5. CSV 2 Software

I have built a software that imports a CSV file and converts it into a web app (which kind of displays the CSV in a password-protected webpage with some filter section at the top). For the CSV to import, it must contain 6 columns, the first of which must be the name of the entry, the second must be a URL for the entry, the third must be an image URL, columns 4-5 must be numbers (whether integers or decimal), and then the sixth and final column can be anything. In theory you - or any LLM - can generate this CSV file since it is just semi-colon delimited text. And the data to be entered into the CSV can be researched, by browsing the Internet, Google search, etc (and for the images we can either take screenshots of the relevant webpage, insert a relevant png from the nounproject or use Google images to find a relevant image). In short, we can in theory build such a web app on any topic. I therefore want to build a task whereby I can specify a type of web app I want (in a few sentences), the AI will decide upon the exact columns we will want in our CSV (the key component of this entire project), and then the agent will research the internet, build the CSV and then send it to my software tool via an API call (this final call to the software tool has not been added yet, but will be soon).

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

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

a) The final output of this process would be a web app that displays data from a CSV file in a password-protected webpage with a filter section at the top. The web app would be generated by your software tool, which takes the CSV file as input and converts it into the web app.

b) If the task is broken down into components, the core components/outputs needed to generate the final web app would be:

1. A clear and concise description of the desired web app topic and content, provided by the user to the AI agent. This should include the specific theme, purpose, and any key features or requirements for the web app.

2. A well-structured CSV file containing the data to be displayed in the web app. The CSV file must have 6 columns:
   - Column 1: The name of each entry (string)
   - Column 2: A URL for each entry (string)
   - Column 3: An image URL for each entry (string)
   - Columns 4-5: Numeric values (integer or decimal)
   - Column 6: Any additional data (string)

The AI agent will be responsible for deciding on the exact column names and researching the relevant data to populate the CSV file.

3. The images referenced in the CSV file's third column. These images can be obtained by the AI agent through various methods, such as taking screenshots of relevant webpages, inserting relevant PNGs from the nounproject, or using Google Images to find suitable images. The AI agent will need to ensure that the image URLs in the CSV file are accessible and properly formatted.

4. An API call to send the generated CSV file to your software tool. The AI agent will need to make this API call once the CSV file is complete, passing the file as input to your software tool for processing.

5. The software tool itself, which takes the CSV file as input and generates the password-protected web app with the specified data and a filter section at the top. This tool is already developed by you and will be responsible for the final rendering of the web app based on the provided CSV data.

In summary, the core components are the web app description, the well-formatted CSV file containing the researched data and image URLs, the actual images, the API call to send the CSV to your software tool, and finally, the software tool that generates the web app from the CSV data.

GPT4:- final-output-and-component-outputs

a) The final output of this task is a functional web app which displays data refined from a CSV in a manageable, searchable and visual format. The CSV is generated by the AI based on a specific theme or topic provided, and contains appropriate data for each of the six column requirements. This CSV data is then sent to the web app conversion tool via an API call, which successfully converts it and presents it in a password-protected webpage.

b) Breakdown of the components required for this task:

1. **Web App Request**: This is the initial input from a user specifying the nature or theme for the web app. This directive helps the AI in determining the columns and the type of data for the CSV file.

2. **Web Research:** An AI-powered web research routine that can navigate the web to find the necessary data to populate the CSV file. This includes textual data, URLs and image URLs related to the theme requested.

3. **Image Processing:** An AI tool suited for finding and processing images. This might involve taking screenshots, finding relevant images on Google, or understanding and using the nounproject to acquire relevant pngs.

4. **CSV File Creation:** The AI should identify the appropriate data points from the researched data and format these into a semi-colon delimited CSV file according to the required structure: The first column containing name data, the second a URL, the third an image URL, the fourth and fifth columns containing numeric data and the final column containing any form of data consistent with the theme.

5. **API Interfacing:** A developed way for the AI agent to communicate with the web app conversion tool, sending the created CSV file for conversion. This would involve understanding the API's request and response system.

6. **Web App Verification:** Finally, the AI would need to verify that the output web app is functional and correctly password protected. This might involve testing the functionality of the web app, ensuring it displays the correct data in the intended format with a functioning filter system.

claude 3 - extract final output

extract component outputs

b) If the task is broken down into components, the core components/outputs needed to generate the final web app would be:

The AI agent will be responsible for deciding on the exact column names and researching the relevant data to populate the CSV file.

Claude - research-ideas-and-input-variables

Thank you for the detailed explanation of your project. Let me address your questions one by one:

a) To clarify the final output of the task, I would ask the following questions:
1. What specific topic or theme should the web app cover? (e.g., a list of top restaurants in a city, a collection of famous paintings, etc.)
2. What should be the title of the web app?
3. Are there any specific requirements for the URL and image URL columns? (e.g., should the URLs be from specific domains or follow a certain format?)
4. What range of values should the numeric columns (columns 4-5) cover? Are there any specific requirements for these columns?
5. Is there any specific requirement for the sixth column, or can it contain any type of data?
6. How many entries should the CSV file contain?

b) Before launching the task completion process, the input should include:
1. The topic or theme of the web app (e.g., "Top 50 Restaurants in New York City")
2. The desired title of the web app
3. Any specific requirements for the URL and image URL columns
4. The range of values or any specific requirements for the numeric columns
5. Any specific requirement for the sixth column
6. The number of entries the CSV file should contain

c) I have no insights or suggestions as to how we might want to silo and segment any of the core tasks. The task of generating a CSV file with the specified columns and researching the relevant information can be performed as a single, cohesive process. However, if the web app requires additional features or complex data relationships, it might be beneficial to break down the task into smaller, specialized components.

