6. Write Software Spec & Mockups

I want the Agent to write a specification document and design mockups for cloud-based software that I want to build on a no-code platform like Bubble. I will then pass the mockup and spec to a developer to build. Before launching the Agent I will decide on the core functionality of the tool and research some possible APIs/libraries we can use (but roughly and in the abstract). Then, I will input this overview into the tool (so input will be the name of the tool and a rough description of what it will do, and potential libraries to use). Now the Agent will activate, completing the following steps: first, an LLM will expand upon my initial idea, brainstorming a list of ways we could do it (including possible APIs, libraries and SDKs). Second, we will research these APIs and libraries, deciding which are the best options. We should run several searches, to generally research the suggested APIs/libraries, and also to research whether they can be used on bubble. Next, the LLM will review the results - coupled with the initial brainstorm - and decide on the final technical sequence needed. Next, we need to create the design mockups. I will provide 8 template pages, and so an LLM with vision (eg GPT Vision) will need to review these and decide the 3-4 pages we should reuse. The LLM should select these, and provide a rough commentary on what edits should be made. Next, the LLM should download and edit these HTML pages. Now we have the mockup pages designed, the LLM should write the technical spec of how this could be done. I will provide examples/templates of previous specs for you to review. Since this is the most important step, we should divide it into sections (intro, technical process, discussion of what each design page does), and have LLMs edit, review and finalise each step. Finally, we will return the technical spec as HTML and the final edited design mockup pages in a zip.

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

final-output-comps..1

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

a) Final output:
The final output of this automated workflow would be:
1. A technical specification document in HTML format, detailing the functionality, APIs, libraries, and the technical process of how the cloud-based software can be built on a no-code platform like Bubble.
2. A zip file containing 3-4 edited HTML design mockup pages, showcasing the user interface and user experience of the proposed software.

b) Components breakdown:
To generate the final output, the following components would be required:

1. Initial input:
   - Name of the tool
   - Rough description of the tool's functionality
   - Potential APIs, libraries, and SDKs to be used

2. Brainstorming output:
   - A list of possible ways to build the software, including APIs, libraries, and SDKs
   - This output would be generated by the LLM based on the initial input

3. Research results:
   - Information gathered from several searches about the suggested APIs, libraries, and their compatibility with Bubble
   - This output would be generated by the AI agent through web searches

4. Final technical sequence:
   - A decision on the final technical approach to build the software, based on the brainstorming output and research results
   - This output would be generated by the LLM after reviewing the brainstorming and research components

5. Design mockup templates:
   - 8 pre-designed HTML template pages provided by you

6. Selected and edited mockup pages:
   - 3-4 HTML pages selected from the provided templates
   - These pages would be chosen and edited by an LLM with vision capabilities (e.g., GPT Vision)
   - The LLM would provide a commentary on the necessary edits for each selected page

7. Technical specification document:
   - A detailed document in HTML format, divided into sections:
     - Introduction
     - Technical process
     - Discussion of each design page's functionality
   - Previous spec examples/templates provided by you for reference
   - The LLM would write, edit, review, and finalize each section of the document

8. Final output:
   - The finalized technical specification document in HTML format
   - A zip file containing the edited design mockup pages

These components, when processed in the specified order by the AI agent, would lead to the desired final output of the technical specification document and the design mockups.

final-output-comps..2 GEM

## Understanding the Final Output of the Automated Workflow

### a) Final Output

The final output of this automated workflow would be a zipped folder containing two main components:

* **Technical Specification Document (HTML format):**  This document will outline the functionalities, technical requirements, and design specifications for the cloud-based software. It will serve as a blueprint for the developer to understand and build the software according to your vision.
* **Design Mockups (HTML pages):** This set of HTML pages will visually represent the user interface and user experience of the software. It will showcase the layout, design elements, and interactions within the application.

### b) Components of the Final Output

To generate the final output, several key components need to be created during the process:

**Technical Specification Document Components:**

* **Introduction:**  This section will provide an overview of the software, its purpose, target audience, and core functionalities.
* **Technical Process:** This section will detail the technical implementation, including:
    * **API and Library Selection:**  A clear description of the chosen APIs and libraries, their functions, and how they will be integrated into the software. 
    * **Data Flow and Logic:** Explanation of how data will be processed and manipulated within the application.
    * **Architecture and Infrastructure:**  Overview of the underlying technical architecture and infrastructure required to support the software. 
* **Design Page Descriptions:** Each design mockup will have a corresponding section explaining its purpose, functionality, and user interactions.

**Design Mockup Components:**

* **Template Selection and Modification:** Based on your provided templates, the AI will select and modify 3-4 HTML pages to align with the envisioned design.
* **Visual Design Elements:** The mockups will incorporate visual elements like layout, color schemes, typography, buttons, images, and interactive components.
* **User Interface and User Experience Design:** The mockups will demonstrate the user flow and interactions within the application.

**Additional Considerations:**

* **LLM Prompts and Instructions:** Specific instructions and prompts will guide the AI agent through each step of the process. These prompts will ensure the AI understands the desired outcome and generates the necessary outputs.
* **Research Data:** Information gathered from researching APIs, libraries, and technical solutions will inform the decision-making process for the technical specifications.

By piecing together these components, the AI agent will provide you with a comprehensive package containing the technical specifications and design mockups required to effectively communicate your vision to the developers and bring your cloud-based software to life.

final-output-comps GPT..3

a) The final output of this task would be a detailed and complete technical specification document in HTML format, as well as edited design mockups (HTML pages) of the desired software compressed in a zip file. The specification document will outline all the details including the technical process, and functionality of each design page. The mockups will be visual representatives of how the final product will look and function.

b) Given the task description provided, it appears the task can be broken down into the following core components/outputs:

1. A complete list of expanded ideas: This would be brainstormed by an LLM based on your initial input, essentially detailing various ways the tool could be built including all the potential APIs, libraries, and SDKs.

2. Research data on APIs and libraries: The LLM will have to perform several searches to thoroughly research the initially suggested APIs/libraries and determine if they can be used on the Bubble platform.

3. Finalized technical approach: The LLM, after reviewing the initial brainstorm and the research results, will decide the final technical methodology that will be put in place. This should include a determined list of the chosen APIs, libraries, and SDKs.

4. Selected templates for mockups: The LLM equipped with vision would review the 8 template pages you provide and select 3-4 that would best serve your intended tool, providing commentary on necessary edits.

5. Edited HTML pages: Having been guided on what changes to make, the LLM would then download and edit the relevant HTML pages to create the design mockups.

6. A technical specification document: The LLM will produce a structured technical document detailing the complete process based on the defined sections (intro, technical process, detailing of each design page). This will be done following a thorough review and editing process of similar previous examples/templates that you provide.

These components will come together to create the final output: A technical specification document and edited design mockups which can be handed over to a developer for execution.

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

a) Final output:
The final output of this automated workflow would be:
1. A technical specification document in HTML format, detailing the following:
   - Introduction: An overview of the software, its purpose, target audience, and core functionalities.
   - Technical process: A comprehensive description of the technical implementation, including the selected APIs, libraries, and SDKs, data flow and logic, and the underlying technical architecture and infrastructure.
   - Design page descriptions: A section for each design mockup, explaining its purpose, functionality, and user interactions.

2. A zip file containing 3-4 edited HTML design mockup pages, showcasing the user interface and user experience of the proposed software. These pages will include:
   - Visual design elements: Layout, color schemes, typography, buttons, images, and interactive components.
   - User interface and user experience design: Demonstration of the user flow and interactions within the application.

b) Components breakdown:
To generate the final output, the following components would be required:

1. Initial input:
   - Name of the tool
   - Rough description of the tool's functionality
   - Potential APIs, libraries, and SDKs to be used

2. Brainstorming output:
   - A list of possible ways to build the software, including APIs, libraries, and SDKs
   - Generated by the LLM based on the initial input

3. Research results:
   - Information gathered from several searches about the suggested APIs, libraries, and their compatibility with Bubble
   - Generated by the AI agent through web searches

4. Final technical sequence:
   - A decision on the final technical approach to build the software, based on the brainstorming output and research results
   - Generated by the LLM after reviewing the brainstorming and research components

5. Design mockup templates:
   - 8 pre-designed HTML template pages provided by you

6. Selected and edited mockup pages:
   - 3-4 HTML pages selected from the provided templates
   - Chosen and edited by an LLM with vision capabilities (e.g., GPT Vision)
   - Commentary provided by the LLM on the necessary edits for each selected page

7. Technical specification document:
   - A detailed document in HTML format, divided into sections:
     - Introduction
     - Technical process
     - Design page descriptions
   - Previous spec examples/templates provided by you for reference
   - Written, edited, reviewed, and finalized by the LLM for each section of the document

8. LLM prompts and instructions:
   - Specific instructions and prompts to guide the AI agent through each step of the process
   - Ensures the AI understands the desired outcome and generates the necessary outputs

These components, when processed in the specified order by the AI agent, would lead to the desired final output of the technical specification document and the design mockups, ready to be handed over to a developer for execution on a no-code platform like Bubble.

claude 3 - extract final output

extract component outputs

To generate the final output, the following components would be required:

