CLAUDE:- final-output-and-component-outputs

a) The final output of this automated workflow would be a CSV file containing a structured database of information about news websites relevant to a specific keyword topic. Each row in the CSV would represent a single news website and include the following data:

1. Original full screenshot image of the website (likely in PNG or JPEG format)
2. Cropped image of the "TOP NEWS" or "LATEST" container based on identified x/y coordinates (also in PNG or JPEG format)
3. Final title of the website
4. Description of the website
5. Related keywords (1-5, comma-separated)
6. Secondary keywords (6-20, comma-separated)

This CSV file could be used for various purposes, such as content aggregation, news analysis, or competitive research.

b) To achieve the final output, the workflow can be broken down into several key components:

1. Keyword Input: A mechanism for the user to provide a single keyword topic for researching news websites.

2. Research and Shortlisting: An AI-powered web scraping tool to search for and generate a shortlist of relevant news websites (e.g., top 10) based on the input keyword. The output would be a list of URLs.

3. Screenshot Capture: A tool to capture full-page screenshots of each website from the shortlist. The output would be a set of screenshot images.

4. Image Analysis and Cropping: An image processing library to analyze the screenshots, identify the presence of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1, and crop the identified container using the x/y coordinates. The output would be a set of cropped images.

5. Website Categorization: An AI-powered system to categorize the websites based on traffic rank (using SimilarWeb or similar tools), keyword relevance (using AI-based keyword extraction), and text content analysis (using web scraping and natural language processing). The output would be a set of relevant information for each website.

6. LLM Integration: An AI language model to generate the final title, description, related keywords, and secondary keywords for each website based on the categorization information.

7. Database Creation and Export: A script to compile all the collected data (full screenshot, cropped image, title, description, keywords) into a structured CSV file.

8. Data Storage: A storage system to store the generated images and CSV file for future reference and use.

Additional considerations include error handling, scalability, and potentially a user interface for inputting the keyword and initiating the process. The workflow would require integration of various programming languages, libraries, and APIs to create a cohesive automated system.

claude 3 - extract final output

extract component outputs

To achieve the final output, the workflow can be broken down into several key components:

1. Keyword Input: A mechanism for the user to provide a single keyword topic for researching news websites.

3. Screenshot Capture: A tool to capture full-page screenshots of each website from the shortlist. The output would be a set of screenshot images.

6. LLM Integration: An AI language model to generate the final title, description, related keywords, and secondary keywords for each website based on the categorization information.

7. Database Creation and Export: A script to compile all the collected data (full screenshot, cropped image, title, description, keywords) into a structured CSV file.

8. Data Storage: A storage system to store the generated images and CSV file for future reference and use.

Claude - research-ideas-and-input-variables

Thank you for providing the detailed task description. Let me address your questions one by one:

a) Clarifying questions for the final output:
1. What specific information should be included in the CSV file? You mentioned the original screenshot, cropped image, title, description, related keywords, and secondary keywords. Are there any other data points you'd like to include?
2. What format should the images be saved in (e.g., PNG, JPEG)?
3. Is there a specific naming convention you'd like to use for the images and the CSV file?
4. Do you have any preferences for the size or resolution of the screenshots and cropped images?
5. Are there any specific requirements for the length of the title, description, and the number of related and secondary keywords?

b) Required inputs before launching the task completion process:
1. The keyword topic for researching relevant news websites.
2. Any specific criteria or preferences for selecting the news websites (e.g., language, region, or reputation).
3. The desired number of websites to include in the final database.
4. The file path or directory where the output CSV file and images should be saved.

c) Potential task segmentation:
1. Researching and shortlisting news websites:
   - Segment the research process into separate tasks for different search engines or sources (e.g., Google News, Bing News, or specific news aggregators).
   - Divide the research by language or region if targeting a specific audience.

2. Analyzing website screenshots:
   - Separate the screenshot capture process from the image analysis process. This allows for potentially using different tools or methods for each task.
   - Consider segmenting the image analysis task into sub-tasks such as identifying the "TOP NEWS" or "LATEST" container, verifying the aspect ratio, and cropping the relevant section.

3. Categorizing and enriching website data:
   - Break down the categorization process into separate tasks for analyzing traffic rank, keyword relevance, and content crawling.
   - Divide the keyword generation task into two distinct steps: generating related keywords and generating secondary keywords.

By segmenting these tasks, you can potentially use specialized tools, APIs, or methods for each sub-task, leading to a more modular and efficient workflow. However, the segmentation should be balanced with the overall complexity of the project, and you should assess whether the benefits outweigh the added intricacy.

GPT - research-ideas-and-input-variables

a) Here are some questions I would ask to clarify the final output of the task:

1. What is the desired format for the CSV output? Can you provide an example of what this should look like?
2. How many news websites do you want the agent to research for each keyword? 
3. How should the agent determine what constitutes the "most important, updated and/or newsworthy parts" of the website? This may vary from website to website. 
4. What criteria should the agent use to 'filter down' the shortlist of URLs? Are there any specific features/characteristics it should look out for apart from the presence of a "TOP NEWS" or "LATEST" container? How should the AI handle sites with dynamic elements?
5. How do you classify a website as 'relevant' for your requirements? Any specific metric or factor like traffic rank, type of content?
6. How many related and secondary keywords should the agent generate for each website? What should be the basis for choosing these keywords? Is there a preference in terms of keyword hierarchy or grouping?
7. What is the desired size or aspect ratio for the cropped images? Any specific resolution required for image quality maintenance?

b) Before launching the task completion process, the agent would possibly need the following inputs:

1. A single keyword topic: This would be used to research and generate a list of news websites related to the topic.
2. Parameters to classify a website as relevant: This can include criteria like website traffic, type of content or keyword research results for relevance.
3. The rules-set for categorizing the websites and generating related and secondary keywords.
4. Guidelines or specification for cropping the screenshot; for instance, specific locations (x, y coordinates) or features to consider, desired aspect ratio or size. 
5. Any specific format or layout required for generating the final CSV file, to ensure the output aligns with the user's needs.

template-ideas

Based on the task description and the desired final output, I suggest collecting the following templates to help refine the definition of a successful output:

1. CSV file template:
   - Search for examples of well-structured CSV files that contain similar data (e.g., image URLs, titles, descriptions, and keywords).
   - Analyze the structure, column names, and data types used in these CSV files.
   - Create a template CSV file with the desired columns and a few sample rows to ensure compatibility with the intended use case.

2. News website screenshot template:
   - Collect screenshots of popular news websites relevant to the target keyword topic.
   - Analyze the layout, design, and common elements of these websites (e.g., "TOP NEWS" or "LATEST" containers).
   - Identify the typical aspect ratios and positions of these containers to guide the cropping process.

3. Cropped image template:
   - Based on the analysis of news website screenshots, create a template or mockup of the desired cropped image.
   - Ensure that the cropped image template has an aspect ratio between 1:3 and 3:1, as specified in the task description.
   - Use this template to guide the development of the image cropping functionality in the automated workflow.

4. Website metadata template:
   - Research and collect examples of well-written titles, descriptions, and keywords for news websites.
   - Analyze the length, format, and style of these metadata elements.
   - Create a template or guidelines for generating effective titles, descriptions, and keywords based on the findings.

5. Keyword research template:
   - Look for examples of keyword research reports or templates used in the news or content industry.
   - Analyze the structure and format of these reports, paying attention to how related and secondary keywords are presented.
   - Create a template for the keyword research process that aligns with the desired output format (comma-separated keywords in the CSV file).

6. Automated workflow template:
   - Research and review existing automated workflows or pipelines for web scraping, image processing, and data categorization tasks.
   - Identify best practices, common tools, and libraries used in these workflows.
   - Create a high-level template or flowchart of the automated workflow, incorporating the key steps and components identified in the task description.

By collecting and analyzing these templates, you can gain valuable insights into what a successful output should look like and how to structure the automated workflow to achieve the desired results. These templates will also help communicate the project requirements and expectations to the AI agent, ensuring that the final output meets the specified criteria.

claude 3 - searches

Here are 5 specific keyphrases to search for templates that can help refine the definition of a successful output for your task:

1. "example csv file news website data title description keywords"
   - This search should return examples of well-structured CSV files containing data similar to what you need, such as titles, descriptions, and keywords for news websites.

2. "top news website screenshot layout analysis"
   - This search should provide screenshots and analyses of popular news websites, helping to identify common layout elements like "TOP NEWS" or "LATEST" containers and their typical positions and aspect ratios.

3. "news website top stories container cropped image example"
   - This search should yield examples of cropped images focusing on the main news containers of websites, which can serve as a template for the desired output of the automated workflow.

4. "news website metadata best practices title description keywords"
   - This search should return guidelines and examples of well-written titles, descriptions, and keywords for news websites, helping to create a template for generating effective metadata.

5. "keyword research report template news industry"
   - This search should provide examples of keyword research reports or templates used in the news industry, which can be used to structure the keyword research process and ensure alignment with the desired output format (comma-separated keywords in the CSV file).

By searching for these specific keyphrases and reviewing the top results, you should be able to find relevant templates and examples that can help refine the definition of a successful output for your automated workflow task.

Example CSV File News Website Data Title Description Keywords
Top News Website Screenshot Layout Analysis
News Website Top Stories Container Cropped Image Example
News Website Metadata Best Practices Title Description Keywords
Keyword Research Report Template News Industry

subagent-summary

A) FULL TASK SUMMARY: Develop an automated workflow to research, capture, analyze, and categorize news websites based on a user-provided keyword, and export the results to a structured CSV file.

B) FINAL TASK OUTPUT: A CSV file containing structured data for each relevant news website, including full screenshot (PNG or JPEG), cropped image of the "TOP NEWS" or "LATEST" container (PNG or JPEG), title, description, related keywords (1-5, comma-separated), and secondary keywords (6-20, comma-separated).