GPT - research-ideas-and-input-variables

a) Clarification questions for the final output:

1. What type of content should be primarily displayed on the web app? Should it be data-driven, informational, or promotional? For instance, if it's a wedding website, should it detail about the couple, venue, and date or it should showcase images and videos from the wedding?
2. What type of web app is required? Is it a static-page or a dynamic multi-page web app? If it’s a multi-page, what specific sub-sections or pages should be included?
3. What specific kind of filters are anticipated on the webpage - filters based on categories, dates, numeric values, or any other specific detail?
4. What is the overall theme, style, or branding to be followed?
5. Since we need URLs, there must be some online presence related to the topic of the app. Could you specify what forms of online presence are acceptable or preferred (like social media, websites, forums, etc)?
6. What kind of data is expected in the sixth column? Is some specific category required or it can be arbitrary?
7. How many entries should the web app support (both at launch and potential scale in the future)?

b) Inputs needed for task completion:

1. A detailed description or brief of the web app. This includes information like the specific content of the web app, type of web app (single page or multi-page), specific requirements for each page, intended audience, etc.
2. Clear specifications of the fields required in the CSV like the correct order, format, and source or method of obtaining (if any specific is required).
3. Theme, style, or design specifications for the web app to match branding.
4. Valid credentials for API calls. Also, necessary documentation regarding API use.
5. Preferred sources for data scraping and researching.
6. Specifications of image requirements (size, format, where to source them from).
7. Guidelines on how to handle data privacy and any specific laws that need to be considered during the scraping process (especially for personally identifiable information).

template-ideas

Based on the description of the final output and the considerations you mentioned, here are a few template suggestions that could help refine the definition of a successful output for your task:

1. Example CSV files:
   - Search for and collect example CSV files that have a similar structure to what your software tool expects (6 columns with specific data types).
   - These examples can serve as a reference for the AI agent when generating the CSV file for your web app.

2. Screenshots or demo videos of similar web apps:
   - Look for existing web apps that display data from a CSV or similar structured data in a tabular format with filters.
   - Collect screenshots or demo videos of these web apps to visualize how the data is presented and how the filters work.
   - The AI agent can use these visual references to understand the desired layout and functionality of your web app.

3. HTML/CSS code snippets of data tables and filter sections:
   - Search for code snippets or templates that demonstrate how to create data tables and filter sections in HTML/CSS.
   - These code snippets can provide a starting point or inspiration for the structure and styling of your web app.
   - The AI agent can analyze these code snippets to understand the underlying structure and adapt them to your specific requirements.

4. Descriptions or documentation of similar software tools:
   - Look for software tools or libraries that convert CSV files into web apps or generate data-driven websites.
   - Collect descriptions, documentation, or tutorials that explain how these tools work and what features they offer.
   - The AI agent can review these materials to gain insights into common functionalities and best practices for building CSV-driven web apps.

5. User reviews or feedback on similar web apps:
   - Search for user reviews, feedback, or testimonials related to web apps that display data from CSV files.
   - These reviews can highlight the key features, usability aspects, and benefits that users appreciate in such web apps.
   - The AI agent can analyze this feedback to identify desirable characteristics and incorporate them into your web app.

By collecting these templates and examples, the AI agent can gain a clearer understanding of what constitutes a successful output for your task. It can use these references to guide its decision-making process, ensure that the generated CSV file meets the required structure, and align the web app's functionality and presentation with user expectations and industry standards.

Remember to provide clear instructions to the AI agent on how to access and utilize these templates effectively. Regularly review and update the templates as needed to maintain their relevance and effectiveness in guiding the AI agent towards successful outputs.

claude 3 - searches

Based on the suggestions I provided above, here are 5 specific keyphrases you can use to search for templates and examples to refine the definition of a successful output for your task:

1. "example csv file with 6 columns url image numbers"
   This search will help you find example CSV files that have a similar structure to what your software tool expects, with 6 columns containing specific data types like URL, image, and numbers.

2. "web app display csv data filters demo video"
   This search will help you find demo videos or screenshots of web apps that display data from a CSV file in a tabular format with filters, similar to what you want to achieve with your software tool.

3. "html css code snippet data table filters codepen"
   This search will help you find code snippets or templates on platforms like CodePen that demonstrate how to create data tables and filter sections in HTML/CSS, which can serve as a starting point for the structure and styling of your web app.

4. "csv to web app converter tool documentation"
   This search will help you find documentation or tutorials for software tools or libraries that convert CSV files into web apps or generate data-driven websites, providing insights into common functionalities and best practices.

5. "data driven web app csv user reviews feedback"
   This search will help you find user reviews, feedback, or testimonials related to web apps that display data from CSV files, highlighting the key features, usability aspects, and benefits that users appreciate in such web apps.

These specific keyphrases are designed to quickly unearth relevant templates and examples that can help refine your understanding of what constitutes a successful output for your task. By including specific keywords like "example," "demo," "code snippet," "documentation," and "user reviews," you are more likely to find practical and actionable templates that the AI agent can study and learn from.

Remember to review the search results carefully, focusing on the most relevant and high-quality templates that align with your task requirements. Provide clear instructions to the AI agent on how to access and analyze these templates effectively to guide its decision-making process and ensure a successful output.

subagent-summary

A) FULL TASK SUMMARY: Create a web app that displays data from a user-defined CSV file in a password-protected webpage with a filter section.

B) FINAL TASK OUTPUT: A fully functional web application that allows users to securely access and filter data from a custom CSV file. The web app will be generated by a proprietary software tool that converts the CSV file into a password-protected webpage. The CSV file will contain 6 columns: name (string), URL (string), image URL (string), two numeric values (integer or decimal), and an additional data column (string). The web app will be hosted on a server and accessible via a unique URL.

C) COMPONENTS OVERVIEW:
1. User input: A clear description of the desired web app topic and content.
2. CSV file: A well-structured CSV file containing the data to be displayed in the web app.
3. Images: Relevant images referenced in the CSV file's third column.
4. API call: An API call to send the generated CSV file to the proprietary software tool.
5. Software tool: A tool that generates the password-protected web app from the CSV data.

D) OVERVIEW OF SUBAGENTS:
1. User Input Processor: Collects and clarifies the user's input for the web app topic and content.
2. Data Researcher: Researches and collects relevant data based on the user's input.
3. CSV Generator: Generates a well-structured CSV file using the collected data.
4. Image Collector: Obtains relevant images for each entry in the CSV file.
5. API Communicator: Sends the generated CSV file to the proprietary software tool via an API call.