1. Initial input:
   - Name of the tool
   - Rough description of the tool's functionality
   - Potential APIs, libraries, and SDKs to be used

2. Brainstorming output:
   - A list of possible ways to build the software, including APIs, libraries, and SDKs
   - Generated by the LLM based on the initial input

3. Research results:
   - Information gathered from several searches about the suggested APIs, libraries, and their compatibility with Bubble
   - Generated by the AI agent through web searches

5. Design mockup templates:
   - 8 pre-designed HTML template pages provided by you

Claude - research-ideas-and-input-variables

Thank you for providing the detailed task description. 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 features and functionalities should the cloud-based software have?
2. Is there a particular industry or target audience for this software?
3. Are there any specific design preferences or branding guidelines to consider for the mockups?
4. What is the desired level of detail and complexity for the technical specification document?
5. Are there any specific file formats or delivery methods preferred for the final outputs (e.g., HTML, PDF, zip file)?

b) Before launching the task completion process, the following inputs should be provided:

1. A clear and concise name for the cloud-based software.
2. A detailed description of the software's core functionality and purpose.
3. A list of potential APIs, libraries, and SDKs that could be used in the development process.
4. The 8 template pages that will be used as a starting point for the design mockups.
5. Examples or templates of previous technical specification documents for reference.

c) Based on the task description, I have identified a few areas that could benefit from segmentation:

1. Research and selection of APIs, libraries, and SDKs: This task could be divided into two parts - the initial brainstorming of potential options and the subsequent research to determine the best choices based on compatibility with the no-code platform (Bubble) and the project's requirements.

2. Design mockup creation: The process of selecting the 3-4 pages to reuse from the provided templates and editing them could be separated into distinct tasks. This would allow for more focused attention on each page and ensure that the necessary changes are made according to the LLM's commentary.

3. Technical specification document: Since this is the most critical component of the project, it would be beneficial to break it down into smaller, manageable sections. As mentioned in the task description, the document could be divided into an introduction, technical process, and discussion of each design page's functionality. Each section could then be edited, reviewed, and finalized separately by the LLMs to ensure clarity, accuracy, and completeness. This segmentation would also make it easier to identify and address any issues or inconsistencies in the document.

GPT - research-ideas-and-input-variables

a) To clarify what the final output of the task should be, here are some questions I would ask:

1. What are the key features and functionalities the cloud-based software should have? 
2. What is the specific purpose of the software? (E.g., CRM, project management, E-commerce store, etc.)
3. What is the targeted user profile for the software? (E.g., businesses, individuals, etc.)
4. What should be the level of complexity and sophistication in the design mockups? 
5. Are there any specific design elements you want included or excluded in the mockups? 
6. Are there any competitors or similar software systems that we can draw inspiration from for certain functionalities?
7. How many design mockups are required in total?
8. Is there a specific visual aesthetic or standard we need to adhere to or avoid while creating the mockups?
9. What is the expected length and structure of the technical specification document?
10. What specific elements must be included in the technical specification document besides the intro, technical process, and discussion sections?

b) Here are some of the inputs the task should accept before launching the task completion process:

1. Brief: A detailed description of the software, including its key features and functionalities.
2. API/Libraries: A list of potential APIs/libraries to be used.
3. Libraries Details: More details on what each library is for and how it could potentially be used. 
4. Template Pages: The eight template pages that the AI can use to create the design mockups.
5. Reference Documents: Examples/templates of previous specifications and design mockups for reference.
6. User Profile: Information about the intended users or target audience.
7. Supplementary Information: Any other information that could help in choosing libraries and designing the mockups, such as competitor analysis, industry trends, user behaviour data, legal and compliance requirements, etc.

template-ideas

Based on the description of the final output and the capabilities of the AI Agent, here are some template suggestions that could help refine the definition of a successful output for this task:

1. Technical specification documents:
   - Search for and collect examples of well-structured and comprehensive technical specification documents for similar cloud-based software projects. These documents can serve as a reference for the structure, content, and level of detail required in the final output.
   - Visit websites like GitHub, where open-source projects often include detailed technical documentation. Review these documents to understand best practices in technical writing and documentation.

2. Design mockups:
   - Browse through design inspiration platforms like Dribbble, Behance, or UI8 to find examples of high-quality user interface designs for web applications similar to the proposed software. These designs can provide ideas for layout, color schemes, and UI components.
   - Look for design system documentation from popular companies like Google's Material Design, Apple's Human Interface Guidelines, or IBM's Carbon Design System. These resources offer guidelines and best practices for creating consistent and user-friendly interfaces.

3. No-code platform examples:
   - Explore the Bubble app showcase or app templates to find examples of successful applications built using the Bubble platform. Analyze their design and functionality to understand what can be achieved within the constraints of the platform.
   - Search for blog posts, case studies, or tutorials that showcase the development process and final output of applications built using Bubble or similar no-code platforms. These resources can provide insights into the technical capabilities and limitations of such platforms.

4. API and library documentation:
   - Once the AI Agent has identified potential APIs, libraries, and SDKs to use, it should collect and review their official documentation. This documentation will provide details on how to integrate and use these tools effectively.
   - Look for code samples, tutorials, and demo projects that utilize the selected APIs and libraries. These resources can offer practical examples of how to implement specific functionalities and integrate different tools.

By collecting and analyzing these templates, the AI Agent can gain a clearer understanding of what constitutes a successful output for the technical specification document and design mockups. The Agent can use these references to guide its decision-making process, ensure that the final output meets industry standards, and aligns with the capabilities of the chosen no-code platform and selected APIs/libraries.

claude 3 - searches

Here are 5 specific keyphrases for you to search, to help refine the definition of a successful output for the task:

1. "bubble app technical specification document example"
   This search should help find examples of technical specification documents specifically for applications built using the Bubble platform. Reviewing these documents will provide insights into the structure and content expected for the final output.

2. "saas application ui design mockup dribbble"
   Searching for SaaS (Software as a Service) application UI design mockups on Dribbble will showcase high-quality, professional design examples that can serve as inspiration for the design mockups in the final output.

3. "material design documentation"
   Material Design is a popular design system created by Google. Searching for its documentation will provide guidelines and best practices for creating consistent and user-friendly interfaces, which can be applied to the design mockups.

4. "bubble app showcase"
   Exploring the Bubble app showcase will provide examples of successful applications built using the Bubble platform. Analyzing their design and functionality will help understand what can be achieved within the constraints of the platform.

5. "integrating [specific API/library] in bubble tutorial"
   Once the AI Agent has identified a specific API, library, or SDK to use, searching for tutorials on integrating it with Bubble will provide practical examples and guidance on how to implement the desired functionalities within the no-code platform.

By searching for these specific keyphrases and reviewing the top results, you should be able to find relevant templates and examples that will help refine the definition of a successful output for the technical specification document and design mockups.

Bubble App Technical Specification Document Example
SaaS Application UI Design Mockup on Dribbble
Material Design Documentation
Bubble App Showcase
Integrating [specific API/library] in Bubble Tutorial

subagent-summary

A) FULL TASK SUMMARY: Create a technical specification document and design mockups for a cloud-based software using AI-driven automated workflow.

B) FINAL TASK OUTPUT:
- A technical specification document in HTML format (approximately 10-15 pages, ~5000 words)
- A zip file containing 3-4 edited HTML design mockup pages (each page ~2MB in size)

C) COMPONENTS OVERVIEW:
1. Initial input
2. Brainstorming output
3. Research results
4. Final technical sequence
5. Design mockup templates
6. Selected and edited mockup pages
7. Technical specification document
8. LLM prompts and instructions

D) OVERVIEW OF SUBAGENTS:
- InputAgent: Collects and processes the initial input from the user
- BrainstormingAgent: Generates a list of possible ways to build the software based on the initial input
- ResearchAgent: Conducts web searches to gather information about the suggested APIs, libraries, and their compatibility with Bubble
- TechnicalSequenceAgent: Decides on the final technical approach based on the brainstorming output and research results
- MockupSelectionAgent: Selects and edits 3-4 HTML pages from the provided templates using computer vision capabilities
- SpecificationAgent: Writes, edits, reviews, and finalizes the technical specification document in HTML format
- CoordinatorAgent: Manages the workflow, ensures proper communication between subagents, and compiles the final output

E) SUBAGENT 1: InputAgent
- Final output: A structured representation of the initial input (name of the tool, description, potential APIs/libraries/SDKs)
- Accomplishment: The InputAgent will use natural language processing (NLP) to parse the user's input, extract relevant information, and store it in a structured format (e.g., JSON). It will validate the input and prompt the user for clarification if needed.
- Sequence: Receive user input > Parse input using NLP > Extract relevant information > Validate input > Store structured input > Pass to BrainstormingAgent

F) SUBAGENT 2: BrainstormingAgent
- Final output: A list of possible ways to build the software, including APIs, libraries, and SDKs
- Accomplishment: The BrainstormingAgent will use the structured input from the InputAgent and generate a list of potential approaches to build the software. It will leverage its knowledge of software development best practices, design patterns, and available technologies to create a comprehensive list of options.
- Sequence: Receive structured input > Generate list of potential approaches > Rank approaches based on feasibility and relevance > Pass ranked list to ResearchAgent