C) COMPONENTS OVERVIEW:
1. Keyword input interface
2. Web scraping and shortlisting of relevant news websites
3. Full-page screenshot capture
4. Image analysis and cropping of "TOP NEWS" or "LATEST" containers
5. Website categorization using traffic rank, keyword relevance, and text content analysis
6. Title, description, and keyword generation using an AI language model
7. Compilation of data into a structured CSV file
8. Storage of generated images and CSV file

D) OVERVIEW OF SUBAGENTS:
1. Keyword Input Agent: Handles user input and validation of the keyword topic.
2. Web Research Agent: Searches for and shortlists relevant news websites based on the input keyword.
3. Screenshot Capture Agent: Captures full-page screenshots of each shortlisted website.
4. Image Analysis Agent: Analyzes screenshots, identifies and crops "TOP NEWS" or "LATEST" containers.
5. Website Categorization Agent: Categorizes websites using traffic rank, keyword relevance, and text content analysis.
6. Content Generation Agent: Generates titles, descriptions, and keywords for each website using an AI language model.
7. Data Compilation Agent: Compiles all collected data into a structured CSV file.
8. Data Storage Agent: Stores generated images and the CSV file in a designated storage system.

E) SUBAGENT 1: Keyword Input Agent
- Name: Keyword Input Agent
- Final Output: Validated keyword topic string
- Accomplishment: The agent will create a simple user interface (e.g., command-line or web-based) to accept a single keyword topic from the user. It will validate the input to ensure it is a single keyword or phrase and handle any errors or edge cases.
- Sequence: Create input interface > Accept user input > Validate input > Return validated keyword topic string

F) SUBAGENT 2: Web Research Agent
- Name: Web Research Agent
- Final Output: List of relevant news website URLs (e.g., top 10)
- Accomplishment: The agent will use the validated keyword topic to search for relevant news websites using a combination of search engines, news APIs, and web scraping techniques. It will analyze the results and generate a shortlist of the most relevant websites (e.g., top 10) based on factors such as keyword relevance, website authority, and freshness of content.
- Sequence: Receive validated keyword topic > Perform web search and scraping > Analyze results > Generate shortlist of relevant URLs > Return list of URLs

G) SUBAGENT 3: Screenshot Capture Agent
- Name: Screenshot Capture Agent
- Final Output: Full-page screenshot images of each shortlisted website
- Accomplishment: The agent will use a headless browser (e.g., Puppeteer or Selenium) to visit each URL from the shortlist and capture a full-page screenshot of the website. It will handle any navigation or loading issues and ensure high-quality screenshots are captured.
- Sequence: Receive list of URLs > Initialize headless browser > Visit each URL > Capture full-page screenshot > Save screenshot image > Return set of screenshot images

H) SUBAGENT 4: Image Analysis Agent
- Name: Image Analysis Agent
- Final Output: Cropped images of "TOP NEWS" or "LATEST" containers from each website screenshot
- Accomplishment: The agent will use image processing libraries (e.g., OpenCV, Pillow) to analyze each website screenshot, identify the presence of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1, and crop the identified container using the x/y coordinates. It will handle cases where no suitable container is found and return appropriate error messages.
- Sequence: Receive set of screenshot images > Analyze each image for "TOP NEWS" or "LATEST" containers > Identify containers with aspect ratio between 1:3 and 3:1 > Crop identified containers using x/y coordinates > Save cropped images > Return set of cropped images

I) SUBAGENT 5: Website Categorization Agent
- Name: Website Categorization Agent
- Final Output: Set of relevant categorization information for each website (traffic rank, keyword relevance, text content analysis)
- Accomplishment: The agent will use various tools and techniques to categorize each website based on traffic rank (using SimilarWeb API or similar), keyword relevance (using AI-based keyword extraction on website content), and text content analysis (using web scraping and natural language processing). It will compile the results into a structured format for each website.
- Sequence: Receive list of URLs > Retrieve traffic rank using SimilarWeb API > Scrape website content > Perform keyword extraction and relevance analysis > Perform text content analysis using NLP > Compile categorization information for each website > Return set of categorization information

J) SUBAGENT 6: Content Generation Agent
- Name: Content Generation Agent
- Final Output: Generated title, description, related keywords (1-5), and secondary keywords (6-20) for each website
- Accomplishment: The agent will use an AI language model (e.g., GPT-3) to generate a title, description, and two sets of keywords for each website based on the categorization information provided by the Website Categorization Agent. It will ensure the generated content is relevant, coherent, and within the specified limits (e.g., 1-5 related keywords, 6-20 secondary keywords).
- Sequence: Receive set of categorization information > Generate title using AI language model > Generate description using AI language model > Generate 1-5 related keywords using AI language model > Generate 6-20 secondary keywords using AI language model > Compile generated content for each website > Return set of generated content

K) SUBAGENT 7: Data Compilation Agent
- Name: Data Compilation Agent
- Final Output: Structured CSV file containing all collected data for each website
- Accomplishment: The agent will compile all the collected data (full screenshot, cropped image, title, description, keywords) into a structured CSV file. It will ensure proper formatting, handle missing data, and validate the final CSV file for integrity.
- Sequence: Receive full screenshots, cropped images, titles, descriptions, and keywords > Create CSV structure with appropriate headers > Iterate through data and populate CSV rows > Validate CSV file for formatting and integrity > Save final CSV file > Return path to CSV file

L) SUBAGENT 8: Data Storage Agent
- Name: Data Storage Agent
- Final Output: Stored images and CSV file in a designated storage system
- Accomplishment: The agent will store the generated images (full screenshots and cropped images) and the final CSV file in a designated storage system (e.g., local file system, cloud storage). It will ensure proper file naming conventions, directory structure, and access control.
- Sequence: Receive generated images and CSV file > Determine appropriate storage location > Create directory structure > Copy images and CSV file to storage location > Validate successful storage > Return confirmation of storage completion

subagent-summary-amended-for-actual-tasks..1

Based on the available tasks and the components required for the automated news website analysis and categorization workflow, here's an updated list of subagents:

A) FULL TASK SUMMARY: Develop an automated workflow to research, capture, analyze, and categorize news websites based on a user-provided keyword, and export the results to a structured CSV file.

C) COMPONENTS OVERVIEW:
1. Web scraping and shortlisting of relevant news websites
2. Full-page screenshot capture
3. Image analysis and cropping of "TOP NEWS" or "LATEST" containers
4. Website categorization using traffic rank, keyword relevance, and text content analysis
5. Title, description, and keyword generation using an AI language model
6. Compilation of data into a structured CSV file
7. Storage of generated images and CSV file

D) OVERVIEW OF SUBAGENTS:
1. Web Research Agent: Searches for and shortlists relevant news websites based on the input keyword.
2. Screenshot Capture Agent: Captures full-page screenshots of each shortlisted website.
3. Image Analysis Agent: Analyzes screenshots, identifies and crops "TOP NEWS" or "LATEST" containers.
4. Website Categorization Agent: Categorizes websites using traffic rank, keyword relevance, and text content analysis.
5. Content Generation Agent: Generates titles, descriptions, and keywords for each website using an AI language model.
6. Data Compilation Agent: Compiles all collected data into a structured CSV file.
7. Data Storage Agent: Stores generated images and the CSV file in a designated storage system.

E) SUBAGENT 1: Web Research Agent
- Name: Web Research Agent
- Final Output: List of relevant news website URLs (e.g., top 10)
- Accomplishment: The agent will use the validated keyword topic to search for relevant news websites using a combination of search engines, news APIs, and web scraping techniques. It will analyze the results and generate a shortlist of the most relevant websites (e.g., top 10) based on factors such as keyword relevance, website authority, and freshness of content.
- Sequence:
  1. Use "Search Google" (task 15) to find relevant news websites for the input keyword
  2. Use "Scrape links from website" (task 17) to extract URLs from the top search results
  3. Use "Find related keywords" (task 21) to expand the search and find more relevant websites
  4. Use GPT-4 (task 1) to analyze and shortlist the most relevant news website URLs
  5. Return the list of shortlisted URLs

F) SUBAGENT 2: Screenshot Capture Agent
- Name: Screenshot Capture Agent
- Final Output: Full-page screenshot images of each shortlisted website
- Accomplishment: The agent will capture full-page screenshots of each website from the shortlist.
- Sequence:
  1. Receive the list of shortlisted URLs from the Web Research Agent
  2. Use "Take screenshot of website" (task 20) for each URL to capture full-page screenshots
  3. Use "Save content to server" (task 9) to save the screenshot images on the server
  4. Return the list of screenshot image URLs

G) SUBAGENT 3: Image Analysis Agent
- Name: Image Analysis Agent
- Final Output: Cropped images of "TOP NEWS" or "LATEST" containers from each website screenshot
- Accomplishment: The agent will use image processing libraries to analyze each website screenshot, identify the presence of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1, and crop the identified container using the x/y coordinates.
- Sequence:
  1. Receive the list of screenshot image URLs from the Screenshot Capture Agent
  2. Use "Extract visual data from image" (task 5) to analyze each screenshot and identify "TOP NEWS" or "LATEST" containers with the specified aspect ratio
  3. Use "Crop image" (task 25) to crop the identified containers using the x/y coordinates
  4. Use "Save content to server" (task 9) to save the cropped images on the server
  5. Return the list of cropped image URLs

H) SUBAGENT 4: Website Categorization Agent
- Name: Website Categorization Agent
- Final Output: Set of relevant categorization information for each website (traffic rank, keyword relevance, text content analysis)
- Accomplishment: The agent will use various tools and techniques to categorize each website based on traffic rank (using SimilarWeb API or similar), keyword relevance (using AI-based keyword extraction on website content), and text content analysis (using web scraping and natural language processing).
- Sequence:
  1. Receive the list of shortlisted URLs from the Web Research Agent
  2. Use "Scrape text content of website" (task 16) to extract text content from each website
  3. Use GPT-4 (task 1) to analyze the scraped text content and extract relevant keywords and phrases
  4. Use an external API (REQUIRED) to retrieve traffic rank data for each website
  5. Compile the categorization information (traffic rank, keyword relevance, text content analysis) for each website
  6. Return the set of categorization information