E) SUBAGENT 1: User Input Processor
- Name: User Input Processor
- Final output: A clear and concise description of the desired web app topic and content.
- Accomplishment: The subagent will interact with the user through a chat interface, asking questions to clarify the web app's theme, purpose, and any key features or requirements. It will use natural language processing to analyze the user's responses and generate a structured summary of the web app's specifications.
- Sequence:
  1. Initiate a conversation with the user and request a brief description of the web app.
  2. Ask follow-up questions to clarify the web app's theme, purpose, and key features.
  3. Analyze the user's responses using natural language processing.
  4. Generate a structured summary of the web app's specifications.
  5. Present the summary to the user for confirmation and make any necessary revisions.
  6. Output the final web app description.

F) SUBAGENT 2: Data Researcher
- Name: Data Researcher
- Final output: A list of relevant data points for each entry in the CSV file.
- Accomplishment: The subagent will use the web app description provided by the User Input Processor to search the internet for relevant data. It will employ web scraping techniques and APIs to collect data from various sources, such as websites, databases, and online repositories. The subagent will organize the collected data into a structured format suitable for the CSV Generator.
- Sequence:
  1. Receive the web app description from the User Input Processor.
  2. Break down the description into key topics and themes.
  3. Use web scraping techniques and APIs to search for relevant data on the internet.
  4. Collect and organize the data into a structured format.
  5. Validate the data for accuracy and relevance.
  6. Output the list of relevant data points for each entry in the CSV file.

G) SUBAGENT 3: CSV Generator
- Name: CSV Generator
- Final output: A well-structured CSV file containing the data to be displayed in the web app.
- Accomplishment: The subagent will take the list of relevant data points provided by the Data Researcher and generate a CSV file that adheres to the required structure (6 columns: name, URL, image URL, two numeric values, and an additional data column). It will ensure that the data is properly formatted and sanitized to prevent any issues when processing the CSV file.
- Sequence:
  1. Receive the list of relevant data points from the Data Researcher.
  2. Create a new CSV file with the required 6 columns.
  3. Iterate through the data points and populate the CSV file accordingly.
  4. Validate the CSV file for proper structure and formatting.
  5. Perform data sanitization to remove any potential issues (e.g., special characters, inconsistent formatting).
  6. Output the final CSV file.

H) SUBAGENT 4: Image Collector
- Name: Image Collector
- Final output: A collection of relevant images for each entry in the CSV file.
- Accomplishment: The subagent will use the data in the CSV file, particularly the name and URL columns, to find relevant images for each entry. It will employ various methods, such as taking screenshots of webpages, searching for relevant PNGs on the nounproject, or using Google Images to find suitable images. The subagent will ensure that the collected images are properly formatted and accessible via their respective URLs.
- Sequence:
  1. Receive the CSV file from the CSV Generator.
  2. Iterate through each row in the CSV file.
  3. For each row, use the name and URL columns to search for relevant images.
  4. Collect images using methods like webpage screenshots, nounproject PNGs, or Google Images.
  5. Validate the collected images for proper formatting and accessibility.
  6. Update the CSV file with the corresponding image URLs.
  7. Output the updated CSV file and the collection of images.

I) SUBAGENT 5: API Communicator
- Name: API Communicator
- Final output: A successful API call to send the generated CSV file to the proprietary software tool.
- Accomplishment: The subagent will establish a connection with the proprietary software tool's API and send the generated CSV file as input. It will handle any necessary authentication, data formatting, and error handling to ensure a successful transmission.
- Sequence:
  1. Receive the updated CSV file from the Image Collector.
  2. Establish a connection with the proprietary software tool's API.
  3. Authenticate the connection using the provided credentials.
  4. Format the CSV file data as required by the API.
  5. Send the formatted data to the API endpoint.
  6. Handle any errors or exceptions that may occur during the transmission.
  7. Confirm the successful receipt of the CSV file by the software tool.
  8. Output a status report indicating the success or failure of the API call.

subagent-summary-amended-for-actual-tasks

Thank you for providing the updated list of available tasks. Based on this information, I have revised the subagents and their corresponding tasks to complete the web app generation process.

SUBAGENT 1: Data Researcher
- Final output: A list of relevant data points for each entry in the CSV file.
- Sequence:
  1. Receive the web app description from the user input.
  2. Use the "Search Google" task (15) to find relevant data based on the web app description.
  3. Scrape the text content of the top relevant websites using the "Scrape text content of website" task (16).
  4. Analyze the scraped content using GPT4 (1) to extract relevant data points for each entry in the CSV file.
  5. Output the list of relevant data points.

SUBAGENT 2: CSV Generator
- Final output: A well-structured CSV file containing the data to be displayed in the web app.
- Sequence:
  1. Receive the list of relevant data points from the Data Researcher.
  2. Use GPT4 (1) to generate a well-structured CSV file with the required 6 columns based on the data points.
  3. Save the generated CSV file to the server using the "Save content to server" task (9).
  4. Output the URL of the saved CSV file.

SUBAGENT 3: Image Collector
- Final output: A collection of relevant images for each entry in the CSV file.
- Sequence:
  1. Receive the CSV file URL from the CSV Generator.
  2. For each entry in the CSV file, use the "Search Google Images" task (19) to find relevant images based on the name and URL columns.
  3. Save the top relevant image for each entry to the server using the "Save external web file/folder to server" task (18).
  4. Update the CSV file with the corresponding image URLs using GPT4 (1).
  5. Save the updated CSV file to the server using the "Save content to server" task (9).
  6. Output the URL of the updated CSV file.

SUBAGENT 4: API Communicator
- Final output: A successful API call to send the generated CSV file to the proprietary software tool.
- Sequence:
  1. Receive the updated CSV file URL from the Image Collector.
  2. Use GPT4 (1) to generate the necessary API call payload, including the CSV file data and any required authentication.
  3. REQUIRED: Make an API call to the proprietary software tool, passing the CSV file data as input.
  4. Handle any errors or exceptions that may occur during the API call.
  5. Output a status report indicating the success or failure of the API call.