G) SUBAGENT 3: ResearchAgent
- Final output: Information gathered from web searches about the suggested APIs, libraries, and their compatibility with Bubble
- Accomplishment: The ResearchAgent will use the ranked list of approaches from the BrainstormingAgent and conduct web searches to gather more information about each suggested API, library, and SDK. It will also investigate their compatibility with the Bubble platform. The agent will use web scraping, NLP, and information retrieval techniques to extract relevant data from the search results.
- Sequence: Receive ranked list of approaches > Conduct web searches for each approach > Scrape relevant information from search results > Analyze compatibility with Bubble > Compile research findings > Pass findings to TechnicalSequenceAgent

H) SUBAGENT 4: TechnicalSequenceAgent
- Final output: A decision on the final technical approach to build the software
- Accomplishment: The TechnicalSequenceAgent will review the brainstorming output and research findings to determine the most suitable technical approach for building the software. It will consider factors such as feasibility, scalability, maintainability, and alignment with the project goals. The agent will use decision-making algorithms and heuristics to arrive at the final technical sequence.
- Sequence: Receive brainstorming output and research findings > Evaluate each approach based on predefined criteria > Apply decision-making algorithms > Select the most suitable technical approach > Pass decision to MockupSelectionAgent and SpecificationAgent

I) SUBAGENT 5: MockupSelectionAgent
- Final output: 3-4 HTML pages selected and edited from the provided templates, along with commentary on the necessary edits for each page
- Accomplishment: The MockupSelectionAgent will use computer vision and image processing techniques to analyze the provided HTML template pages. It will select the most relevant 3-4 pages based on the project requirements and the final technical approach. The agent will then edit the selected pages, making necessary changes to the layout, color schemes, typography, and interactive components. It will also generate a commentary on the edits made to each page.
- Sequence: Receive design mockup templates > Analyze templates using computer vision > Select 3-4 most relevant pages > Edit selected pages > Generate commentary on edits > Pass edited pages and commentary to CoordinatorAgent

J) SUBAGENT 6: SpecificationAgent
- Final output: A detailed technical specification document in HTML format, divided into sections (Introduction, Technical process, Design page descriptions)
- Accomplishment: The SpecificationAgent will use the final technical approach, edited mockup pages, and commentary from the MockupSelectionAgent to write a comprehensive technical specification document. It will follow the provided examples and templates to ensure consistency and completeness. The agent will use NLP and language models to generate the content for each section, and it will iteratively review, edit, and refine the document.
- Sequence: Receive final technical approach, edited mockup pages, and commentary > Review provided examples and templates > Generate content for Introduction section > Generate content for Technical process section > Generate content for Design page descriptions section > Review, edit, and refine each section > Compile the final specification document > Pass the document to CoordinatorAgent

K) SUBAGENT 7: CoordinatorAgent
- Final output: The final technical specification document in HTML format and the zip file containing the edited design mockup pages
- Accomplishment: The CoordinatorAgent will manage the entire workflow, ensuring proper communication and data exchange between the subagents. It will monitor the progress of each subagent, handle errors and exceptions, and ensure that the final output meets the desired specifications. The agent will also be responsible for compiling the final technical specification document and the edited design mockup pages into a single package for delivery.
- Sequence: Initiate the workflow > Pass initial input to InputAgent > Monitor progress of each subagent > Handle communication and data exchange between subagents > Compile the final specification document received from SpecificationAgent > Compile the edited mockup pages received from MockupSelectionAgent > Package the specification document and mockup pages into a single deliverable > Return the final output to the user

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

Absolutely, I will update the list of subagents based on the available tasks and the required components for this specific project. Here's the revised list:

A) FULL TASK SUMMARY: Create a technical specification document and design mockups for a cloud-based software using AI-driven automated workflow.

B) FINAL TASK OUTPUT:
- A technical specification document in HTML format (approximately 10-15 pages, ~5000 words)
- A zip file containing 3-4 edited HTML design mockup pages (each page ~2MB in size)

C) COMPONENTS OVERVIEW:
1. Brainstorming output
2. Research results
3. Final technical sequence
4. Selected and edited mockup pages
5. Technical specification document
6. LLM prompts and instructions

D) OVERVIEW OF SUBAGENTS:
- BrainstormingAgent: Generates a list of possible ways to build the software based on the initial input
- ResearchAgent: Conducts web searches to gather information about the suggested APIs, libraries, and their compatibility with Bubble
- TechnicalSequenceAgent: Decides on the final technical approach based on the brainstorming output and research results
- MockupSelectionAgent: Selects and edits 3-4 HTML pages from the provided templates using computer vision capabilities
- SpecificationAgent: Writes, edits, reviews, and finalizes the technical specification document in HTML format
- CoordinatorAgent: Manages the workflow, ensures proper communication between subagents, and compiles the final output

E) SUBAGENT 1: BrainstormingAgent
- Final output: A list of possible ways to build the software, including APIs, libraries, and SDKs
- Tasks used:
  - GPT4 (Task 1): Generate a list of potential approaches to build the software based on the initial input
  - Save content to server (Task 9): Save the brainstorming output as a text file on the server
- Sequence: Receive initial input > Generate list of potential approaches using GPT4 > Save the brainstorming output to the server > Pass the saved file URL to ResearchAgent

F) SUBAGENT 2: ResearchAgent
- Final output: Information gathered from web searches about the suggested APIs, libraries, and their compatibility with Bubble
- Tasks used:
  - Access text files already saved (Task 10): Retrieve the brainstorming output from the server
  - Search Google (Task 15): Conduct web searches for each approach suggested in the brainstorming output
  - Scrape text content of website (Task 16): Extract relevant information from the search results
  - GPT4 (Task 1): Analyze the scraped content for compatibility with Bubble and compile research findings
  - Save content to server (Task 9): Save the research findings as a text file on the server
- Sequence: Retrieve brainstorming output from the server > Conduct web searches for each approach > Scrape relevant information from search results > Analyze compatibility with Bubble using GPT4 > Compile research findings > Save findings to the server > Pass the saved file URL to TechnicalSequenceAgent

G) SUBAGENT 3: TechnicalSequenceAgent
- Final output: A decision on the final technical approach to build the software
- Tasks used:
  - Access text files already saved (Task 10): Retrieve the brainstorming output and research findings from the server
  - GPT4 (Task 1): Evaluate each approach based on predefined criteria and apply decision-making algorithms to select the most suitable technical approach
  - Save content to server (Task 9): Save the final technical sequence as a text file on the server
- Sequence: Retrieve brainstorming output and research findings from the server > Evaluate each approach using GPT4 > Apply decision-making algorithms > Select the most suitable technical approach > Save the final technical sequence to the server > Pass the saved file URL to MockupSelectionAgent and SpecificationAgent

H) SUBAGENT 4: MockupSelectionAgent
- Final output: 3-4 HTML pages selected and edited from the provided templates, along with commentary on the necessary edits for each page
- Tasks used:
  - Save external web file/folder to server (Task 18): Save the provided design mockup templates to the server
  - Extract visual data from image (Task 5): Analyze the templates using GPT Vision
  - GPT4 (Task 1): Select the most relevant 3-4 pages based on the project requirements and the final technical approach
  - REQUIRED: HTML Editor API/Library - Edit the selected HTML pages according to the project requirements
  - Save content to server (Task 9): Save the edited mockup pages and commentary to the server
- Sequence: Save design mockup templates to the server > Analyze templates using GPT Vision > Select 3-4 most relevant pages using GPT4 > Edit selected pages using HTML Editor API/Library > Generate commentary on edits using GPT4 > Save edited pages and commentary to the server > Pass the saved file URLs to CoordinatorAgent

I) SUBAGENT 5: SpecificationAgent
- Final output: A detailed technical specification document in HTML format, divided into sections (Introduction, Technical process, Design page descriptions)
- Tasks used:
  - Access text files already saved (Task 10): Retrieve the final technical approach and edited mockup pages from the server
  - GPT4 (Task 1): Generate content for each section of the specification document based on the final technical approach and edited mockup pages
  - Save content to server (Task 9): Save the generated content for each section as separate HTML files on the server
  - Access text files already saved (Task 10): Retrieve the generated section files from the server
  - GPT4 (Task 1): Review, edit, and refine each section of the specification document
  - Concatenate text/html files (Task 13): Combine the refined sections into a single HTML file
  - Save content to server (Task 9): Save the final specification document as an HTML file on the server
- Sequence: Retrieve final technical approach and edited mockup pages from the server > Generate content for each section using GPT4 > Save generated content for each section to the server > Retrieve generated section files from the server > Review, edit, and refine each section using GPT4 > Concatenate the refined sections into a single HTML file > Save the final specification document to the server > Pass the saved file URL to CoordinatorAgent

J) SUBAGENT 6: CoordinatorAgent
- Final output: The final technical specification document in HTML format and the zip file containing the edited design mockup pages
- Tasks used:
  - Access text files already saved (Task 10): Retrieve the final specification document and edited mockup pages from the server
  - Zip and unzip files (Task 14): Compress the edited mockup pages into a zip file
  - Save content to server (Task 9): Save the zip file to the server
  - Move files around server (Task 8): Move the final specification document and the zip file to a public directory on the server
- Sequence: Retrieve final specification document and edited mockup pages from the server > Compress edited mockup pages into a zip file > Save the zip file to the server > Move the final specification document and the zip file to a public directory on the server > Return the public URLs of the specification document and the zip file as the final output

K) REQUIRED API/LIBRARY:
- HTML Editor API/Library: An API or library that allows editing HTML files programmatically, which is necessary for the MockupSelectionAgent to edit the selected HTML mockup pages according to the project requirements.