J) SUBAGENT 6: Data Compilation Agent
- Name: Data Compilation Agent
- Final Output: Structured CSV file containing all collected data for each website
- Accomplishment: The agent will compile all the collected data (full screenshot, cropped image, title, description, keywords) into a structured CSV file.
- Sequence:
  1. Receive the full screenshots, cropped images, titles, descriptions, and keywords from the respective agents
  2. Use GPT-4 (task 1) to create a structured CSV file with appropriate headers and populate the data for each website
  3. Use "Save content to server" (task 9) to save the CSV file on the server
  4. Return the URL of the saved CSV file

K) SUBAGENT 7: Data Storage Agent
- Name: Data Storage Agent
- Final Output: Stored images and CSV file in a designated storage system
- Accomplishment: The agent will store the generated images (full screenshots and cropped images) and the final CSV file in a designated storage system (e.g., local file system, cloud storage).
- Sequence:
  1. Receive the generated images and CSV file from the respective agents
  2. Use "Move files around server" (task 8) to organize and store the images and CSV file in a designated directory structure on the server
  3. Return confirmation of successful storage completion

This updated list of subagents utilizes the available tasks to accomplish the components required for the automated news website analysis and categorization workflow. The only additional requirement is an external API to retrieve traffic rank data for each website, which is marked as "REQUIRED" in the Website Categorization Agent's sequence.

subagent-summary-amended-for-actual-tasks..2

Thank you for providing the list of available tasks. Based on this information, I have updated the subagents to complete the automated news website research and categorization workflow. Please note that I have included a few additional API calls or python libraries that I believe are necessary for the task, marked as "REQUIRED".

A) FULL TASK SUMMARY: Develop an automated workflow to research, capture, analyze, and categorize news websites based on a user-provided keyword, and export the results to a structured CSV file.

E) SUBAGENT 1: Web Research Agent
- Name: Web Research Agent
- Final Output: List of relevant news website URLs (e.g., top 10)
- Accomplishment: The agent will use the validated keyword topic to search for relevant news websites using a combination of search engines, news APIs, and web scraping techniques. It will analyze the results and generate a shortlist of the most relevant websites (e.g., top 10) based on factors such as keyword relevance, website authority, and freshness of content.
- Sequence:
  1. Receive validated keyword topic
  2. Use "Search Google" task to perform web search
  3. REQUIRED: Use a news API (e.g., NewsAPI, Currents API) to search for relevant news articles and extract website URLs
  4. Use "Scrape links from website" task to extract additional relevant URLs from the top search results
  5. REQUIRED: Analyze and rank the collected URLs based on keyword relevance, website authority (e.g., using Domain Authority or Alexa Rank), and content freshness
  6. Generate a shortlist of the top 10 relevant news website URLs
  7. Return list of URLs

F) SUBAGENT 2: Screenshot Capture Agent
- Name: Screenshot Capture Agent
- Final Output: Full-page screenshot images of each shortlisted website
- Accomplishment: The agent will use the "Take screenshot of website" task to visit each URL from the shortlist and capture a full-page screenshot of the website. It will handle any navigation or loading issues and ensure high-quality screenshots are captured.
- Sequence:
  1. Receive list of URLs
  2. Iterate through each URL and use "Take screenshot of website" task to capture a full-page screenshot
  3. Use "Move files around server" task to move the screenshot images to a designated directory
  4. Return the list of screenshot image URLs

G) SUBAGENT 3: Image Analysis Agent
- Name: Image Analysis Agent
- Final Output: Cropped images of "TOP NEWS" or "LATEST" containers from each website screenshot
- Accomplishment: The agent will use the "Extract visual data from image" task to analyze each website screenshot, identify the presence of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1, and crop the identified container using the "Crop image" task with the provided x/y coordinates. It will handle cases where no suitable container is found and return appropriate error messages.
- Sequence:
  1. Receive list of screenshot image URLs
  2. Iterate through each image URL and use "Extract visual data from image" task to identify the presence and location of "TOP NEWS" or "LATEST" containers with an aspect ratio between 1:3 and 3:1
  3. Use "Crop image" task to crop the identified containers using the provided x/y coordinates
  4. Use "Move files around server" task to move the cropped images to a designated directory
  5. Return the list of cropped image URLs

H) SUBAGENT 4: Website Categorization Agent
- Name: Website Categorization Agent
- Final Output: Set of relevant categorization information for each website (traffic rank, keyword relevance, text content analysis)
- Accomplishment: The agent will use various tasks and techniques to categorize each website based on traffic rank (REQUIRED: using SimilarWeb API or similar), keyword relevance (using "Find related keywords" task and REQUIRED: AI-based keyword extraction on website content), and text content analysis (using "Scrape text content of website" task and REQUIRED: natural language processing techniques). It will compile the results into a structured format for each website.
- Sequence:
  1. Receive list of website URLs
  2. REQUIRED: Retrieve traffic rank for each URL using SimilarWeb API or similar
  3. Use "Scrape text content of website" task to extract text content from each website
  4. Use "Find related keywords" task to determine keyword relevance for each website
  5. REQUIRED: Perform AI-based keyword extraction on the scraped text content to identify additional relevant keywords
  6. REQUIRED: Perform text content analysis using natural language processing techniques to extract key topics and entities
  7. Compile categorization information (traffic rank, keyword relevance, text content analysis) for each website
  8. Return the set of categorization information

I) SUBAGENT 5: Content Generation Agent
- Name: Content Generation Agent
- Final Output: Generated title, description, related keywords (1-5), and secondary keywords (6-20) for each website
- Accomplishment: The agent will use the "GPT4" task to generate a title, description, and two sets of keywords for each website based on the categorization information provided by the Website Categorization Agent. It will ensure the generated content is relevant, coherent, and within the specified limits (e.g., 1-5 related keywords, 6-20 secondary keywords).
- Sequence:
  1. Receive set of categorization information for each website
  2. For each website, use "GPT4" task to:
    a. Generate a title based on the categorization information
    b. Generate a description based on the categorization information
    c. Generate 1-5 related keywords based on the categorization information
    d. Generate 6-20 secondary keywords based on the categorization information
  3. Compile the generated content (title, description, related keywords, secondary keywords) for each website
  4. Return the set of generated content

J) SUBAGENT 6: Data Compilation Agent
- Name: Data Compilation Agent
- Final Output: Structured CSV file containing all collected data for each website
- Accomplishment: The agent will use the "Save content to server" task to compile all the collected data (full screenshot URLs, cropped image URLs, titles, descriptions, keywords) into a structured CSV file. It will ensure proper formatting, handle missing data, and validate the final CSV file for integrity.
- Sequence:
  1. Receive full screenshot URLs, cropped image URLs, titles, descriptions, and keywords for each website
  2. Create a structured CSV file with appropriate headers (e.g., "Full Screenshot URL", "Cropped Image URL", "Title", "Description", "Related Keywords", "Secondary Keywords")
  3. Iterate through the data for each website and populate the corresponding row in the CSV file
  4. Validate the CSV file for proper formatting and data integrity
  5. Use "Save content to server" task to save the final CSV file
  6. Return the URL of the saved CSV file

K) SUBAGENT 7: Data Storage Agent
- Name: Data Storage Agent
- Final Output: Stored images and CSV file in a designated storage system
- Accomplishment: The agent will use the "Move files around server" task to store the generated images (full screenshots and cropped images) and the final CSV file in a designated storage system (e.g., specific directory on the server). It will ensure proper file naming conventions and directory structure.
- Sequence:
  1. Receive the list of full screenshot URLs, cropped image URLs, and the URL of the final CSV file
  2. Use "Move files around server" task to move the full screenshot images to a designated directory (e.g., "full_screenshots")
  3. Use "Move files around server" task to move the cropped images to a designated directory (e.g., "cropped_images")
  4. Use "Move files around server" task to move the final CSV file to a designated directory (e.g., "data")
  5. Validate the successful storage of all files
  6. Return confirmation of storage completion

These subagents, when executed in the proper sequence, should be able to complete the automated news website research and categorization workflow using the available tasks and a few additional required API calls or python libraries.

subagent-summary-amended-for-actual-tasks GPT..3

Given the available tasks, I will now redefine the subagents and their functionalities required to complete the initial task of creating a database of news websites with specific information extracted from them:

D) OVERVIEW OF SUBAGENTS:
1. Web Research Agent: Searches for relevant news websites.
2. Screenshot Capture Agent: Takes screenshots of shortlisted websites.
3. Image Analysis and Cropping Agent: Identifies and crops the "TOP NEWS" or "LATEST" containers.
4. Website Categorization Agent: Gathers relevant categorization information for each website.
5. Content Generation Agent: Generates website titles, descriptions, related keywords, and secondary keywords.
6. Data Compilation Agent: Compiles all collected data into a CSV file.
7. Data Storage Agent: Manages internal storage of generated images and the CSV file.

E) SUBAGENTS AND TASKS:

1. Web Research Agent
- Task(s) used: 
   15) Search Google
   16) Scrape text content of website

2. Screenshot Capture Agent
- Task(s) used: 
   20) Take screenshot of website

3. Image Analysis and Cropping Agent
- Task(s) used: 
   5) Extract visual data from image
   25) Crop image

4. Website Categorization Agent
- Task(s) used: 
   16) Scrape text content of website
   21) Find related keywords
   1) GPT4 for traffic rank estimation and additional categorization using the text content (Note: SimilarWeb API access would be ideal, but since it's not listed, GPT4 can provide approximate estimates based on known data. If this is a significant limitation, it can be marked as REQUIRED to add a SimilarWeb API call).