SUBAGENT 5: Web App Tester
- Final output: A test report confirming the functionality and accessibility of the generated web app.
- Sequence:
  1. Receive the web app URL from the API Communicator (assuming the proprietary software tool provides this after processing the CSV file).
  2. Use the "Scrape text content of website" task (16) to extract the text content of the web app.
  3. Use GPT4 (1) to analyze the scraped content and ensure that the data from the CSV file is properly displayed.
  4. Use the "Take screenshot of website" task (20) to capture screenshots of the web app.
  5. Use the "Extract visual data from image" task (5) to analyze the screenshots and ensure that the web app's layout and design are as expected.
  6. Output a test report summarizing the findings and confirming the web app's functionality and accessibility.

These subagents, utilizing the available tasks, should be able to complete the web app generation process as described in the original objective. The only missing piece is the API call to the proprietary software tool, which is marked as "REQUIRED" for you to implement.

random-autocomplete

To get the end goal of generating a web app that displays data from a CSV file in a password-protected webpage with a filter section, the most relevant tasks might be:

1. Receive and understand the user's input describing the desired web app topic and content.
2. Determine the appropriate columns for the CSV file based on the user's input and the requirements of the software tool (first column: entry name, second column: URL, third column: image URL, fourth and fifth columns: numbers, sixth column: any data).
3. Research and gather relevant data from the internet, such as websites, images, and numeric information related to the chosen topic.
4. Organize and structure the collected data into the determined CSV format.
5. Generate or acquire relevant images for each entry in the CSV file, either by taking screenshots, using images from the nounproject, or searching Google Images.
6. Create the final CSV file with all the collected and formatted data.
7. Submit the CSV file to the software tool via an API call.
8. Receive confirmation from the software tool that the web app has been successfully generated.

The overall sequence would be:
Understand user input > Determine CSV columns > Research and gather data > Organize data into CSV format > Generate or acquire images > Create final CSV file > Submit CSV to software tool via API > Receive confirmation of web app generation

To accomplish this task, the Agent would need to:

1. Use natural language processing to interpret the user's input and extract the necessary information about the desired web app topic and content.
2. Apply a decision-making algorithm to determine the most appropriate columns for the CSV file based on the user's input and the software tool's requirements.
3. Utilize web scraping, API calls, or other data collection methods to gather relevant information from various internet sources.
4. Employ data manipulation and formatting techniques to structure the collected information into the required CSV format.
5. Use image processing and generation tools, such as screenshot capture, API calls to the nounproject, or Google Image search, to acquire relevant images for each entry in the CSV file.
6. Combine the formatted data and images into a final CSV file.
7. Make an API call to the software tool, passing the CSV file as input.
8. Process the response from the software tool to confirm the successful generation of the web app.

With the Agent's access to a wide range of APIs, Python libraries, and a Windows 11 virtual machine, it should be capable of handling the various tasks involved in this process, from understanding user input to generating the final web app.

claude 3 - mermaid-for-all

SubAgent #1

claude 3 - subagent1

subagent1-refined

A) SUBAGENT SUMMARY: The Data Researcher subagent will search the internet for relevant data based on a user-provided web app description, scrape the text content from the top relevant websites, and analyze the scraped content to extract relevant data points for each entry in the CSV file.

B) FINAL TASK OUTPUT: A list of relevant data points for each entry in the CSV file, with a minimum of 6 data points per entry (corresponding to the 6 required columns in the final CSV file). The data points will be in plain text format, organized as a JSON object with key-value pairs for each entry.

C) SUBAGENT INPUT: A clear and concise description of the desired web app topic and content, provided by the user. This should include the specific theme, purpose, and any key features or requirements for the web app.

E) SUBAGENT TASK SUMMARY:
1. Receive the web app description from the user input.
2. Use GPT-4 to generate a list of relevant search keywords based on the web app description.
3. For each search keyword:
   a. Use the "Search Google" task (15) to find relevant websites.
   b. Save the list of relevant website URLs to the server using the "Save content to server" task (9).
4. For each relevant website URL:
   a. Use the "Scrape text content of website" task (16) to extract the text content.
   b. Save the scraped text content to the server using the "Save content to server" task (9).
5. Use GPT-4 to analyze the scraped text content and extract relevant data points for each entry in the CSV file, ensuring a minimum of 6 data points per entry.
6. Structure the extracted data points as a JSON object with key-value pairs for each entry.
7. Save the JSON object containing the relevant data points to the server using the "Save content to server" task (9).
8. Output the URL of the saved JSON file containing the relevant data points.

F) SILOS:
SILO 1: Keyword Generation
- Input: Web app description from the user
- Output: List of relevant search keywords

SILO 2: Website URL Extraction
- Input: List of relevant search keywords
- Output: List of relevant website URLs

SILO 3: Text Content Scraping
- Input: List of relevant website URLs
- Output: Scraped text content from each website

SILO 4: Data Point Extraction
- Input: Scraped text content from each website
- Output: JSON object containing relevant data points for each entry in the CSV file

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the web app description from the user input.
2. Use GPT-4 to generate a list of relevant search keywords based on the web app description.
3. For each search keyword (assuming 5 search keywords):
   a. Use the "Search Google" task (15) to find relevant websites.
   b. Save the list of relevant website URLs to the server using the "Save content to server" task (9).
   (Repeat steps 3a and 3b for each of the 5 search keywords)
4. For each relevant website URL (assuming 3 relevant URLs per search keyword, resulting in a total of 15 URLs):
   a. Use the "Scrape text content of website" task (16) to extract the text content.
   b. Save the scraped text content to the server using the "Save content to server" task (9).
   (Repeat steps 4a and 4b for each of the 15 relevant website URLs)
5. Use GPT-4 to analyze the scraped text content from all 15 websites and extract relevant data points for each entry in the CSV file, ensuring a minimum of 6 data points per entry (assuming a total of 10 entries in the CSV file).
6. Structure the extracted data points as a JSON object with key-value pairs for each of the 10 entries.
7. Save the JSON object containing the relevant data points for all 10 entries to the server using the "Save content to server" task (9).
8. Output the URL of the saved JSON file containing the relevant data points for all 10 entries.