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

Based on the updated list of available tasks, I have revised the subagents and their respective tasks to complete the given objective:

A) FULL TASK SUMMARY: Create a technical specification document and design mockups for a cloud-based software using AI-driven automated workflow.

B) FINAL TASK OUTPUT:
- A technical specification document in HTML format (approximately 10-15 pages, ~5000 words)
- A zip file containing 3-4 edited HTML design mockup pages (each page ~2MB in size)

C) COMPONENTS OVERVIEW:
1. Brainstorming output
2. Research results
3. Final technical sequence
4. Selected and edited mockup pages
5. Technical specification document

E) SUBAGENT 1: BrainstormingAgent
- Final output: A list of possible ways to build the software, including APIs, libraries, and SDKs
- Tasks used:
  - GPT4 (Task 1): Generate a list of potential approaches to build the software based on the initial input
  - GPT4 (Task 1): Rank the approaches based on feasibility and relevance
- Sequence: Receive initial input > Generate list of potential approaches using GPT4 > Rank approaches using GPT4 > Pass ranked list to ResearchAgent

F) SUBAGENT 2: ResearchAgent
- Final output: Information gathered from web searches about the suggested APIs, libraries, and their compatibility with Bubble
- Tasks used:
  - Search Google (Task 15): Conduct web searches for each approach
  - Scrape text content of website (Task 16): Extract relevant information from search results
  - GPT4 (Task 1): Analyze compatibility with Bubble and compile research findings
- Sequence: Receive ranked list of approaches > Conduct web searches for each approach using Task 15 > Scrape relevant information from search results using Task 16 > Analyze compatibility with Bubble and compile findings using GPT4 > Pass findings to TechnicalSequenceAgent

G) SUBAGENT 3: TechnicalSequenceAgent
- Final output: A decision on the final technical approach to build the software
- Tasks used:
  - GPT4 (Task 1): Evaluate each approach based on predefined criteria and apply decision-making algorithms to select the most suitable technical approach
- Sequence: Receive brainstorming output and research findings > Evaluate each approach using GPT4 > Select the most suitable technical approach using GPT4 > Pass decision to MockupSelectionAgent and SpecificationAgent

H) SUBAGENT 4: MockupSelectionAgent
- Final output: 3-4 HTML pages selected and edited from the provided templates, along with commentary on the necessary edits for each page
- Tasks used:
  - Extract visual data from image (Task 5): Analyze the provided HTML template pages using computer vision
  - GPT4 (Task 1): Select the most relevant 3-4 pages based on the project requirements and the final technical approach
  - REQUIRED: HTML Editor API/Library - Edit the selected pages, making necessary changes to the layout, color schemes, typography, and interactive components
  - GPT4 (Task 1): Generate commentary on the edits made to each page
  - Save content to server (Task 9): Save the edited HTML pages to the server
- Sequence: Receive design mockup templates > Analyze templates using Task 5 > Select 3-4 most relevant pages using GPT4 > Edit selected pages using HTML Editor API/Library > Generate commentary on edits using GPT4 > Save edited pages to server using Task 9 > Pass edited pages and commentary to CoordinatorAgent

I) SUBAGENT 5: SpecificationAgent
- Final output: A detailed technical specification document in HTML format, divided into sections (Introduction, Technical process, Design page descriptions)
- Tasks used:
  - GPT4 (Task 1): Generate content for each section of the specification document (Introduction, Technical process, Design page descriptions)
  - GPT4 (Task 1): Review, edit, and refine each section of the document
  - GPT4 (Task 1): Compile the final specification document
  - Save content to server (Task 9): Save the specification document in HTML format to the server
- Sequence: Receive final technical approach, edited mockup pages, and commentary > Generate content for each section using GPT4 > Review, edit, and refine each section using GPT4 > Compile the final specification document using GPT4 > Save the document to server using Task 9 > Pass the document to CoordinatorAgent

J) SUBAGENT 6: CoordinatorAgent
- Final output: The final technical specification document in HTML format and the zip file containing the edited design mockup pages
- Tasks used:
  - GPT4 (Task 1): Monitor progress of each subagent and handle communication and data exchange between subagents
  - Access text files already saved (Task 11): Retrieve the specification document and edited mockup pages from the server
  - Zip and unzip files (Task 14): Compile the edited mockup pages into a zip file
- Sequence: Initiate the workflow > Monitor progress of each subagent using GPT4 > Handle communication and data exchange between subagents using GPT4 > Retrieve the final specification document from the server using Task 11 > Retrieve the edited mockup pages from the server using Task 11 > Compile the mockup pages into a zip file using Task 14 > Return the final output to the user

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

Based on the tasks available to your AI agent, here's an updated breakdown of the subagents and specific tasks they might perform to complete each component toward achieving the final objective:

D) COMPONENTS OVERVIEW AND SUBAGENT TASKS
---------------------------------------------------

1) Brainstorming output (Component: Brainstorming output)
- Subagent: BrainstormingAgent
  - Task: GPT4 (#1)
    - INPUT: Plain text overview of initial idea, including the name of the tool, functionality, and potential APIs/libraries.
    - OUTPUT: A list (plain text) of possible implementation options including APIs, libraries, and SDKs.

2) Research results (Component: Research results)
- Subagent: ResearchAgent
  - Task: Search Google (#15)
    - INPUT: Keywords from brainstorming output related to APIs/libraries and their compatibility with Bubble.
    - OUTPUT: Text summary of the first 20x URLs for each keyword from Google SERP.
  - Task: Scrape text content of website (#16)
    - INPUT: URLs from Google search on relevant APIs/libraries.
    - OUTPUT: Text content with relevant information about the APIs/libraries.

3) Final technical sequence (Component: Final technical sequence)
- Subagent: TechnicalDecisionAgent
  - Task: GPT4 (#1)
    - INPUT: Text summaries of research findings and brainstorming list.
    - OUTPUT: Text (plain text) decision on the final technical approach incorporating selected APIs/libraries.

4) Mockup selection and editing (Component: Selected and edited mockup pages)
- Subagent: DesignAgent
  - Task: Extract visual data from image (#5)
    - INPUT: URLs of provided template images.
    - OUTPUT: Text (analysis of each template).

5) Create design mockup edits (Component: Design mockup edit instructions)
- Subagent: EditMockupAgent
  - Task: GPT4 (#1)
    - INPUT: Text analysis of template designs and specifications for edits.
    - OUTPUT: Instructions for edit requirements.

6) Implement design mockup edits (Component: Finalized design mockups)
- Subagent: ImplementationAgent
  - Task: Bannerbear - image creator (#26)
    - INPUT: Final design mockup edit instructions as JSON.
    - OUTPUT: Edited images according to design specifications.

7) Write technical specification (Component: Technical specification document)
- Subagent: SpecificationWritingAgent
  - Task: GPT4 (#1)
    - INPUT: Decisions from TechnicalDecisionAgent, edited mockups, and commentary from EditMockupAgent.
    - OUTPUT: Technical specification document in plain text format.

8) Compile final documents (Component: Compilation of final documents & packaging)
- Subagent: FinalizationAgent
  - Task: Save content to server (#9)
    - INPUT: Technical specification document text to be converted into an HTML file.
    - OUTPUT: HTML file URL of the technical spec.

- Task: Zip and unzip files (#14)
    - INPUT: List of up-to-date file URLs (HTML spec and image mockups) to be zipped.
    - OUTPUT: URL of the zipped file containing the spec and mockup pages.

REQUIRED COMPONENTS
---------------------------------------------------

- There might be a need for a task that directly converts plain text to HTML format (and vice versa), as the FormatterAgent is needed to ensure the technical specification document aligns with HTML standards. This could involve prompt generator (#2) and code interpreter (#3) tasks to create custom HTML templates.

- The process of editing mockup pages could require HTML editing capabilities that go beyond static image editing, to implement interactive elements or to edit the actual HTML/CSS/JavaScript of the pages. If the capability doesn't exist within the provided task list, a task for directly editing and saving HTML/CSS/JavaScript files on the server (webpage editing task) is REQUIRED.

- Task coordination might need to be more actively managed than is implied by the current list. A ProjectCoordinatorAgent might need to oversee the entire operation to ensure timing and integration between tasks are correctly handled. Existing tasks don't cover this dynamic coordination, so this could require a custom task to be developed: WorkflowCoordinator (REQUIRED).

By defining these tasks using existing capabilities and highlighting possible required additions, we've created a granular and actionable subagent model to realize the final objective effectively.

subagent-summary-amended-for-actual-tasks

A) FULL TASK SUMMARY: Create a technical specification document and design mockups for a cloud-based software using AI-driven automated workflow.