5. Content Generation Agent
- Task(s) used: 
   1) GPT4

6. Data Compilation Agent
- Task(s) used:
   9) Save content to server (to assemble the CSV)
   13) Concatena text/html files (if needed to merge multiple CSV files into one, depending on the workflow)

7. Data Storage Agent
- Task(s) used:
   8) Move files around server (for organizing screenshots and CSV data)
   9) Save content to server (for permanent storage of the final CSV)

By leveraging these tasks and chaining them efficiently, each subagent will perform its dedicated function within the workflow to achieve the end goal. Any required functionalities not provided by the current tasks have been noted as "REQUIRED" for further development.

subagent-summary-amended-for-actual-tasks

A) FULL TASK SUMMARY: Develop an automated workflow to research, capture, analyze, and categorize news websites based on a user-provided keyword, and export the results to a structured CSV file.

D) OVERVIEW OF SUBAGENTS:
1. Web Research Agent: Searches for and shortlists relevant news websites based on the input keyword.
2. Screenshot Capture Agent: Captures full-page screenshots of each shortlisted website.
3. Image Analysis and Cropping Agent: Analyzes screenshots, identifies and crops "TOP NEWS" or "LATEST" containers.
4. Website Categorization Agent: Categorizes websites using traffic rank, keyword relevance, and text content analysis.
5. Content Generation Agent: Generates titles, descriptions, and keywords for each website using an AI language model.
6. Data Compilation Agent: Compiles all collected data into a structured CSV file.
7. Data Storage Agent: Stores generated images and the CSV file in a designated storage system.

E) SUBAGENT 1: Web Research Agent
- Tasks used:
  - Search Google (task 15)
  - Scrape text content of website (task 16)
  - Scrape links from website (task 17)
  - Find related keywords (task 21)
  - GPT4 (task 1) for analyzing and shortlisting URLs
- Accomplishment: The agent will use the input keyword to search for relevant news websites using Google search, scrape links and text content from the top search results, find related keywords to expand the search, and use GPT-4 to analyze and shortlist the most relevant URLs based on factors such as keyword relevance, website authority, and content freshness.
- Output: List of relevant news website URLs (e.g., top 10)

F) SUBAGENT 2: Screenshot Capture Agent
- Tasks used:
  - Take screenshot of website (task 20)
  - Save content to server (task 9)
- Accomplishment: The agent will capture full-page screenshots of each website from the shortlist and save the screenshot images on the server.
- Output: List of screenshot image URLs

G) SUBAGENT 3: Image Analysis and Cropping Agent
- Tasks used:
  - Extract visual data from image (task 5)
  - Crop image (task 25)
  - Save content to server (task 9)
- Accomplishment: The agent will analyze each website screenshot using AI vision, identify the presence of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1, crop the identified container using the provided x/y coordinates, and save the cropped images on the server.
- Output: List of cropped image URLs

H) SUBAGENT 4: Website Categorization Agent
- Tasks used:
  - Scrape text content of website (task 16)
  - Find related keywords (task 21)
  - GPT4 (task 1) for analyzing text content and estimating traffic rank
  - REQUIRED: API call to retrieve traffic rank data (e.g., SimilarWeb API)
- Accomplishment: The agent will scrape text content from each website, find related keywords, use GPT-4 to analyze the text content and estimate traffic rank (if an API is not available), and compile categorization information (traffic rank, keyword relevance, text content analysis) for each website.
- Output: Set of categorization information for each website

I) SUBAGENT 5: Content Generation Agent
- Tasks used:
  - GPT4 (task 1)
- Accomplishment: The agent will use GPT-4 to generate a title, description, related keywords (1-5), and secondary keywords (6-20) for each website based on the categorization information provided by the Website Categorization Agent.
- Output: Generated content (title, description, keywords) for each website

J) SUBAGENT 6: Data Compilation Agent
- Tasks used:
  - Save content to server (task 9)
  - Concatena text/html files (task 13) if needed to merge multiple CSV files
- Accomplishment: The agent will compile all the collected data (full screenshot URLs, cropped image URLs, titles, descriptions, keywords) into a structured CSV file, ensure proper formatting and data integrity, and save the final CSV file on the server.
- Output: URL of the saved CSV file

K) SUBAGENT 7: Data Storage Agent
- Tasks used:
  - Move files around server (task 8)
  - Save content to server (task 9)
- Accomplishment: The agent will store the generated images (full screenshots and cropped images) and the final CSV file in a designated storage system, ensuring proper file naming conventions and directory structure.
- Output: Confirmation of storage completion

These subagents, when executed in the proper sequence and with the addition of the required API call for retrieving traffic rank data, will complete the automated news website research and categorization workflow, resulting in a comprehensive CSV file containing the specified data for each relevant website.

random-autocomplete

To achieve the end goal of creating a structured CSV file containing information about news websites relevant to a specific keyword topic, the most relevant tasks might be:

1. Keyword research and website discovery
2. Website screenshot capture
3. Image analysis and cropping
4. Website categorization and data extraction
5. CSV file generation and export

The overall sequence of actions would be:

1. Accept user input for the keyword topic
2. Perform keyword research and generate a list of relevant news websites
3. Filter the list of websites based on the presence of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1
4. Capture full-page screenshots of the filtered websites
5. Analyze the screenshots to identify the x/y coordinates of the "TOP NEWS" or "LATEST" container
6. Crop the screenshots based on the identified x/y coordinates
7. Categorize the websites using data from SimilarWeb, keyword research, and content analysis
8. Pass the collected data to an LLM to generate a final title, description, and related keywords for each website
9. Save the original full screenshot, cropped image, final title, description, related keywords, and secondary keywords for each website in a structured format
10. Export the structured data as a CSV file

To accomplish these tasks, the Agent would need to utilize various APIs and libraries, such as:

- Search engine APIs (e.g., Google, Bing) for keyword research and website discovery
- Web scraping libraries (e.g., BeautifulSoup, Scrapy) for extracting data from websites
- Headless browsing libraries (e.g., Puppeteer, Selenium) for capturing website screenshots
- Image processing libraries (e.g., OpenCV, Pillow) for analyzing and cropping screenshots
- NLP libraries (e.g., spaCy, NLTK) for content analysis and keyword extraction
- LLM APIs (e.g., GPT-3, BERT) for generating titles, descriptions, and keywords
- Data manipulation libraries (e.g., Pandas) for structuring and exporting data as a CSV file

By leveraging these tools and following the outlined sequence of actions, the Agent should be able to successfully complete the task and generate the desired CSV file.

claude 3 - mermaid-for-all

SubAgent #1

claude 3 - subagent1

subagent1-refined

A) SUBAGENT SUMMARY: The Web Research Agent searches for relevant news websites based on a user-provided keyword, scrapes and analyzes the content, and shortlists the most relevant URLs.

B) FINAL TASK OUTPUT: A JSON file containing a list of the top 10 relevant news website URLs, along with their titles, descriptions, and relevance scores.

C) SUBAGENT INPUT: A single keyword or keyphrase (1-200 characters) provided by the user.

E) SUBAGENT TASK SUMMARY:

1. User Input > Search Google (task 15) 
   - Input: User-provided keyword or keyphrase
   - Output: Google SERP results (text summary with position, title, and snippet for the first 20 URLs)

2. Google SERP results > GPT4 (task 1)
   - Input: Google SERP results
   - Output: Extracted list of 20 URLs from the SERP results

3. Extracted URL list > Scrape text content of website (task 16) [loop through each URL]
   - Input: Individual URL from the extracted list
   - Output: Scraped text content of the website (max 25,000 characters)

4. Scraped text content > Save content to server (task 9) [loop through each URL]
   - Input: Scraped text content, file extension (.txt)
   - Output: Internal server URL for the saved text file

5. Extracted URL list > Find related keywords (task 21)
   - Input: User-provided keyword or keyphrase
   - Output: 10 related keywords/phrases with search volume, cost per click, and relevance scores

6. Related keywords > Search Google (task 15) [loop through each related keyword]
   - Input: Individual related keyword/phrase
   - Output: Google SERP results for the related keyword (text summary with position, title, and snippet for the first 20 URLs)

7. Related keyword SERP results > GPT4 (task 1) [loop through each related keyword]
   - Input: Google SERP results for the related keyword
   - Output: Extracted list of 20 URLs from the related keyword SERP results

8. Extracted related keyword URL lists > Scrape text content of website (task 16) [loop through each URL]
   - Input: Individual URL from the extracted related keyword lists
   - Output: Scraped text content of the website (max 25,000 characters)

9. Scraped related keyword text content > Save content to server (task 9) [loop through each URL]
   - Input: Scraped text content, file extension (.txt)
   - Output: Internal server URL for the saved text file

10. All saved text files > GPT4 (task 1)
    - Input: List of internal server URLs for all saved text files (original keyword and related keywords)
    - Output: Shortlisted top 10 URLs with their titles, descriptions, and relevance scores

11. Shortlisted URLs > Save content to server (task 9)
    - Input: Shortlisted URLs (JSON format)
    - Output: Internal server URL for the saved JSON file

F) SILOS:

Silo 1: Initial Keyword Research
- Search Google (task 15) for the user-provided keyword
- GPT4 (task 1) to extract URLs from SERP results
- Scrape text content of website (task 16) for each extracted URL
- Save content to server (task 9) for each scraped text content

Silo 2: Related Keyword Research
- Find related keywords (task 21) for the user-provided keyword
- Search Google (task 15) for each related keyword
- GPT4 (task 1) to extract URLs from related keyword SERP results
- Scrape text content of website (task 16) for each extracted related keyword URL
- Save content to server (task 9) for each scraped related keyword text content

Silo 3: URL Shortlisting
- GPT4 (task 1) to analyze all saved text files and shortlist the top 10 URLs
- Save content to server (task 9) to save the shortlisted URLs as a JSON file