Total tasks:
- 1 user input task
- 1 keyword generation task
- 5 search tasks (1 for each search keyword)
- 5 URL list saving tasks (1 for each search keyword)
- 15 website scraping tasks (1 for each relevant URL)
- 15 scraped content saving tasks (1 for each relevant URL)
- 1 data point extraction task (for all 10 entries)
- 1 JSON object structuring task (for all 10 entries)
- 1 JSON file saving task
- 1 output task

Grand total: 46 individual tasks

Subagent1 task-ID-numbers

subagent1-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the web app description from the user input. [web-app-description]
2. Use GPT-4 to generate a list of relevant search keywords based on the web app description. [search-keywords]
3. For each search keyword (assuming 5 search keywords):
   a. Use the "Search Google" task (15) to find relevant websites. [keyword1-urls], [keyword2-urls], [keyword3-urls], [keyword4-urls], [keyword5-urls]
   b. Save the list of relevant website URLs to the server using the "Save content to server" task (9). [keyword1-urls-saved], [keyword2-urls-saved], [keyword3-urls-saved], [keyword4-urls-saved], [keyword5-urls-saved]
   (Repeat steps 3a and 3b for each of the 5 search keywords)
4. For each relevant website URL (assuming 3 relevant URLs per search keyword, resulting in a total of 15 URLs):
   a. Use the "Scrape text content of website" task (16) to extract the text content. [keyword1-url1-scraped], [keyword1-url2-scraped], [keyword1-url3-scraped], [keyword2-url1-scraped], [keyword2-url2-scraped], [keyword2-url3-scraped], [keyword3-url1-scraped], [keyword3-url2-scraped], [keyword3-url3-scraped], [keyword4-url1-scraped], [keyword4-url2-scraped], [keyword4-url3-scraped], [keyword5-url1-scraped], [keyword5-url2-scraped], [keyword5-url3-scraped]
   b. Save the scraped text content to the server using the "Save content to server" task (9). [keyword1-url1-scraped-saved], [keyword1-url2-scraped-saved], [keyword1-url3-scraped-saved], [keyword2-url1-scraped-saved], [keyword2-url2-scraped-saved], [keyword2-url3-scraped-saved], [keyword3-url1-scraped-saved], [keyword3-url2-scraped-saved], [keyword3-url3-scraped-saved], [keyword4-url1-scraped-saved], [keyword4-url2-scraped-saved], [keyword4-url3-scraped-saved], [keyword5-url1-scraped-saved], [keyword5-url2-scraped-saved], [keyword5-url3-scraped-saved]
   (Repeat steps 4a and 4b for each of the 15 relevant website URLs)
5. Use GPT-4 to analyze the scraped text content from all 15 websites and extract relevant data points for each entry in the CSV file, ensuring a minimum of 6 data points per entry (assuming a total of 10 entries in the CSV file). [extracted-data-points]
6. Structure the extracted data points as a JSON object with key-value pairs for each of the 10 entries. [structured-json]
7. Save the JSON object containing the relevant data points for all 10 entries to the server using the "Save content to server" task (9). [saved-json]
8. Output the URL of the saved JSON file containing the relevant data points for all 10 entries. [output-json-url]

H) OUTPUT TOKENS

- [web-app-description]
- [search-keywords]
- [keyword1-urls], [keyword2-urls], [keyword3-urls], [keyword4-urls], [keyword5-urls]
- [keyword1-urls-saved], [keyword2-urls-saved], [keyword3-urls-saved], [keyword4-urls-saved], [keyword5-urls-saved]
- [keyword1-url1-scraped], [keyword1-url2-scraped], [keyword1-url3-scraped], [keyword2-url1-scraped], [keyword2-url2-scraped], [keyword2-url3-scraped], [keyword3-url1-scraped], [keyword3-url2-scraped], [keyword3-url3-scraped], [keyword4-url1-scraped], [keyword4-url2-scraped], [keyword4-url3-scraped], [keyword5-url1-scraped], [keyword5-url2-scraped], [keyword5-url3-scraped]
- [keyword1-url1-scraped-saved], [keyword1-url2-scraped-saved], [keyword1-url3-scraped-saved], [keyword2-url1-scraped-saved], [keyword2-url2-scraped-saved], [keyword2-url3-scraped-saved], [keyword3-url1-scraped-saved], [keyword3-url2-scraped-saved], [keyword3-url3-scraped-saved], [keyword4-url1-scraped-saved], [keyword4-url2-scraped-saved], [keyword4-url3-scraped-saved], [keyword5-url1-scraped-saved], [keyword5-url2-scraped-saved], [keyword5-url3-scraped-saved]
- [extracted-data-points]
- [structured-json]
- [saved-json]
- [output-json-url]

subagent1refined-repeating-json-core

A) SUBAGENT SUMMARY: Web App Data Gathering and Structuring Subagent

B) FINAL TASK OUTPUT: A JSON file containing structured data points for 10 entries, each with a minimum of 6 data points, saved on the server. The JSON file URL is provided as the final output.

C) SUBAGENT INPUT:
- Web app description provided by the user

E) SUBAGENT TASK SUMMARY:
1. Receive the web app description from the user input.
2. Generate a list of 5 relevant search keywords based on the web app description using GPT-4.
3. For each search keyword, find 3 relevant websites using the "Search Google" task and save the list of URLs to the server using the "Save content to server" task.
4. For each relevant website URL (total of 15), scrape the text content using the "Scrape text content of website" task and save the scraped content to the server using the "Save content to server" task.
5. Analyze the scraped text content from all 15 websites using GPT-4 to extract relevant data points for each of the 10 entries in the CSV file, ensuring a minimum of 6 data points per entry.
6. Structure the extracted data points as a JSON object with key-value pairs for each of the 10 entries.
7. Save the JSON object containing the relevant data points for all 10 entries to the server using the "Save content to server" task.
8. Output the URL of the saved JSON file containing the relevant data points for all 10 entries.