B) FINAL TASK OUTPUT:
- A technical specification document in HTML format (approximately 10-15 pages, ~5000 words)
- A zip file containing 3-4 edited HTML design mockup pages (each page ~2MB in size)

C) COMPONENTS OVERVIEW:
1. Brainstorming output
2. Research results
3. Final technical sequence
4. Selected and edited mockup pages
5. Technical specification document

L) ADDITIONAL CONSIDERATIONS:
- Task coordination might need to be more actively managed than is implied by the current list. A ProjectCoordinatorAgent might need to oversee the entire operation to ensure timing and integration between tasks are correctly handled. Existing tasks don't cover this dynamic coordination, so this could require a custom task to be developed: WorkflowCoordinator (REQUIRED).
- There might be a need for a task that directly converts plain text to HTML format (and vice versa), as the FormatterAgent is needed to ensure the technical specification document aligns with HTML standards. This could involve prompt generator (#2) and code interpreter (#3) tasks to create custom HTML templates.

random-autocomplete

To achieve the end goal of creating a technical specification document and design mockups for a cloud-based software, the AI Agent could follow this overall sequence:

1. Brainstorming and Ideation:
   - Analyze the user's initial input (name of the tool and rough description) and brainstorm a list of potential ways to implement the software, including possible APIs, libraries, and SDKs.

2. Research and Selection:
   - Conduct research on the suggested APIs, libraries, and SDKs to determine their suitability for the project and compatibility with the Bubble platform.
   - Make a final selection of the most appropriate technical components based on the research findings and initial brainstorming.

3. Design Mockup Creation:
   - Review the provided 8 template pages and select the 3-4 most relevant pages for the software.
   - Provide commentary on the necessary edits to adapt the selected pages for the specific software requirements.
   - Download and edit the chosen HTML pages to create the design mockups, incorporating visual design elements and demonstrating user flow and interactions.

4. Technical Specification Document:
   - Write the technical specification document in HTML format, divided into sections:
     - Introduction: Provide an overview of the software, its purpose, target audience, and core functionalities.
     - Technical Process: Describe the technical implementation in detail, including the selected APIs, libraries, SDKs, data flow, logic, and the underlying technical architecture and infrastructure.
     - Design Page Descriptions: Explain the purpose, functionality, and user interactions for each design mockup page.
   - Utilize LLMs to edit, review, and finalize each section of the technical specification document, ensuring clarity, coherence, and completeness.

5. Output Preparation:
   - Compile the final technical specification document in HTML format.
   - Package the edited design mockup pages into a zip file.

6. Delivery:
   - Return the HTML technical specification document and the zip file containing the design mockup pages as the final output of the automated workflow.

By following this sequence of tasks, the AI Agent can effectively brainstorm, research, design, and document the cloud-based software based on the user's initial input. The Agent's access to a wide range of APIs, libraries, and a Windows 11 Virtual Machine will enable it to accomplish these tasks efficiently and deliver the desired output.

claude 3 - mermaid-for-all

SubAgent #1

claude 3 - subagent1

subagent1-refined

A) SUBAGENT SUMMARY: The BrainstormingAgent generates a list of potential approaches to build the software based on the initial input, using GPT-4 and saves the output to the server.

B) FINAL TASK OUTPUT: A text file (brainstorming_output.txt) containing a list of possible ways to build the software, including APIs, libraries, and SDKs. The file size will be approximately 2,000-4,000 characters.

C) SUBAGENT INPUT:
- Name of the tool (1-50 characters)
- Rough description of the tool's functionality (50-500 characters)
- Potential APIs, libraries, and SDKs to be used (50-500 characters)

D) SUBAGENT TASK SUMMARY:
1. Receive initial input (tool name, description, and potential APIs/libraries/SDKs)
2. Concatenate the input data into a single string, separated by newline characters
3. Use GPT-4 (Task 1) to generate a list of potential approaches to build the software based on the concatenated input string
   - Prompt GPT-4 to brainstorm 5-10 different ways to build the software, considering the provided APIs, libraries, and SDKs
   - Encourage GPT-4 to think creatively and suggest alternative or complementary technologies if appropriate
   - The output should be a numbered list of approaches, each described in 50-200 words
4. Save the GPT-4 output to the server as a text file named "brainstorming_output.txt" using the "Save content to server" task (Task 9)
5. Pass the saved file URL to the ResearchAgent

E) SILOS:
This subagent does not require silos as it is a straightforward process without parallel tasks.

F) DETAILED TASK BREAKDOWN:
1. Receive initial input
   - Tool name (1-50 characters)
   - Tool description (50-500 characters)
   - Potential APIs, libraries, and SDKs (50-500 characters)

2. Concatenate input data
   - Concatenate the tool name, description, and potential APIs/libraries/SDKs into a single string
   - Separate each input item with a newline character ("\n")
   - Example output: "Tool Name\nTool Description\nPotential APIs, libraries, and SDKs"

3. Generate brainstorming output using GPT-4 (Task 1)
   - Input: Concatenated input string from step 2
   - Prompt:
     "Based on the following input:
     [Concatenated input string]
     
     Please brainstorm 5-10 different approaches to build this software, considering the provided APIs, libraries, and SDKs. Think creatively and suggest alternative or complementary technologies if appropriate. Present your output as a numbered list, with each approach described in 50-200 words."
   - Output: A numbered list of potential approaches to build the software

4. Save brainstorming output to server (Task 9)
   - Input: GPT-4 output from step 3
   - File extension: .txt
   - File name: "brainstorming_output.txt"
   - Output: Internal URL of the saved file (e.g., server/_public/timestamp/storage/brainstorming_output.txt)

5. Pass saved file URL to ResearchAgent
   - Pass the internal URL of the saved "brainstorming_output.txt" file to the ResearchAgent for further processing

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive initial input (1 task)
   - Tool name (1-50 characters)
   - Tool description (50-500 characters)
   - Potential APIs, libraries, and SDKs (50-500 characters)

2. Concatenate input data (1 task)
   - Concatenate the tool name, description, and potential APIs/libraries/SDKs into a single string
   - Separate each input item with a newline character ("\n")
   - Example output: "Tool Name\nTool Description\nPotential APIs, libraries, and SDKs"

3. Generate brainstorming output using GPT-4 (Task 1) (1 task)
   - Input: Concatenated input string from step 2
   - Prompt:
     "Based on the following input:
     [Concatenated input string]
     
     Please brainstorm 5-10 different approaches to build this software, considering the provided APIs, libraries, and SDKs. Think creatively and suggest alternative or complementary technologies if appropriate. Present your output as a numbered list, with each approach described in 50-200 words."
   - Output: A numbered list of potential approaches to build the software

4. Save brainstorming output to server (Task 9) (1 task)
   - Input: GPT-4 output from step 3
   - File extension: .txt
   - File name: "brainstorming_output.txt"
   - Output: Internal URL of the saved file (e.g., server/_public/timestamp/storage/brainstorming_output.txt)

5. Pass saved file URL to ResearchAgent (1 task)
   - Pass the internal URL of the saved "brainstorming_output.txt" file to the ResearchAgent for further processing

Total tasks: 5

In this specific subagent, there are no explicitly mentioned repetitions of tasks. The process involves a linear sequence of tasks, each performed once, resulting in a total of 5 tasks.

Subagent1 task-ID-numbers

subagent1-output-variables

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

--------------
G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive initial input (1 task)
   - Tool name (1-50 characters) [tool-name]
   - Tool description (50-500 characters) [tool-description]
   - Potential APIs, libraries, and SDKs (50-500 characters) [potential-tech]

2. Concatenate input data (1 task)
   - Concatenate the tool name, description, and potential APIs/libraries/SDKs into a single string [concatenated-input]
   - Separate each input item with a newline character ("\n")
   - Example output: "Tool Name\nTool Description\nPotential APIs, libraries, and SDKs"

3. Generate brainstorming output using GPT-4 (Task 1) (1 task)
   - Input: Concatenated input string from step 2 [concatenated-input]
   - Prompt:
     "Based on the following input:
     [concatenated-input]
     
     Please brainstorm 5-10 different approaches to build this software, considering the provided APIs, libraries, and SDKs. Think creatively and suggest alternative or complementary technologies if appropriate. Present your output as a numbered list, with each approach described in 50-200 words."
   - Output: A numbered list of potential approaches to build the software [brainstorming-output]

4. Save brainstorming output to server (Task 9) (1 task)
   - Input: GPT-4 output from step 3 [brainstorming-output]
   - File extension: .txt
   - File name: "brainstorming_output.txt"
   - Output: Internal URL of the saved file (e.g., server/_public/timestamp/storage/brainstorming_output.txt) [brainstorming-output-url]

5. Pass saved file URL to ResearchAgent (1 task)
   - Pass the internal URL of the saved "brainstorming_output.txt" file to the ResearchAgent for further processing [brainstorming-output-url]

Total tasks: 5

H) OUTPUT TOKENS:
- [tool-name]
- [tool-description]
- [potential-tech]
- [concatenated-input]
- [brainstorming-output]
- [brainstorming-output-url]
--------------

In this specific subagent, there are no explicitly mentioned repetitions of tasks. The process involves a linear sequence of tasks, each performed once, resulting in a total of 5 tasks and 6 unique output tokens.

subagent1refined-repeating-json-core

A) SUBAGENT SUMMARY: Brainstorm and research potential approaches to build the specified software using the provided input data.