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. User Input > Search Google (task 15)
2. Google SERP results > GPT4 (task 1)
3. Extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
4. Scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
5. Extracted URL list > Find related keywords (task 21)
6. Related keyword 1 > Search Google (task 15)
7. Related keyword 1 SERP results > GPT4 (task 1)
8. Related keyword 1 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
9. Related keyword 1 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
10. Related keyword 2 > Search Google (task 15)
11. Related keyword 2 SERP results > GPT4 (task 1)
12. Related keyword 2 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
13. Related keyword 2 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
14. Related keyword 3 > Search Google (task 15)
15. Related keyword 3 SERP results > GPT4 (task 1)
16. Related keyword 3 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
17. Related keyword 3 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
18. Related keyword 4 > Search Google (task 15)
19. Related keyword 4 SERP results > GPT4 (task 1)
20. Related keyword 4 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
21. Related keyword 4 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
22. Related keyword 5 > Search Google (task 15)
23. Related keyword 5 SERP results > GPT4 (task 1)
24. Related keyword 5 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
25. Related keyword 5 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
26. Related keyword 6 > Search Google (task 15)
27. Related keyword 6 SERP results > GPT4 (task 1)
28. Related keyword 6 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
29. Related keyword 6 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
30. Related keyword 7 > Search Google (task 15)
31. Related keyword 7 SERP results > GPT4 (task 1)
32. Related keyword 7 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
33. Related keyword 7 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
34. Related keyword 8 > Search Google (task 15)
35. Related keyword 8 SERP results > GPT4 (task 1)
36. Related keyword 8 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
37. Related keyword 8 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
38. Related keyword 9 > Search Google (task 15)
39. Related keyword 9 SERP results > GPT4 (task 1)
40. Related keyword 9 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
41. Related keyword 9 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
42. Related keyword 10 > Search Google (task 15)
43. Related keyword 10 SERP results > GPT4 (task 1)
44. Related keyword 10 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs]
45. Related keyword 10 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs]
46. All saved text files > GPT4 (task 1)
47. Shortlisted URLs > Save content to server (task 9)

Subagent1 task-ID-numbers

subagent1-output-variables

Here's the updated task list with output tokens:

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. User Input > Search Google (task 15) [user-keyword]
2. Google SERP results > GPT4 (task 1) [serp-results-user-keyword]
3. Extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-user-keyword-url1] to [scraped-text-user-keyword-url20]
4. Scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-user-keyword-url1] to [saved-text-user-keyword-url20]
5. Extracted URL list > Find related keywords (task 21) [related-keywords-user-keyword]
6. Related keyword 1 > Search Google (task 15) [related-keyword1]
7. Related keyword 1 SERP results > GPT4 (task 1) [serp-results-related-keyword1]
8. Related keyword 1 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword1-url1] to [scraped-text-related-keyword1-url20]
9. Related keyword 1 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword1-url1] to [saved-text-related-keyword1-url20]
10. Related keyword 2 > Search Google (task 15) [related-keyword2]
11. Related keyword 2 SERP results > GPT4 (task 1) [serp-results-related-keyword2]
12. Related keyword 2 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword2-url1] to [scraped-text-related-keyword2-url20]
13. Related keyword 2 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword2-url1] to [saved-text-related-keyword2-url20]
14. Related keyword 3 > Search Google (task 15) [related-keyword3]
15. Related keyword 3 SERP results > GPT4 (task 1) [serp-results-related-keyword3]
16. Related keyword 3 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword3-url1] to [scraped-text-related-keyword3-url20]
17. Related keyword 3 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword3-url1] to [saved-text-related-keyword3-url20]
18. Related keyword 4 > Search Google (task 15) [related-keyword4]
19. Related keyword 4 SERP results > GPT4 (task 1) [serp-results-related-keyword4]
20. Related keyword 4 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword4-url1] to [scraped-text-related-keyword4-url20]
21. Related keyword 4 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword4-url1] to [saved-text-related-keyword4-url20]
22. Related keyword 5 > Search Google (task 15) [related-keyword5]
23. Related keyword 5 SERP results > GPT4 (task 1) [serp-results-related-keyword5]
24. Related keyword 5 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword5-url1] to [scraped-text-related-keyword5-url20]
25. Related keyword 5 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword5-url1] to [saved-text-related-keyword5-url20]
26. Related keyword 6 > Search Google (task 15) [related-keyword6]
27. Related keyword 6 SERP results > GPT4 (task 1) [serp-results-related-keyword6]
28. Related keyword 6 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword6-url1] to [scraped-text-related-keyword6-url20]
29. Related keyword 6 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword6-url1] to [saved-text-related-keyword6-url20]
30. Related keyword 7 > Search Google (task 15) [related-keyword7]
31. Related keyword 7 SERP results > GPT4 (task 1) [serp-results-related-keyword7]
32. Related keyword 7 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword7-url1] to [scraped-text-related-keyword7-url20]
33. Related keyword 7 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword7-url1] to [saved-text-related-keyword7-url20]
34. Related keyword 8 > Search Google (task 15) [related-keyword8]
35. Related keyword 8 SERP results > GPT4 (task 1) [serp-results-related-keyword8]
36. Related keyword 8 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword8-url1] to [scraped-text-related-keyword8-url20]
37. Related keyword 8 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword8-url1] to [saved-text-related-keyword8-url20]
38. Related keyword 9 > Search Google (task 15) [related-keyword9]
39. Related keyword 9 SERP results > GPT4 (task 1) [serp-results-related-keyword9]
40. Related keyword 9 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword9-url1] to [scraped-text-related-keyword9-url20]
41. Related keyword 9 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword9-url1] to [saved-text-related-keyword9-url20]
42. Related keyword 10 > Search Google (task 15) [related-keyword10]
43. Related keyword 10 SERP results > GPT4 (task 1) [serp-results-related-keyword10]
44. Related keyword 10 extracted URL list > Scrape text content of website (task 16) [repeated for each of the 20 URLs] [scraped-text-related-keyword10-url1] to [scraped-text-related-keyword10-url20]
45. Related keyword 10 scraped text content > Save content to server (task 9) [repeated for each of the 20 URLs] [saved-text-related-keyword10-url1] to [saved-text-related-keyword10-url20]
46. All saved text files > GPT4 (task 1) [gpt4-output-all-text-files]
47. Shortlisted URLs > Save content to server (task 9) [saved-shortlisted-urls]

H) OUTPUT TOKENS

[user-keyword]
[serp-results-user-keyword]
[scraped-text-user-keyword-url1] to [scraped-text-user-keyword-url20]
[saved-text-user-keyword-url1] to [saved-text-user-keyword-url20]
[related-keywords-user-keyword]
[related-keyword1] to [related-keyword10]
[serp-results-related-keyword1] to [serp-results-related-keyword10]
[scraped-text-related-keyword1-url1] to [scraped-text-related-keyword1-url20]
[scraped-text-related-keyword2-url1] to [scraped-text-related-keyword2-url20]
[scraped-text-related-keyword3-url1] to [scraped-text-related-keyword3-url20]
[scraped-text-related-keyword4-url1] to [scraped-text-related-keyword4-url20]
[scraped-text-related-keyword5-url1] to [scraped-text-related-keyword5-url20]
[scraped-text-related-keyword6-url1] to [scraped-text-related-keyword6-url20]
[scraped-text-related-keyword7-url1] to [scraped-text-related-keyword7-url20]
[scraped-text-related-keyword8-url1] to [scraped-text-related-keyword8-url20]
[scraped-text-related-keyword9-url1] to [scraped-text-related-keyword9-url20]
[scraped-text-related-keyword10-url1] to [scraped-text-related-keyword10-url20]
[saved-text-related-keyword1-url1] to [saved-text-related-keyword1-url20]
[saved-text-related-keyword2-url1] to [saved-text-related-keyword2-url20]
[saved-text-related-keyword3-url1] to [saved-text-related-keyword3-url20]
[saved-text-related-keyword4-url1] to [saved-text-related-keyword4-url20]
[saved-text-related-keyword5-url1] to [saved-text-related-keyword5-url20]
[saved-text-related-keyword6-url1] to [saved-text-related-keyword6-url20]
[saved-text-related-keyword7-url1] to [saved-text-related-keyword7-url20]
[saved-text-related-keyword8-url1] to [saved-text-related-keyword8-url20]
[saved-text-related-keyword9-url1] to [saved-text-related-keyword9-url20]
[saved-text-related-keyword10-url1] to [saved-text-related-keyword10-url20]
[gpt4-output-all-text-files]
[saved-shortlisted-urls]

subagent1refined-repeating-json-core

Here is the TASKFLOW SUMMARY for the subagent you described:

G) TASKFLOW SUMMARY

1. Title task: User inputs search query
Description of the task: The user enters a search query or topic to research news websites on
Input(s) involved: [user input topic]
Output token name: user_search_query

2. Title task: Search Google for query and extract top URLs
Description of the task: Searches Google for the user's query, extracts the top 20 URLs from the search results
Input(s) involved: user_search_query
Output token name: top_20_urls

3. Title task: Scrape text content from each of the top 20 URLs 
Description of the task: Visits each of the top 20 URLs and scrapes the main text content from the page
Input(s) involved: top_20_urls
Output token name: scraped_text_content_top20

4. Title task: Save scraped text content to server
Description of the task: Saves the scraped text content from each of the top 20 URLs to the server 
Input(s) involved: scraped_text_content_top20
Output token name: saved_text_content_top20

5. Title task: Find 5 most relevant keywords from the scraped text content
Description of the task: Analyzes the scraped text content to identify the top 5 most relevant keywords
Input(s) involved: scraped_text_content_top20
Output token name: top_5_relevant_keywords

6. Title task: For each of the top 5 keywords, search Google and extract top 20 URLs
Description of the task: Conducts a Google search for each of the top 5 keywords, extracts the top 20 URLs from each set of search results
Input(s) involved: top_5_relevant_keywords
Output token name: top20_urls_per_keyword