F) SILOS: Not applicable for this subagent.

G) TASKFLOW SUMMARY:

1. Title task: Receive web app description from user
   Description of the task: Obtain the user's input describing the desired web app, including its purpose, content, and any specific requirements.
   Input(s) involved: [user_input_web_app_description]
   Output token name: web_app_description

2. Title task: Generate search keywords
   Description of the task: Use GPT-4 to analyze the web app description and generate a list of 5 relevant search keywords that will help find suitable content for the web app.
   Input(s) involved: [web_app_description]
   Output token name: search_keywords

3. Title task: Search Google for relevant websites (repeated 5 times)
   Description of the task: For each search keyword, use the "Search Google" task to find 3 relevant websites that contain useful information for the web app.
   Input(s) involved: [search_keyword]
   Output token name: relevant_website_urls_list_keyword_n (where n is 1-5)

4. Title task: Save relevant website URLs to server (repeated 5 times)
   Description of the task: For each list of relevant website URLs, use the "Save content to server" task to store the list on the server for later access.
   Input(s) involved: [relevant_website_urls_list_keyword_n] (where n is 1-5)
   Output token name: saved_relevant_website_urls_list_keyword_n (where n is 1-5)

5. Title task: Scrape text content from relevant websites (repeated 15 times)
   Description of the task: For each relevant website URL, use the "Scrape text content of website" task to extract the text content of the webpage.
   Input(s) involved: [relevant_website_url]
   Output token name: scraped_text_content_url_n (where n is 1-15)

6. Title task: Save scraped text content to server (repeated 15 times)
   Description of the task: For each scraped text content, use the "Save content to server" task to store the content on the server for later access.
   Input(s) involved: [scraped_text_content_url_n] (where n is 1-15)
   Output token name: saved_scraped_text_content_url_n (where n is 1-15)

7. Title task: Extract relevant data points from scraped content
   Description of the task: Use GPT-4 to analyze the scraped text content from all 15 websites and extract relevant data points for each of the 10 entries in the CSV file, ensuring a minimum of 6 data points per entry.
   Input(s) involved: [saved_scraped_text_content_url_1], [saved_scraped_text_content_url_2], ..., [saved_scraped_text_content_url_15]
   Output token name: extracted_data_points

8. Title task: Structure data points as JSON object
   Description of the task: Organize the extracted data points into a JSON object with key-value pairs for each of the 10 entries, maintaining a clear and consistent structure.
   Input(s) involved: [extracted_data_points]
   Output token name: structured_json_data

9. Title task: Save JSON object to server
   Description of the task: Use the "Save content to server" task to store the JSON object containing the relevant data points for all 10 entries on the server.
   Input(s) involved: [structured_json_data]
   Output token name: saved_json_file_url

10. Title task: Output URL of saved JSON file
    Description of the task: Provide the URL of the saved JSON file containing the relevant data points for all 10 entries, which will be used by the next subagent or as the final output of the workflow.
    Input(s) involved: [saved_json_file_url]
    Output token name: final_json_file_url

subagent1mermaid

SubAgent #2

claude 3 - subagent2

subagent2-refined

A) SUBAGENT SUMMARY: The CSV Generator subagent creates a well-structured CSV file containing the data to be displayed in the web app.

B) FINAL TASK OUTPUT: A well-structured CSV file containing 6 columns of data (name, URL, image URL, two numeric columns, and one additional data column) saved on the server with a corresponding URL.

C) SUBAGENT INPUT: A list of relevant data points for each entry in the CSV file, provided by the Data Researcher subagent.

E) SUBAGENT TASK SUMMARY:

1. Receive the list of relevant data points from the Data Researcher (INPUT: Plain text, max 4000 tokens).
2. Use GPT4 (1) to analyze the data points and determine the appropriate column names for the 6 required columns based on the data types and content (INPUT: Plain text data points, max 4000 tokens; OUTPUT: Plain text column names, max 4000 tokens).
3. Use GPT4 (1) to generate the CSV content by formatting the data points into a comma-separated structure, using the determined column names as headers (INPUT: Plain text data points and column names, max 4000 tokens; OUTPUT: Plain text CSV content, max 4000 tokens).
4. Save the generated CSV content to the server using the "Save content to server" task (9) (INPUT: CSV content and ".csv" file extension; OUTPUT: Internal server URL of the saved CSV file).
5. Output the URL of the saved CSV file (OUTPUT: Plain text URL, max 4000 tokens).

F) SILOS:

Silo 1: Determine CSV Structure
- Receive the list of relevant data points from the Data Researcher.
- Use GPT4 (1) to analyze the data points and determine the appropriate column names for the 6 required columns based on the data types and content.

Silo 2: Generate CSV Content
- Use GPT4 (1) to generate the CSV content by formatting the data points into a comma-separated structure, using the determined column names as headers.

Silo 3: Save and Output CSV File
- Save the generated CSV content to the server using the "Save content to server" task (9).
- Output the URL of the saved CSV file.

subagent2refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

In this specific subagent, there are no explicit instructions for repeating tasks or building the CSV file in multiple parts. The subagent is designed to receive a single list of data points, determine the column names, generate the CSV content, save it to the server, and output the URL of the saved file. Therefore, the full list of tasks remains the same as the original "E) SUBAGENT TASK SUMMARY" section, without any additional repetitions or duplications.