B) FINAL TASK OUTPUT: An internal URL pointing to a saved .txt file containing the brainstorming output generated by GPT-4, which will be passed to the ResearchAgent for further processing.

C) SUBAGENT INPUT:
   - Tool name (1-50 characters)
   - Tool description (50-500 characters)
   - Potential APIs, libraries, and SDKs (50-500 characters)

E) SUBAGENT TASK SUMMARY:
1. Receive initial input data (tool name, description, and potential APIs/libraries/SDKs)
2. Concatenate input data into a single string, separating each item with a newline character
3. Generate brainstorming output using GPT-4 by prompting it with the concatenated input string
4. Save the brainstorming output to a .txt file on the server
5. Pass the internal URL of the saved .txt file to the ResearchAgent for further processing

F) SILOS: N/A

G) TASKFLOW SUMMARY:

Task 1: Receive initial input
Description: Receive the tool name (1-50 characters), tool description (50-500 characters), and potential APIs, libraries, and SDKs (50-500 characters) as input.
Input(s) involved: tool_name, tool_description, potential_technologies
Output token name: initial_input_data

Task 2: Concatenate input data
Description: Concatenate the tool name, description, and potential APIs/libraries/SDKs into a single string, separating each input item with a newline character.
Input(s) involved: initial_input_data
Output token name: concatenated_input_string

Task 3: Generate brainstorming output using GPT-4
Description: Prompt GPT-4 with the concatenated input string to generate a numbered list of 5-10 potential approaches to build the software, considering the provided technologies and alternatives.
Input(s) involved: concatenated_input_string
Output token name: brainstorming_output

Task 4: Save brainstorming output to server
Description: Save the GPT-4 generated brainstorming output to a .txt file named "brainstorming_output.txt" on the server and obtain the internal URL of the saved file.
Input(s) involved: brainstorming_output
Output token name: brainstorming_output_url

Task 5: Pass saved file URL to ResearchAgent
Description: Pass the internal URL of the saved "brainstorming_output.txt" file to the ResearchAgent for further processing.
Input(s) involved: brainstorming_output_url
Output token name: research_agent_input

subagent1mermaid

SubAgent #2

claude 3 - subagent2

subagent2-refined

A) SUBAGENT SUMMARY: ResearchAgent gathers information about suggested APIs, libraries, and their compatibility with Bubble through web searches and analysis.

B) FINAL TASK OUTPUT:
- A text file (research_findings.txt) containing a summary of the research findings, approximately 2000-3000 words in length, saved on the server.
- The file URL is passed to the TechnicalSequenceAgent for further processing.

C) SUBAGENT INPUT:
- The brainstorming output file URL, which contains a list of possible ways to build the software, including APIs, libraries, and SDKs.

E) SUBAGENT TASK SUMMARY:
1. Access text files already saved (Task 10): Retrieve the brainstorming output from the server.
2. GPT4 (Task 1): Analyze the brainstorming output and extract individual APIs, libraries, and SDKs to research. Save the extracted list as a text file (research_topics.txt) on the server.
3. Search Google (Task 15): For each API, library, and SDK in the research_topics.txt file, conduct a web search to gather information about its functionality and compatibility with Bubble. Save the search results as separate text files (search_results_1.txt, search_results_2.txt, etc.) on the server.
4. Scrape text content of website (Task 16): For each search result file, extract the relevant text content from the top 3 websites. Save the scraped content as separate text files (scraped_content_1.txt, scraped_content_2.txt, etc.) on the server.
5. GPT4 (Task 1): Analyze the scraped content for each API, library, and SDK to determine its compatibility with Bubble and summarize the findings. Save the compatibility analysis as separate text files (compatibility_analysis_1.txt, compatibility_analysis_2.txt, etc.) on the server.
6. GPT4 (Task 1): Compile the individual compatibility analysis files into a single research findings summary (research_findings.txt). Save the research_findings.txt file on the server.
7. Pass the research_findings.txt file URL to the TechnicalSequenceAgent for further processing.

F) SILOS:
Silo 1: Research Topic Extraction
- Input: brainstorming_output.txt file URL
- Tasks: Access text files already saved (Task 10), GPT4 (Task 1)
- Output: research_topics.txt file URL

Silo 2: Web Search
- Input: research_topics.txt file URL
- Tasks: Search Google (Task 15)
- Output: search_results_1.txt, search_results_2.txt, etc. file URLs

Silo 3: Content Scraping
- Input: search_results_1.txt, search_results_2.txt, etc. file URLs
- Tasks: Scrape text content of website (Task 16)
- Output: scraped_content_1.txt, scraped_content_2.txt, etc. file URLs

Silo 4: Compatibility Analysis
- Input: scraped_content_1.txt, scraped_content_2.txt, etc. file URLs
- Tasks: GPT4 (Task 1)
- Output: compatibility_analysis_1.txt, compatibility_analysis_2.txt, etc. file URLs

Silo 5: Research Findings Compilation
- Input: compatibility_analysis_1.txt, compatibility_analysis_2.txt, etc. file URLs
- Tasks: GPT4 (Task 1), Save content to server (Task 9)
- Output: research_findings.txt file URL

subagent2refined-repeating

Here is the updated subagent structure with the "G) FULL TASKS (INCLUDING REPEATING TASKS)" section added:

A) SUBAGENT SUMMARY: ResearchAgent gathers information about suggested APIs, libraries, and their compatibility with Bubble through web searches and analysis.

C) SUBAGENT INPUT:
- The brainstorming output file URL, which contains a list of possible ways to build the software, including APIs, libraries, and SDKs.

F) SILOS:
Silo 1: Research Topic Extraction
- Input: brainstorming_output.txt file URL
- Tasks: Access text files already saved (Task 10), GPT4 (Task 1)
- Output: research_topics.txt file URL

Silo 2: Web Search
- Input: research_topics.txt file URL
- Tasks: Search Google (Task 15)
- Output: search_results_1.txt, search_results_2.txt, etc. file URLs

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Access text files already saved (Task 10): Retrieve the brainstorming_output.txt file from the server.
2. GPT4 (Task 1): Analyze the brainstorming_output.txt file and extract individual APIs, libraries, and SDKs to research. Save the extracted list as a text file (research_topics.txt) on the server.
3. For each API, library, and SDK in the research_topics.txt file (assuming there are N items):
   a. Search Google (Task 15): Conduct a web search to gather information about its functionality and compatibility with Bubble. Save the search results as a separate text file (search_results_N.txt) on the server.
   b. Scrape text content of website (Task 16): For the search_results_N.txt file, extract the relevant text content from the top 3 websites. Save the scraped content as a separate text file (scraped_content_N.txt) on the server.
   c. GPT4 (Task 1): Analyze the scraped_content_N.txt file to determine the API, library, or SDK's compatibility with Bubble and summarize the findings. Save the compatibility analysis as a separate text file (compatibility_analysis_N.txt) on the server.
4. GPT4 (Task 1): Compile the individual compatibility_analysis_N.txt files into a single research findings summary (research_findings.txt).
5. Save content to server (Task 9): Save the research_findings.txt file on the server.
6. Pass the research_findings.txt file URL to the TechnicalSequenceAgent for further processing.

Subagent2 task-ID-numbers

subagent2-output-variables

Here is the updated subagent structure with the "H) OUTPUT TOKENS" section added:

A) SUBAGENT SUMMARY: ResearchAgent gathers information about suggested APIs, libraries, and their compatibility with Bubble through web searches and analysis.

B) FINAL TASK OUTPUT:
- A text file (research_findings.txt) containing a summary of the research findings, approximately 2000-3000 words in length, saved on the server.
- The file URL [research-findings-url] is passed to the TechnicalSequenceAgent for further processing.

C) SUBAGENT INPUT:
- The brainstorming output file URL [brainstorming-output-url], which contains a list of possible ways to build the software, including APIs, libraries, and SDKs.

E) SUBAGENT TASK SUMMARY:
1. Access text files already saved (Task 10): Retrieve the brainstorming output from the server using [brainstorming-output-url].
2. GPT4 (Task 1): Analyze the brainstorming output and extract individual APIs, libraries, and SDKs to research. Save the extracted list as a text file (research_topics.txt) on the server and store the URL as [research-topics-url].
3. Search Google (Task 15): For each API, library, and SDK in the research_topics.txt file, conduct a web search to gather information about its functionality and compatibility with Bubble. Save the search results as separate text files (search_results_1.txt, search_results_2.txt, etc.) on the server and store the URLs as [search-results-1-url], [search-results-2-url], etc.
4. Scrape text content of website (Task 16): For each search result file, extract the relevant text content from the top 3 websites. Save the scraped content as separate text files (scraped_content_1.txt, scraped_content_2.txt, etc.) on the server and store the URLs as [scraped-content-1-url], [scraped-content-2-url], etc.
5. GPT4 (Task 1): Analyze the scraped content for each API, library, and SDK to determine its compatibility with Bubble and summarize the findings. Save the compatibility analysis as separate text files (compatibility_analysis_1.txt, compatibility_analysis_2.txt, etc.) on the server and store the URLs as [compatibility-analysis-1-url], [compatibility-analysis-2-url], etc.
6. GPT4 (Task 1): Compile the individual compatibility analysis files into a single research findings summary (research_findings.txt). Save the research_findings.txt file on the server and store the URL as [research-findings-url].
7. Pass the [research-findings-url] to the TechnicalSequenceAgent for further processing.