7. Title task: Scrape text content from each set of 20 URLs per keyword
Description of the task: Visits each of the 20 URLs for the 5 keywords and scrapes the main text content
Input(s) involved: top20_urls_per_keyword 
Output token name: scraped_text_content_per_keyword

8. Title task: Save scraped text content for each keyword to server
Description of the task: Saves the scraped text content from the 20 URLs for each of the 5 keywords to the server
Input(s) involved: scraped_text_content_per_keyword
Output token name: saved_text_content_per_keyword

9. Title task: Concatenate all saved text content and send to GPT-4
Description of the task: Combines all the saved text content files into one and passes it to GPT-4 for analysis and filtering
Input(s) involved: saved_text_content_top20, saved_text_content_per_keyword 
Output token name: filtered_url_list

10. Title task: Save the final filtered list of URLs to server 
Description of the task: Takes the filtered list of URLs output by GPT-4 and saves it to the server
Input(s) involved: filtered_url_list
Output token name: final_url_list

subagent1mermaid

<!DOCTYPE html>
<html>
<head>
  <title>Mermaid Diagram Example</title>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script>
</head>
<body>
  <div class="mermaid">
graph TD
A[User inputs search query] -->|user_search_query| B(Search Google for query and extract top URLs)
B --> |top_20_urls| C(Scrape text content from each of the top 20 URLs)
C --> |scraped_text_content_top20| D(Save scraped text content to server)
C --> |scraped_text_content_top20| E(Find 5 most relevant keywords from the scraped text content)
E --> |top_5_relevant_keywords| F(For each of the top 5 keywords, search Google and extract top 20 URLs)
F --> |top20_urls_per_keyword| G(Scrape text content from each set of 20 URLs per keyword)
G --> |scraped_text_content_per_keyword| H(Save scraped text content for each keyword to server)
D --> |saved_text_content_top20| I(Concatenate all saved text content and send to GPT-4)
H --> |saved_text_content_per_keyword| I
I --> |filtered_url_list| J(Save the final filtered list of URLs to server)
  </div>

SubAgent #2

claude 3 - subagent2

subagent2-refined

A) SUBAGENT SUMMARY: The Screenshot Capture Agent will capture full-page screenshots of each website from the shortlist and save the screenshot images on the server.

B) FINAL TASK OUTPUT: A list of screenshot image URLs (PNG format, 1400x2400 pixels) saved on the server, corresponding to the shortlisted websites.

C) SUBAGENT INPUT:
- A list of relevant news website URLs (e.g., top 10) generated by the Web Research Agent

D) SUBAGENT TASK SUMMARY:
1. Receive the list of relevant news website URLs from the Web Research Agent
2. For each URL in the list:
   a. Use the "Take screenshot of website" task (task 20) to capture a full-page screenshot (1400x2400 pixels) of the website
   b. Use the "Save content to server" task (task 9) to save the screenshot image (PNG format) on the server with a unique filename (e.g., website1_screenshot.png)
3. Compile a list of the saved screenshot image URLs
4. Output the list of screenshot image URLs

E) SILOS:
This subagent does not require silos as it performs a single, straightforward task of capturing and saving screenshots for each website in the input list.

Here's a detailed breakdown of the subagent's workflow:

Subagent Input: List of relevant news website URLs
1. For each URL in the list:
   a. Take screenshot of website (task 20)
      - INPUT: URL of the website
      - OUTPUT: Binary data of the screenshot image (PNG, 1400x2400 pixels)
   b. Save content to server (task 9)
      - INPUT: Binary data of the screenshot image, file extension (.png), and desired file name (e.g., website1_screenshot.png)
      - OUTPUT: URL of the saved screenshot image on the server
2. Compile a list of the saved screenshot image URLs
3. Subagent Output: List of screenshot image URLs

This subagent takes a focused approach to capture and save screenshots of the shortlisted websites, ensuring that the output is a well-structured list of screenshot image URLs that can be easily accessed and processed by the subsequent subagents in the workflow.

subagent2refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the list of relevant news website URLs from the Web Research Agent
2. For each URL in the list (assuming 10 URLs):
   a. Use the "Take screenshot of website" task (task 20) to capture a full-page screenshot (1400x2400 pixels) of the website
      - INPUT: URL of the website
      - OUTPUT: Binary data of the screenshot image (PNG, 1400x2400 pixels)
   b. Use the "Save content to server" task (task 9) to save the screenshot image (PNG format) on the server with a unique filename (e.g., website1_screenshot.png)
      - INPUT: Binary data of the screenshot image, file extension (.png), and desired file name (e.g., website1_screenshot.png)
      - OUTPUT: URL of the saved screenshot image on the server
   (Repeat steps 2a and 2b for each of the 10 URLs)
3. Compile a list of the saved screenshot image URLs
4. Output the list of screenshot image URLs

In this case, the main repetition occurs in steps 2a and 2b, where the subagent needs to capture a screenshot and save it to the server for each of the 10 URLs provided by the Web Research Agent. The rest of the tasks (receiving the input, compiling the list of saved screenshot URLs, and outputting the list) are performed only once.

Subagent2 task-ID-numbers

subagent2-output-variables

Here's the updated task list with the output tokens added:

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the list of relevant news website URLs from the Web Research Agent
   - INPUT: List of URLs
   - OUTPUT: [news-urls]

2. For each URL in the list (assuming 10 URLs):
   a. Use the "Take screenshot of website" task (task 20) to capture a full-page screenshot (1400x2400 pixels) of the website
      - INPUT: URL of the website [news-url1], [news-url2], ..., [news-url10]
      - OUTPUT: Binary data of the screenshot image (PNG, 1400x2400 pixels) [screenshot-binary1], [screenshot-binary2], ..., [screenshot-binary10]

b. Use the "Save content to server" task (task 9) to save the screenshot image (PNG format) on the server with a unique filename (e.g., website1_screenshot.png)
      - INPUT: Binary data of the screenshot image [screenshot-binary1], [screenshot-binary2], ..., [screenshot-binary10], file extension (.png), and desired file name (e.g., website1_screenshot.png)
      - OUTPUT: URL of the saved screenshot image on the server [screenshot-url1], [screenshot-url2], ..., [screenshot-url10]

(Repeat steps 2a and 2b for each of the 10 URLs)

3. Compile a list of the saved screenshot image URLs
   - INPUT: [screenshot-url1], [screenshot-url2], ..., [screenshot-url10]
   - OUTPUT: [screenshot-urls]

4. Output the list of screenshot image URLs
   - INPUT: [screenshot-urls]
   - OUTPUT: [final-screenshot-urls]

H) OUTPUT TOKENS:
- [news-urls]: List of relevant news website URLs received from the Web Research Agent
- [news-url1], [news-url2], ..., [news-url10]: Individual URLs from the list of relevant news websites
- [screenshot-binary1], [screenshot-binary2], ..., [screenshot-binary10]: Binary data of the captured screenshot images for each website
- [screenshot-url1], [screenshot-url2], ..., [screenshot-url10]: URLs of the saved screenshot images on the server for each website
- [screenshot-urls]: List of the saved screenshot image URLs
- [final-screenshot-urls]: Final output list of screenshot image URLs

These output tokens can be used as inputs for subsequent tasks in the automated workflow. For example, [news-url1] can be used as an input for the "Take screenshot of website" task, and [screenshot-binary1] can be used as an input for the "Save content to server" task.

subagent2refined-repeating-json-core

A) SUBAGENT SUMMARY: Screenshot Capture and Storage Agent

B) FINAL TASK OUTPUT:
- A list of 10 URLs pointing to the saved screenshot images (PNG format, 1400x2400 pixels) on the server

C) SUBAGENT INPUT:
- A list of 10 relevant news website URLs from the Web Research Agent

E) SUBAGENT TASK SUMMARY:
1. Receive the list of relevant news website URLs from the Web Research Agent
2. For each URL in the list (assuming 10 URLs):
   a. Use the "Take screenshot of website" task to capture a full-page screenshot (1400x2400 pixels) of the website
   b. Use the "Save content to server" task to save the screenshot image (PNG format) on the server with a unique filename (e.g., website1_screenshot.png)
3. Compile a list of the saved screenshot image URLs
4. Output the list of screenshot image URLs

F) SILOS: None

G) TASKFLOW SUMMARY:

Task 1:
Title task: Receive news website URLs
Description of the task: Receive the list of 10 relevant news website URLs from the Web Research Agent
Input(s) involved: [list_of_news_website_urls]
Output token name: [list_of_news_website_urls]

Task 2a:
Title task: Capture website screenshot
Description of the task: Use the "Take screenshot of website" task to capture a full-page screenshot (1400x2400 pixels) of the website
Input(s) involved: [url_of_website]
Output token name: [website_screenshot_binary_data]

Task 2b:
Title task: Save screenshot to server
Description of the task: Use the "Save content to server" task to save the screenshot image (PNG format) on the server with a unique filename (e.g., website1_screenshot.png)
Input(s) involved: [website_screenshot_binary_data], [file_extension], [desired_file_name]
Output token name: [saved_screenshot_url]

Task 3:
Title task: Compile saved screenshot URLs
Description of the task: Compile a list of the saved screenshot image URLs
Input(s) involved: [saved_screenshot_url] (repeated for each screenshot)
Output token name: [list_of_saved_screenshot_urls]

Task 4:
Title task: Output screenshot URLs
Description of the task: Output the list of screenshot image URLs
Input(s) involved: [list_of_saved_screenshot_urls]
Output token name: [list_of_saved_screenshot_urls]

subagent2mermaid

SubAgent #3

claude 3 - subagent3

subagent1-refined

A) SUBAGENT SUMMARY: The Image Analysis and Cropping Agent analyzes website screenshots, identifies and crops relevant containers, and saves the cropped images on the server.

B) FINAL TASK OUTPUT: List of URLs pointing to cropped PNG images (1:3 to 3:1 aspect ratio) saved on the server, each representing a "TOP NEWS" or "LATEST" container from the respective website screenshots.