Subagent2 task-ID-numbers

subagent2-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the list of relevant data points from the Data Researcher (INPUT: Plain text, max 4000 tokens).
2. Use GPT4 (1) to analyze the data points and determine the appropriate column names for the 6 required columns based on the data types and content (INPUT: Plain text data points, max 4000 tokens; OUTPUT: Plain text column names, max 4000 tokens). [column-names]
3. Use GPT4 (1) to generate the CSV content by formatting the data points into a comma-separated structure, using the determined column names as headers (INPUT: Plain text data points and column names, max 4000 tokens; OUTPUT: Plain text CSV content, max 4000 tokens). [csv-content]
4. Save the generated CSV content to the server using the "Save content to server" task (9) (INPUT: CSV content and ".csv" file extension; OUTPUT: Internal server URL of the saved CSV file). [csv-url]
5. Output the URL of the saved CSV file (OUTPUT: Plain text URL, max 4000 tokens). [csv-url]

H) OUTPUT TOKENS

1. [column-names]: The output from task 2, containing the determined column names for the 6 required columns based on the data types and content of the input data points.
2. [csv-content]: The output from task 3, containing the generated CSV content formatted using the determined column names as headers.
3. [csv-url]: The output from task 4, containing the internal server URL of the saved CSV file. This output is also used in task 5.

subagent2refined-repeating-json-core

G) TASKFLOW SUMMARY

1. Receive data points
   Description: Receive the list of relevant data points from the Data Researcher
   Input(s) involved: Plain text, max 4000 tokens
   Output token name: Data points

2. Analyze data points and determine column names
   Description: Use GPT4 (1) to analyze the data points and determine the appropriate column names for the 6 required columns based on the data types and content
   Input(s) involved: Plain text data points, max 4000 tokens
   Output token name: Plain text column names, max 4000 tokens

3. Generate CSV content
   Description: Use GPT4 (1) to generate the CSV content by formatting the data points into a comma-separated structure, using the determined column names as headers
   Input(s) involved: Plain text data points and column names, max 4000 tokens
   Output token name: Plain text CSV content, max 4000 tokens

4. Save CSV content to server
   Description: Save the generated CSV content to the server using the "Save content to server" task (9)
   Input(s) involved: CSV content and ".csv" file extension
   Output token name: Internal server URL of the saved CSV file

5. Output URL of saved CSV file
   Description: Output the URL of the saved CSV file
   Input(s) involved: None
   Output token name: Plain text URL, max 4000 tokens

subagent2mermaid

SubAgent #3

claude 3 - subagent3

subagent1-refined

A) SUBAGENT SUMMARY: The Image Collector subagent searches for relevant images based on the CSV file entries, saves them to the server, and updates the CSV file with the corresponding image URLs.

B) FINAL TASK OUTPUT: A well-structured CSV file containing the original data and updated image URLs for each entry, saved on the server.

C) SUBAGENT INPUT:
- URL of the CSV file generated by the CSV Generator subagent
- Access to the "Search Google Images" task (19)
- Access to the "Save external web file/folder to server" task (18)
- Access to GPT4 (1) for updating the CSV file
- Access to the "Save content to server" task (9)

E) SUBAGENT TASK SUMMARY:
1. Receive the CSV file URL from the CSV Generator subagent
2. Use GPT4 (1) to read the CSV file and extract the name and URL columns for each entry
3. For each entry in the CSV file:
   a. Use the "Search Google Images" task (19) to find relevant images based on the name and URL
   b. Use GPT4 (1) to select the most relevant image URL from the search results
   c. Use the "Save external web file/folder to server" task (18) to save the selected image to the server
   d. Use GPT4 (1) to update the corresponding entry in the CSV file with the saved image URL
4. Use the "Save content to server" task (9) to save the updated CSV file to the server
5. Output the URL of the updated CSV file

F) SILOS:
Silo 1: Image Search and Selection
- For each entry in the CSV file:
  - Use the "Search Google Images" task (19) to find relevant images based on the name and URL
  - Use GPT4 (1) to select the most relevant image URL from the search results

Silo 2: Image Saving
- For each selected image URL:
  - Use the "Save external web file/folder to server" task (18) to save the image to the server

Silo 3: CSV File Updating
- Use GPT4 (1) to read the original CSV file and extract the name and URL columns for each entry
- For each entry in the CSV file:
  - Use GPT4 (1) to update the corresponding entry with the saved image URL
- Use the "Save content to server" task (9) to save the updated CSV file to the server

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the CSV file URL from the CSV Generator subagent
2. Use GPT4 (1) to read the CSV file and extract the name and URL columns for each entry
3. For each entry in the CSV file:
   a. Use the "Search Google Images" task (19) to find relevant images based on the name and URL
   b. Use GPT4 (1) to select the most relevant image URL from the search results
   c. Use the "Save external web file/folder to server" task (18) to save the selected image to the server
   d. Use GPT4 (1) to update the corresponding entry in the CSV file with the saved image URL
4. Use the "Save content to server" task (9) to save the updated CSV file to the server
5. Output the URL of the updated CSV file

Assuming the CSV file contains 10 entries, the full list of tasks, including repetitions, would be:

1. Receive the CSV file URL from the CSV Generator subagent
2. Use GPT4 (1) to read the CSV file and extract the name and URL columns for each entry
3. For entry 1:
   a. Use the "Search Google Images" task (19) to find relevant images based on the name and URL
   b. Use GPT4 (1) to select the most relevant image URL from the search results
   c. Use the "Save external web file/folder to server" task (18) to save the selected image to the server
   d. Use GPT4 (1) to update the corresponding entry in the CSV file with the saved image URL
4. For entry 2:
   a. Use the "Search Google Images" task (19) to find relevant images based on the name and URL
   b. Use GPT4 (1) to select the most relevant image URL from the search results
   c. Use the "Save external web file/folder to server" task (18) to save the selected image to the server
   d. Use GPT4 (1) to update the corresponding entry in the CSV file with the saved image URL
5. (Repeat steps 3-4 for entries 3-10)
6. Use the "Save content to server" task (9) to save the updated CSV file to the server
7. Output the URL of the updated CSV file

In this case, steps 3-4 are repeated for each entry in the CSV file, resulting in a total of 42 tasks (1 + 1 + (4 * 10) + 1 + 1).