F) SILOS:
Silo 1: Research Topic Extraction
- Input: [brainstorming-output-url]
- Tasks: Access text files already saved (Task 10), GPT4 (Task 1)
- Output: [research-topics-url]

Silo 2: Web Search
- Input: [research-topics-url]
- Tasks: Search Google (Task 15)
- Output: [search-results-1-url], [search-results-2-url], etc.

Silo 3: Content Scraping
- Input: [search-results-1-url], [search-results-2-url], etc.
- Tasks: Scrape text content of website (Task 16)
- Output: [scraped-content-1-url], [scraped-content-2-url], etc.

Silo 4: Compatibility Analysis
- Input: [scraped-content-1-url], [scraped-content-2-url], etc.
- Tasks: GPT4 (Task 1)
- Output: [compatibility-analysis-1-url], [compatibility-analysis-2-url], etc.

Silo 5: Research Findings Compilation
- Input: [compatibility-analysis-1-url], [compatibility-analysis-2-url], etc.
- Tasks: GPT4 (Task 1), Save content to server (Task 9)
- Output: [research-findings-url]

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Access text files already saved (Task 10): Retrieve the brainstorming_output.txt file from the server using [brainstorming-output-url].
2. GPT4 (Task 1): Analyze the brainstorming_output.txt file and extract individual APIs, libraries, and SDKs to research. Save the extracted list as a text file (research_topics.txt) on the server and store the URL as [research-topics-url].
3. For each API, library, and SDK in the research_topics.txt file (assuming there are N items):
   a. Search Google (Task 15): Conduct a web search to gather information about its functionality and compatibility with Bubble. Save the search results as a separate text file (search_results_N.txt) on the server and store the URL as [search-results-N-url].
   b. Scrape text content of website (Task 16): For the search_results_N.txt file, extract the relevant text content from the top 3 websites. Save the scraped content as a separate text file (scraped_content_N.txt) on the server and store the URL as [scraped-content-N-url].
   c. GPT4 (Task 1): Analyze the scraped_content_N.txt file to determine the API, library, or SDK's compatibility with Bubble and summarize the findings. Save the compatibility analysis as a separate text file (compatibility_analysis_N.txt) on the server and store the URL as [compatibility-analysis-N-url].
4. GPT4 (Task 1): Compile the individual compatibility_analysis_N.txt files into a single research findings summary (research_findings.txt).
5. Save content to server (Task 9): Save the research_findings.txt file on the server and store the URL as [research-findings-url].
6. Pass the [research-findings-url] to the TechnicalSequenceAgent for further processing.

H) OUTPUT TOKENS:
- [brainstorming-output-url]: URL of the brainstorming output file containing a list of possible ways to build the software, including APIs, libraries, and SDKs.
- [research-topics-url]: URL of the text file containing the extracted list of individual APIs, libraries, and SDKs to research.
- [search-results-N-url]: URL of the text file containing the search results for the Nth API, library, or SDK.
- [scraped-content-N-url]: URL of the text file containing the scraped text content from the top 3 websites for the Nth API, library, or SDK.
- [compatibility-analysis-N-url]: URL of the text file containing the compatibility analysis for the Nth API, library, or SDK.
- [research-findings-url]: URL of the text file containing the compiled research findings summary.

subagent2refined-repeating-json-core

G) TASKFLOW SUMMARY

Title task: Retrieve brainstorming output
Description of the task: Access the brainstorming output file containing a list of possible ways to build the software, including APIs, libraries, and SDKs, from the server.
Input(s) involved: [brainstorming_output_file_url]
Output token name: brainstorming_output.txt

Title task: Extract research topics
Description of the task: Analyze the brainstorming output and extract individual APIs, libraries, and SDKs to research. Save the extracted list as a text file on the server.
Input(s) involved: brainstorming_output.txt
Output token name: research_topics.txt

Title task: Conduct web search
Description of the task: For each API, library, and SDK in the research topics list, conduct a web search to gather information about its functionality and compatibility with Bubble. Save the search results as separate text files on the server.
Input(s) involved: research_topics.txt
Output token name: search_results_1.txt, search_results_2.txt, etc.

Title task: Scrape website content
Description of the task: For each search result file, extract the relevant text content from the top 3 websites. Save the scraped content as separate text files on the server.
Input(s) involved: search_results_1.txt, search_results_2.txt, etc.
Output token name: scraped_content_1.txt, scraped_content_2.txt, etc.

Title task: Analyze compatibility
Description of the task: Analyze the scraped content for each API, library, and SDK to determine its compatibility with Bubble and summarize the findings. Save the compatibility analysis as separate text files on the server.
Input(s) involved: scraped_content_1.txt, scraped_content_2.txt, etc.
Output token name: compatibility_analysis_1.txt, compatibility_analysis_2.txt, etc.

Title task: Compile research findings
Description of the task: Compile the individual compatibility analysis files into a single research findings summary. Save the research findings summary file on the server.
Input(s) involved: compatibility_analysis_1.txt, compatibility_analysis_2.txt, etc.
Output token name: research_findings.txt

Title task: Pass research findings to TechnicalSequenceAgent
Description of the task: Pass the research findings summary file URL to the TechnicalSequenceAgent for further processing.
Input(s) involved: research_findings.txt
Output token name: research_findings_file_url

subagent2mermaid

SubAgent #3

claude 3 - subagent3

subagent1-refined

A) SUBAGENT SUMMARY: The TechnicalSequenceAgent evaluates brainstorming output and research findings to determine the most suitable technical approach for building the software.

B) FINAL TASK OUTPUT: A text file saved on the server (approximately 1000-2000 words) detailing the final technical sequence and approach for building the software, based on the evaluation of brainstorming output and research findings.

C) SUBAGENT INPUT:
- URL of the brainstorming output text file saved on the server
- URL of the research findings text file saved on the server

D) SUBAGENT TASK SUMMARY:
1. Access text files already saved (Task 10): Retrieve the brainstorming output text file from the server using the provided URL.
2. GPT4 (Task 1): Analyze the brainstorming output and extract a list of potential technical approaches, APIs, libraries, and SDKs.
3. Save content to server (Task 9): Save the extracted list as a text file on the server.
4. Access text files already saved (Task 10): Retrieve the research findings text file from the server using the provided URL.
5. GPT4 (Task 1): Analyze the research findings and determine the compatibility, feasibility, and suitability of each potential approach based on predefined criteria (e.g., compatibility with Bubble, ease of implementation, scalability, maintainability).
6. GPT4 (Task 1): Apply decision-making algorithms (e.g., weighted scoring, multi-criteria decision analysis) to rank the potential approaches based on the evaluation results.
7. GPT4 (Task 1): Select the highest-ranked technical approach and generate a detailed description of the final technical sequence, including the chosen APIs, libraries, SDKs, and their integration.
8. Save content to server (Task 9): Save the final technical sequence description as a text file on the server.
9. GPT4 (Task 1): Generate a summary of the final technical sequence, including the key components, their interactions, and the expected outcome.
10. Save content to server (Task 9): Append the summary to the final technical sequence text file on the server.
11. GPT4 (Task 1): Output the URL of the final technical sequence text file for the MockupSelectionAgent and SpecificationAgent to access.

E) SILOS:
Silo 1: Brainstorming Output Analysis
- Tasks: 1, 2, 3
- Input: URL of the brainstorming output text file
- Output: URL of the extracted list of potential technical approaches

Silo 2: Research Findings Analysis
- Tasks: 4, 5
- Input: URL of the research findings text file
- Output: Compatibility, feasibility, and suitability evaluation of each potential approach

Silo 3: Decision-Making and Final Technical Sequence Generation
- Tasks: 6, 7, 8, 9, 10
- Input: Evaluation results from Silo 2
- Output: URL of the final technical sequence text file, including the summary

Silo 4: Output for MockupSelectionAgent and SpecificationAgent
- Task: 11
- Input: URL of the final technical sequence text file
- Output: URL of the final technical sequence text file for the MockupSelectionAgent and SpecificationAgent to access

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Access text files already saved (Task 10): Retrieve the brainstorming output text file from the server using the provided URL.
2. GPT4 (Task 1): Analyze the brainstorming output and extract a list of potential technical approaches, APIs, libraries, and SDKs.
3. Save content to server (Task 9): Save the extracted list as a text file on the server.
4. Access text files already saved (Task 10): Retrieve the research findings text file from the server using the provided URL.
5. GPT4 (Task 1): Analyze the research findings and determine the compatibility, feasibility, and suitability of each potential approach based on predefined criteria (e.g., compatibility with Bubble, ease of implementation, scalability, maintainability).
6. GPT4 (Task 1): Apply decision-making algorithms (e.g., weighted scoring, multi-criteria decision analysis) to rank the potential approaches based on the evaluation results.
7. GPT4 (Task 1): Select the highest-ranked technical approach and generate a detailed description of the final technical sequence, including the chosen APIs, libraries, SDKs, and their integration.
8. Save content to server (Task 9): Save the final technical sequence description as a text file on the server.
9. GPT4 (Task 1): Generate a summary of the final technical sequence, including the key components, their interactions, and the expected outcome.
10. Save content to server (Task 9): Append the summary to the final technical sequence text file on the server.
11. GPT4 (Task 1): Output the URL of the final technical sequence text file for the MockupSelectionAgent and SpecificationAgent to access.