C) SUBAGENT INPUT:
- List of URLs pointing to full-page website screenshot images (PNG or JPEG format) saved on the server

E) SUBAGENT TASK SUMMARY:
1. Receive the list of website screenshot URLs as input
2. For each website screenshot URL:
   a. Extract visual data from image (task 5) to analyze the screenshot and identify the presence and coordinates of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1
   b. If a relevant container is found:
      i. Crop image (task 25) using the identified coordinates
      ii. Save cropped image to server (task 9) as a PNG file
      iii. Add the URL of the saved cropped image to the output list
   c. If no relevant container is found, proceed to the next screenshot
3. Output the list of URLs pointing to the cropped images saved on the server

F) SILOS:
Silo 1: Container Identification
- Extract visual data from image (task 5) for each website screenshot to identify the presence and coordinates of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1

Silo 2: Image Cropping and Saving
- For each screenshot with an identified relevant container:
  - Crop image (task 25) using the identified coordinates
  - Save cropped image to server (task 9) as a PNG file
  - Add the URL of the saved cropped image to the output list

Silo 3: Output Generation
- Compile the list of URLs pointing to the cropped images saved on the server
- Output the list of cropped image URLs

subagent1refined-repeating

A) SUBAGENT SUMMARY: The Image Analysis and Cropping Agent analyzes website screenshots, identifies and crops relevant containers, and saves the cropped images on the server.

C) SUBAGENT INPUT:
- List of URLs pointing to full-page website screenshot images (PNG or JPEG format) saved on the server

Silo 3: Output Generation
- Compile the list of URLs pointing to the cropped images saved on the server
- Output the list of cropped image URLs

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive the list of website screenshot URLs as input
2. For each website screenshot URL:
   a. Load website screenshot image from URL
   b. Extract visual data from image (task 5)
   c. Analyze extracted visual data to identify the presence of a "TOP NEWS" or "LATEST" container
   d. If a relevant container is found:
      i. Calculate the aspect ratio of the identified container
      ii. If the aspect ratio is between 1:3 and 3:1:
         1. Extract the coordinates of the identified container
         2. Crop image (task 25) using the identified coordinates
         3. Generate a unique filename for the cropped image
         4. Save cropped image to server (task 9) as a PNG file
         5. Add the URL of the saved cropped image to the output list
      iii. If the aspect ratio is not between 1:3 and 3:1, proceed to the next website screenshot URL
   e. If no relevant container is found, proceed to the next website screenshot URL
3. Compile the list of URLs pointing to the cropped images saved on the server
4. Output the list of cropped image URLs

Subagent1 task-ID-numbers3

subagent3-output-variables

Here's the updated task list with the output tokens added:

--------------
A) SUBAGENT SUMMARY: The Image Analysis and Cropping Agent analyzes website screenshots, identifies and crops relevant containers, and saves the cropped images on the server.

C) SUBAGENT INPUT:
- List of URLs pointing to full-page website screenshot images (PNG or JPEG format) saved on the server [screenshot-urls]

E) SUBAGENT TASK SUMMARY:
1. Receive the list of website screenshot URLs as input [screenshot-urls]
2. For each website screenshot URL [screenshot-url]:
   a. Extract visual data from image (task 5) to analyze the screenshot and identify the presence and coordinates of a "TOP NEWS" or "LATEST" container with an aspect ratio between 1:3 and 3:1 [container-coordinates]
   b. If a relevant container is found:
      i. Crop image (task 25) using the identified coordinates [cropped-image]
      ii. Save cropped image to server (task 9) as a PNG file [cropped-image-url]
      iii. Add the URL of the saved cropped image to the output list [cropped-image-urls]
   c. If no relevant container is found, proceed to the next screenshot
3. Output the list of URLs pointing to the cropped images saved on the server [cropped-image-urls]

Silo 2: Image Cropping and Saving
- For each screenshot with an identified relevant container:
  - Crop image (task 25) using the identified coordinates [cropped-image]
  - Save cropped image to server (task 9) as a PNG file [cropped-image-url]
  - Add the URL of the saved cropped image to the output list [cropped-image-urls]

Silo 3: Output Generation
- Compile the list of URLs pointing to the cropped images saved on the server [cropped-image-urls]
- Output the list of cropped image URLs [cropped-image-urls]

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive the list of website screenshot URLs as input [screenshot-urls]
2. For each website screenshot URL [screenshot-url]:
   a. Load website screenshot image from URL [screenshot-image]
   b. Extract visual data from image (task 5) [visual-data]
   c. Analyze extracted visual data to identify the presence of a "TOP NEWS" or "LATEST" container [container-presence]
   d. If a relevant container is found:
      i. Calculate the aspect ratio of the identified container [container-aspect-ratio]
      ii. If the aspect ratio is between 1:3 and 3:1:
         1. Extract the coordinates of the identified container [container-coordinates]
         2. Crop image (task 25) using the identified coordinates [cropped-image]
         3. Generate a unique filename for the cropped image [cropped-image-filename]
         4. Save cropped image to server (task 9) as a PNG file [cropped-image-url]
         5. Add the URL of the saved cropped image to the output list [cropped-image-urls]
      iii. If the aspect ratio is not between 1:3 and 3:1, proceed to the next website screenshot URL
   e. If no relevant container is found, proceed to the next website screenshot URL
3. Compile the list of URLs pointing to the cropped images saved on the server [cropped-image-urls]
4. Output the list of cropped image URLs [cropped-image-urls]

H) OUTPUT TOKENS:
- [screenshot-urls]: List of URLs pointing to full-page website screenshot images saved on the server
- [screenshot-url]: Individual URL pointing to a website screenshot image
- [screenshot-image]: Loaded website screenshot image from URL
- [visual-data]: Extracted visual data from the website screenshot image
- [container-presence]: Identified presence of a "TOP NEWS" or "LATEST" container in the screenshot
- [container-aspect-ratio]: Calculated aspect ratio of the identified container
- [container-coordinates]: Extracted coordinates of the identified container
- [cropped-image]: Cropped image using the identified container coordinates
- [cropped-image-filename]: Generated unique filename for the cropped image
- [cropped-image-url]: URL of the saved cropped image on the server
- [cropped-image-urls]: List of URLs pointing to the cropped images saved on the server
--------------

subagent3refined-repeating-json-core

G) TASKFLOW SUMMARY

Title task: Load website screenshot image
Description of the task: Retrieve the website screenshot image from the provided URL and load it into memory for further processing.
Input(s) involved: [website_screenshot_url]
Output token name: [loaded_screenshot_image]

Title task: Extract visual data from image
Description of the task: Analyze the loaded website screenshot image to extract relevant visual data, such as image dimensions, color information, and object detection data.
Input(s) involved: [loaded_screenshot_image]
Output token name: [extracted_visual_data]

Title task: Identify relevant container
Description of the task: Process the extracted visual data to identify the presence and coordinates of a "TOP NEWS" or "LATEST" container within the website screenshot.
Input(s) involved: [extracted_visual_data]
Output token name: [identified_container]

Title task: Calculate container aspect ratio
Description of the task: Determine the aspect ratio of the identified container by calculating the ratio of its width to its height.
Input(s) involved: [identified_container]
Output token name: [container_aspect_ratio]

Title task: Extract container coordinates
Description of the task: Retrieve the coordinates (x, y, width, height) of the identified container from the extracted visual data.
Input(s) involved: [identified_container]
Output token name: [container_coordinates]

Title task: Crop image
Description of the task: Using the extracted container coordinates, crop the loaded website screenshot image to isolate the relevant container.
Input(s) involved: [loaded_screenshot_image], [container_coordinates]
Output token name: [cropped_image]

Title task: Generate unique filename
Description of the task: Create a unique filename for the cropped image, incorporating relevant information such as the website name and a timestamp.
Input(s) involved: [website_screenshot_url], [current_timestamp]
Output token name: [unique_filename]

Title task: Save cropped image to server
Description of the task: Save the cropped image to the server using the generated unique filename, ensuring it is stored in PNG format.
Input(s) involved: [cropped_image], [unique_filename]
Output token name: [saved_cropped_image_url]

Title task: Add cropped image URL to output list
Description of the task: Append the URL of the saved cropped image to the list of output URLs, which will be compiled and returned at the end of the process.
Input(s) involved: [saved_cropped_image_url], [output_url_list]
Output token name: [updated_output_url_list]

Title task: Compile output URL list
Description of the task: Aggregate all the saved cropped image URLs into a single list, ensuring that only valid URLs are included.
Input(s) involved: [updated_output_url_list]
Output token name: [compiled_output_url_list]

Title task: Output cropped image URLs
Description of the task: Return the compiled list of cropped image URLs as the final output of the subagent, making it available for further processing or storage.
Input(s) involved: [compiled_output_url_list]
Output token name: [subagent_output]

subagent3mermaid

prompts-required

Based on the provided components, here are the unique prompts that would need to be written for this agent workflow:

Task: Keyword Relevance Analysis
Output of the task: A relevance score for each website based on the input keyword
Description: A prompt that takes the input keyword and the text content of each website as input, and uses natural language processing techniques to analyze the relevance of the website to the keyword. The output would be a relevance score for each website.

Task: Website Categorization
Output of the task: A set of relevant information for each website (traffic rank, keyword relevance, content summary)
Description: A prompt that takes the relevance score, traffic rank, and text content of each website as input, and generates a categorization of the website. The output would include the traffic rank, keyword relevance, and a brief summary of the website's content.

Task: Title and Description Generation
Output of the task: A final title and description for each website
Description: A prompt that takes the categorization information (traffic rank, keyword relevance, content summary) as input, and generates a final title and description for each website. The title should be attention-grabbing and the description should accurately summarize the website's content.

Task: Keyword Generation
Output of the task: A set of related keywords (1-5) and secondary keywords (6-20) for each website
Description: A prompt that takes the categorization information (traffic rank, keyword relevance, content summary) and the text content of each website as input, and generates a set of related keywords (1-5) and secondary keywords (6-20) for each website. The related keywords should be closely relevant to the website's content, while the secondary keywords can be more broadly related.