Subagent1 task-ID-numbers3

subagent3-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the CSV file URL from the CSV Generator subagent [csv-file-url]
2. Use GPT4 (1) to read the CSV file [csv-file-url] and extract the name and URL columns for each entry [entry-names], [entry-urls]
3. For entry 1:
   a. Use the "Search Google Images" task (19) to find relevant images based on the name [entry-name-1] and URL [entry-url-1] [image-search-results-1]
   b. Use GPT4 (1) to select the most relevant image URL from the search results [image-search-results-1] [selected-image-url-1]
   c. Use the "Save external web file/folder to server" task (18) to save the selected image [selected-image-url-1] to the server [saved-image-url-1]
   d. Use GPT4 (1) to update the corresponding entry in the CSV file [csv-file-url] with the saved image URL [saved-image-url-1] [updated-csv-file-1]
4. For entry 2:
   a. Use the "Search Google Images" task (19) to find relevant images based on the name [entry-name-2] and URL [entry-url-2] [image-search-results-2]
   b. Use GPT4 (1) to select the most relevant image URL from the search results [image-search-results-2] [selected-image-url-2]
   c. Use the "Save external web file/folder to server" task (18) to save the selected image [selected-image-url-2] to the server [saved-image-url-2]
   d. Use GPT4 (1) to update the corresponding entry in the CSV file [updated-csv-file-1] with the saved image URL [saved-image-url-2] [updated-csv-file-2]
5. (Repeat steps 3-4 for entries 3-10)
   [updated-csv-file-3] to [updated-csv-file-10]
6. Use the "Save content to server" task (9) to save the updated CSV file [updated-csv-file-10] to the server [final-csv-file-url]
7. Output the URL of the updated CSV file [final-csv-file-url]

H) OUTPUT TOKENS

[csv-file-url]: The URL of the initial CSV file received from the CSV Generator subagent.
[entry-names]: The list of entry names extracted from the CSV file.
[entry-urls]: The list of entry URLs extracted from the CSV file.
[entry-name-n]: The name of the nth entry in the CSV file.
[entry-url-n]: The URL of the nth entry in the CSV file.
[image-search-results-n]: The search results for relevant images for the nth entry.
[selected-image-url-n]: The most relevant image URL selected from the search results for the nth entry.
[saved-image-url-n]: The URL of the saved image for the nth entry on the server.
[updated-csv-file-n]: The updated CSV file after adding the saved image URL for the nth entry.
[final-csv-file-url]: The URL of the final updated CSV file saved on the server.

subagent3refined-repeating-json-core

G) TASKFLOW SUMMARY

1. Title task: Read CSV file and extract name and URL columns
   Description of the task: Use GPT4 to read the CSV file provided by the CSV Generator subagent and extract the name and URL columns for each entry in the file.
   Input(s) involved: [CSV file URL]
   Output token name: [Extracted name and URL columns]

2. Title task: Find relevant images for each entry
   Description of the task: For each entry in the CSV file, use the "Search Google Images" task to find relevant images based on the name and URL of the entry.
   Input(s) involved: [Extracted name and URL columns]
   Output token name: [Relevant image search results]

3. Title task: Select the most relevant image URL
   Description of the task: Use GPT4 to analyze the image search results for each entry and select the most relevant image URL based on the entry's name and URL.
   Input(s) involved: [Relevant image search results]
   Output token name: [Selected image URL]

4. Title task: Save selected image to server
   Description of the task: Use the "Save external web file/folder to server" task to save the selected image for each entry to the server.
   Input(s) involved: [Selected image URL]
   Output token name: [Saved image URL]

5. Title task: Update CSV file with saved image URLs
   Description of the task: Use GPT4 to update the corresponding entry in the CSV file with the saved image URL for each entry.
   Input(s) involved: [Saved image URL], [Extracted name and URL columns]
   Output token name: [Updated CSV file]

6. Title task: Save updated CSV file to server
   Description of the task: Use the "Save content to server" task to save the updated CSV file, which now includes the image URLs for each entry, to the server.
   Input(s) involved: [Updated CSV file]
   Output token name: [Saved updated CSV file URL]

7. Title task: Output the URL of the updated CSV file
   Description of the task: Provide the URL of the updated CSV file, which now includes the image URLs for each entry, as the final output of the subagent.
   Input(s) involved: [Saved updated CSV file URL]
   Output token name: [Final updated CSV file URL]

subagent3mermaid

prompts-required

Based on the components listed above, here are the unique prompts that we will need to write for this agent workflow:

Task: Web App Topic and Content Description
Output of the task: A clear and concise description of the desired web app topic and content
Description: The user will provide a detailed prompt specifying the theme, purpose, and any key features or requirements for the web app they want to create. This prompt will serve as the input for the AI agent to understand the user's requirements and guide the subsequent tasks.

Task: CSV File Structure Definition
Output of the task: A well-defined structure for the CSV file, including column names and data types
Description: The AI agent will need to generate a prompt that defines the structure of the CSV file based on the user's web app description. This prompt will include the specific column names, their data types (string, numeric, etc.), and any additional formatting requirements. The output of this task will be used as a template for the AI agent to populate the CSV file with researched data.

Task: Data Research and Collection
Output of the task: Relevant data to populate the CSV file
Description: The AI agent will generate a prompt to guide its research and data collection process based on the web app description and the defined CSV file structure. This prompt will include instructions on what type of data to look for, where to find it (e.g., specific websites, databases, or APIs), and how to extract and format the data to fit the CSV file structure.

Task: Image URL Collection
Output of the task: Image URLs for each entry in the CSV file
Description: The AI agent will create a prompt to assist in finding and collecting relevant image URLs for each entry in the CSV file. This prompt will provide guidelines on how to search for images (e.g., using Google Images, nounproject, or taking screenshots), assess their relevance and quality, and format the image URLs to be compatible with the CSV file and the software tool.

Task: API Call Prompt
Output of the task: Instructions for making the API call to send the CSV file to the software tool
Description: The AI agent will generate a prompt that provides step-by-step instructions on how to make the API call to send the completed CSV file to your software tool. This prompt will include details such as the API endpoint, required headers, and any authentication or formatting requirements specific to your software tool's API.

Note: The prompts related to the software tool itself are not included in this list, as it is assumed that the tool has already been developed and is ready to accept the CSV file as input.