Based on the provided subagent task summary, there are no explicit instructions for repeating tasks or processing multiple items. The tasks are designed to handle the brainstorming output and research findings as single entities. Therefore, the full task list remains the same as the one provided in the subagent task summary, without any repetitions or duplications of individual tasks.

Subagent1 task-ID-numbers3

subagent3-output-variables

Here is the updated task list with output tokens:

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Access text files already saved (Task 10): Retrieve the brainstorming output text file from the server using the provided URL. [brainstorming-output]
2. GPT4 (Task 1): Analyze the brainstorming output and extract a list of potential technical approaches, APIs, libraries, and SDKs. [technical-approaches-list]
3. Save content to server (Task 9): Save the extracted list as a text file on the server. [technical-approaches-file]
4. Access text files already saved (Task 10): Retrieve the research findings text file from the server using the provided URL. [research-findings]
5. GPT4 (Task 1): Analyze the research findings and determine the compatibility, feasibility, and suitability of each potential approach based on predefined criteria (e.g., compatibility with Bubble, ease of implementation, scalability, maintainability). [approach-evaluation]
6. GPT4 (Task 1): Apply decision-making algorithms (e.g., weighted scoring, multi-criteria decision analysis) to rank the potential approaches based on the evaluation results. [approach-ranking]
7. GPT4 (Task 1): Select the highest-ranked technical approach and generate a detailed description of the final technical sequence, including the chosen APIs, libraries, SDKs, and their integration. [final-technical-sequence]
8. Save content to server (Task 9): Save the final technical sequence description as a text file on the server. [final-technical-sequence-file]
9. GPT4 (Task 1): Generate a summary of the final technical sequence, including the key components, their interactions, and the expected outcome. [final-technical-sequence-summary]
10. Save content to server (Task 9): Append the summary to the final technical sequence text file on the server. [final-technical-sequence-file-with-summary]
11. GPT4 (Task 1): Output the URL of the final technical sequence text file for the MockupSelectionAgent and SpecificationAgent to access. [final-technical-sequence-url]

H) OUTPUT TOKENS

[brainstorming-output]: The text file containing the brainstorming output.
[technical-approaches-list]: The list of potential technical approaches, APIs, libraries, and SDKs extracted from the brainstorming output.
[technical-approaches-file]: The text file containing the extracted list of potential technical approaches.
[research-findings]: The text file containing the research findings.
[approach-evaluation]: The evaluation results of each potential approach based on predefined criteria.
[approach-ranking]: The ranked list of potential approaches based on the evaluation results.
[final-technical-sequence]: The detailed description of the final technical sequence, including the chosen APIs, libraries, SDKs, and their integration.
[final-technical-sequence-file]: The text file containing the final technical sequence description.
[final-technical-sequence-summary]: The summary of the final technical sequence, including the key components, their interactions, and the expected outcome.
[final-technical-sequence-file-with-summary]: The text file containing both the final technical sequence description and its summary.
[final-technical-sequence-url]: The URL of the final technical sequence text file for the MockupSelectionAgent and SpecificationAgent to access.

subagent3refined-repeating-json-core

G) TASKFLOW SUMMARY

1. Title task: Retrieve brainstorming output
   Description of the task: Access the brainstorming output text file from the server using the provided URL.
   Input(s) involved: [Brainstorming Output URL]
   Output token name: [Brainstorming Output Text]

2. Title task: Extract potential technical approaches
   Description of the task: Analyze the brainstorming output and extract a list of potential technical approaches, APIs, libraries, and SDKs.
   Input(s) involved: [Brainstorming Output Text]
   Output token name: [Potential Technical Approaches List]

3. Title task: Save potential technical approaches
   Description of the task: Save the extracted list of potential technical approaches as a text file on the server.
   Input(s) involved: [Potential Technical Approaches List]
   Output token name: [Potential Technical Approaches URL]

4. Title task: Retrieve research findings
   Description of the task: Access the research findings text file from the server using the provided URL.
   Input(s) involved: [Research Findings URL]
   Output token name: [Research Findings Text]

5. Title task: Evaluate potential approaches
   Description of the task: Analyze the research findings and determine the compatibility, feasibility, and suitability of each potential approach based on predefined criteria.
   Input(s) involved: [Research Findings Text], [Potential Technical Approaches List]
   Output token name: [Evaluated Technical Approaches]

6. Title task: Rank potential approaches
   Description of the task: Apply decision-making algorithms to rank the potential approaches based on the evaluation results.
   Input(s) involved: [Evaluated Technical Approaches]
   Output token name: [Ranked Technical Approaches]

7. Title task: Select and describe final technical sequence
   Description of the task: Select the highest-ranked technical approach and generate a detailed description of the final technical sequence, including the chosen APIs, libraries, SDKs, and their integration.
   Input(s) involved: [Ranked Technical Approaches]
   Output token name: [Final Technical Sequence Description]

8. Title task: Save final technical sequence
   Description of the task: Save the final technical sequence description as a text file on the server.
   Input(s) involved: [Final Technical Sequence Description]
   Output token name: [Final Technical Sequence URL]

9. Title task: Generate final technical sequence summary
   Description of the task: Generate a summary of the final technical sequence, including the key components, their interactions, and the expected outcome.
   Input(s) involved: [Final Technical Sequence Description]
   Output token name: [Final Technical Sequence Summary]

10. Title task: Append summary to final technical sequence file
    Description of the task: Append the summary of the final technical sequence to the text file containing the detailed description on the server.
    Input(s) involved: [Final Technical Sequence Summary], [Final Technical Sequence URL]
    Output token name: [Updated Final Technical Sequence URL]

11. Title task: Output final technical sequence URL
    Description of the task: Output the URL of the final technical sequence text file for the MockupSelectionAgent and SpecificationAgent to access.
    Input(s) involved: [Updated Final Technical Sequence URL]
    Output token name: [Final Technical Sequence URL for Agents]

subagent3mermaid

prompts-required

Based on the provided components, the following unique prompts would be required for the agent workflow:

Task: Brainstorming possible implementations
Output of the task: A list of possible ways to build the software, including APIs, libraries, and SDKs
Description: Write a prompt that provides the LLM with the initial input (name of the tool, rough description of functionality, and potential APIs/libraries/SDKs) and asks it to generate a list of possible ways to build the software, including specific APIs, libraries, and SDKs that could be used.

Task: Reviewing research results and deciding on the final technical sequence
Output of the task: A decision on the final technical approach to build the software
Description: Write a prompt that provides the LLM with the brainstorming output and research results, and asks it to review the information and decide on the final technical sequence to build the software, taking into account the compatibility with Bubble and the project requirements.

Task: Selecting and editing design mockup pages
Output of the task: 3-4 HTML pages selected from the provided templates, with commentary on necessary edits
Description: Write a prompt that provides an LLM with vision capabilities (e.g., GPT Vision) with the 8 pre-designed HTML template pages and asks it to select the 3-4 most suitable pages for the project. The prompt should also ask the LLM to provide a commentary on the edits required for each selected page to align with the project requirements.

Task: Writing the introduction section of the technical specification document
Output of the task: The introduction section of the technical specification document
Description: Write a prompt that provides the LLM with the initial input and asks it to write the introduction section of the technical specification document, including an overview of the software, its purpose, target audience, and core functionalities.

Task: Writing the technical process section of the technical specification document
Output of the task: The technical process section of the technical specification document
Description: Write a prompt that provides the LLM with the final technical sequence and asks it to write the technical process section of the technical specification document, including a comprehensive description of the technical implementation, selected APIs, libraries, and SDKs, data flow and logic, and the underlying technical architecture and infrastructure.

Task: Writing the design page descriptions section of the technical specification document
Output of the task: The design page descriptions section of the technical specification document
Description: Write a prompt that provides the LLM with the selected and edited mockup pages and asks it to write the design page descriptions section of the technical specification document, explaining the purpose, functionality, and user interactions for each design mockup.

Task: Reviewing and finalizing each section of the technical specification document
Output of the task: The finalized technical specification document
Description: Write a prompt that asks the LLM to review and finalize each section of the technical specification document (introduction, technical process, and design page descriptions) to ensure coherence, clarity, and alignment with the project requirements. The prompt should also ask the LLM to make any necessary edits or improvements.

These unique prompts will guide the LLM through the various tasks required to generate the desired outputs for the agent workflow.