Task: Error Handling
Output of the task: A set of potential error scenarios and corresponding solutions
Description: A prompt that identifies potential error scenarios that could occur during the execution of the workflow (e.g., unable to capture screenshot, no relevant container found, etc.) and provides corresponding solutions to handle these errors gracefully. The output would be a set of error scenarios and their respective handling mechanisms.

These prompts would need to be carefully crafted to ensure that the LLM can generate the desired outputs based on the provided inputs. The prompts should be clear, specific, and provide enough context for the LLM to understand the task at hand.

subagent1-json-array

[
  {
    "taskID": 1,
    "semanticTitleOfTask": "User inputs search query",
    "taskDescription": "The user enters a search query or topic to research news websites on",
    "inputDescription": "A string containing the user's search query or topic",
    "inputRequired": [],
    "outputDescription": "The user's search query or topic as a string",
    "outputName": "user_search_query",
    "promptInstruction": ""
  },
  {
    "taskID": 2,
    "semanticTitleOfTask": "Search Google for query and extract top URLs",
    "taskDescription": "Searches Google for the user's query, extracts the top 20 URLs from the search results",
    "inputDescription": "The user's search query or topic from task 1",
    "inputRequired": ["user_search_query"],
    "outputDescription": "A list of the top 20 URLs from the Google search results for the user's query",
    "outputName": "top_20_urls",
    "promptInstruction": ""
  },
  {
    "taskID": 3,
    "semanticTitleOfTask": "Scrape text content from each of the top 20 URLs",
    "taskDescription": "Visits each of the top 20 URLs and scrapes the main text content from the page",
    "inputDescription": "The list of top 20 URLs from the Google search results in task 2",
    "inputRequired": ["top_20_urls"],
    "outputDescription": "The main text content scraped from each of the top 20 URLs",
    "outputName": "scraped_text_content_top20",
    "promptInstruction": ""
  },
  {
    "taskID": 4,
    "semanticTitleOfTask": "Save scraped text content to server",
    "taskDescription": "Saves the scraped text content from each of the top 20 URLs to the server",
    "inputDescription": "The scraped text content from each of the top 20 URLs in task 3",
    "inputRequired": ["scraped_text_content_top20"],
    "outputDescription": "The file paths or URLs of the saved text content files on the server",
    "outputName": "saved_text_content_top20",
    "promptInstruction": ""
  },
  {
    "taskID": 5,
    "semanticTitleOfTask": "Find 5 most relevant keywords from the scraped text content",
    "taskDescription": "Analyzes the scraped text content to identify the top 5 most relevant keywords",
    "inputDescription": "The scraped text content from each of the top 20 URLs in task 3",
    "inputRequired": ["scraped_text_content_top20"],
    "outputDescription": "A list of the top 5 most relevant keywords extracted from the scraped text content",
    "outputName": "top_5_relevant_keywords",
    "promptInstruction": ""
  },
  {
    "taskID": 6,
    "semanticTitleOfTask": "For each of the top 5 keywords, search Google and extract top 20 URLs",
    "taskDescription": "Conducts a Google search for each of the top 5 keywords, extracts the top 20 URLs from each set of search results",
    "inputDescription": "The list of top 5 most relevant keywords from task 5",
    "inputRequired": ["top_5_relevant_keywords"],
    "outputDescription": "A list of the top 20 URLs from the Google search results for each of the top 5 keywords",
    "outputName": "top20_urls_per_keyword",
    "promptInstruction": ""
  },
  {
    "taskID": 7,
    "semanticTitleOfTask": "Scrape text content from each set of 20 URLs per keyword",
    "taskDescription": "Visits each of the 20 URLs for the 5 keywords and scrapes the main text content",
    "inputDescription": "The list of top 20 URLs for each of the top 5 keywords from task 6",
    "inputRequired": ["top20_urls_per_keyword"],
    "outputDescription": "The main text content scraped from each of the 20 URLs for the 5 keywords",
    "outputName": "scraped_text_content_per_keyword",
    "promptInstruction": ""
  },
  {
    "taskID": 8,
    "semanticTitleOfTask": "Save scraped text content for each keyword to server",
    "taskDescription": "Saves the scraped text content from the 20 URLs for each of the 5 keywords to the server",
    "inputDescription": "The scraped text content from each of the 20 URLs for the 5 keywords in task 7",
    "inputRequired": ["scraped_text_content_per_keyword"],
    "outputDescription": "The file paths or URLs of the saved text content files for each keyword on the server",
    "outputName": "saved_text_content_per_keyword",
    "promptInstruction": ""
  },
  {
    "taskID": 9,
    "semanticTitleOfTask": "Concatenate all saved text content and send to GPT-4",
    "taskDescription": "Combines all the saved text content files into one and passes it to GPT-4 for analysis and filtering",
    "inputDescription": "The file paths or URLs of the saved text content files from tasks 4 and 8",
    "inputRequired": ["saved_text_content_top20", "saved_text_content_per_keyword"],
    "outputDescription": "A filtered list of the most relevant URLs based on GPT-4's analysis of the concatenated text content",
    "outputName": "filtered_url_list",
    "promptInstruction": "Analyze the provided text content from various news websites and identify the most relevant URLs based on factors such as keyword relevance, website authority, and content freshness. Return a filtered list of the top 10 most relevant URLs."
  },
  {
    "taskID": 10,
    "semanticTitleOfTask": "Save the final filtered list of URLs to server",
    "taskDescription": "Takes the filtered list of URLs output by GPT-4 and saves it to the server",
    "inputDescription": "The filtered list of the most relevant URLs from GPT-4's analysis in task 9",
    "inputRequired": ["filtered_url_list"],
    "outputDescription": "The file path or URL of the saved list of filtered URLs on the server",
    "outputName": "final_url_list",
    "promptInstruction": ""
  }
]

subagent1-json-array-mermaid

subagent2-json-array

[
  {
    "taskID": 1,
    "semanticTitleOfTask": "Receive news website URLs",
    "taskDescription": "Receive the list of 10 relevant news website URLs from the Web Research Agent",
    "inputDescription": "A list of 10 news website URLs generated by the Web Research Agent",
    "inputRequired": ["list_of_news_website_urls"],
    "outputDescription": "The received list of 10 news website URLs",
    "outputName": "list_of_news_website_urls",
    "promptInstruction": ""
  },
  {
    "taskID": 2,
    "semanticTitleOfTask": "Capture website screenshot",
    "taskDescription": "Use the \"Take screenshot of website\" task to capture a full-page screenshot (1400x2400 pixels) of the website",
    "inputDescription": "A single URL from the list of news website URLs",
    "inputRequired": ["url_of_website"],
    "outputDescription": "The binary data of the captured full-page screenshot",
    "outputName": "website_screenshot_binary_data",
    "promptInstruction": ""
  },
  {
    "taskID": 3,
    "semanticTitleOfTask": "Save screenshot to server",
    "taskDescription": "Use the \"Save content to server\" task to save the screenshot image (PNG format) on the server with a unique filename (e.g., website1_screenshot.png)",
    "inputDescription": "The binary data of the captured full-page screenshot, desired file extension (PNG), and desired filename for the saved screenshot",
    "inputRequired": ["website_screenshot_binary_data", "file_extension", "desired_file_name"],
    "outputDescription": "The URL of the saved screenshot image on the server",
    "outputName": "saved_screenshot_url",
    "promptInstruction": ""
  },
  {
    "taskID": 4,
    "semanticTitleOfTask": "Compile saved screenshot URLs",
    "taskDescription": "Compile a list of the saved screenshot image URLs",
    "inputDescription": "The URLs of the saved screenshot images on the server (repeated for each screenshot)",
    "inputRequired": ["saved_screenshot_url"],
    "outputDescription": "A list of the saved screenshot image URLs",
    "outputName": "list_of_saved_screenshot_urls",
    "promptInstruction": ""
  },
  {
    "taskID": 5,
    "semanticTitleOfTask": "Output screenshot URLs",
    "taskDescription": "Output the list of screenshot image URLs",
    "inputDescription": "The compiled list of saved screenshot image URLs",
    "inputRequired": ["list_of_saved_screenshot_urls"],
    "outputDescription": "The final list of screenshot image URLs, ready for use by the next subagent",
    "outputName": "list_of_saved_screenshot_urls",
    "promptInstruction": ""
  }
]

Cut out news website XY

Sub Agent #1 | Sub Agent #2 | Sub Agent #3

final-output-comps..1

final-output-comps..2

final-output-comps GPT..3

CLAUDE:- final-output-and-component-outputs

claude 3 - extract final output

extract component outputs

Claude - research-ideas-and-input-variables

GPT - research-ideas-and-input-variables

template-ideas

claude 3 - searches

subagent-summary

subagent-summary-amended-for-actual-tasks..1

subagent-summary-amended-for-actual-tasks..2

subagent-summary-amended-for-actual-tasks GPT..3

subagent-summary-amended-for-actual-tasks

random-autocomplete

claude 3 - mermaid-for-all

SubAgent #1

claude 3 - subagent1

subagent1-refined

subagent1refined-repeating

Subagent1 task-ID-numbers

subagent1-output-variables

subagent1refined-repeating-json-core

subagent1mermaid

SubAgent #2

claude 3 - subagent2

subagent2-refined

subagent2refined-repeating

Subagent2 task-ID-numbers

subagent2-output-variables

subagent2refined-repeating-json-core

subagent2mermaid

SubAgent #3

claude 3 - subagent3

subagent1-refined

subagent1refined-repeating

Subagent1 task-ID-numbers3

subagent3-output-variables

subagent3refined-repeating-json-core

subagent3mermaid

prompts-required

subagent1-json-array

subagent1-json-array-mermaid

subagent2-json-array

subagent2-json-array-mermaid