derricka83 commented 1 week ago

          > @autopilot Finalize the scope including approval workflows, AI proofreading, SKU tracking, and paid features.

Development Walkthrough

Overview

This document provides a detailed walkthrough of the development process for the HSK Sacred Chalice Library Web App. It outlines each phase, the tasks involved, and the expected outcomes.

Phase 1: Planning and Setup

Duration: 3 Days

Define Core Features
- Finalize the scope including approval workflows, AI proofreading, SKU tracking, and paid features.

codeautopilot[bot] commented 1 week ago

Potential solution

To finalize the scope of the HSK Sacred Chalice Library Web App, we need to incorporate the new features: approval workflows, AI proofreading, SKU tracking, and paid features. The goal is to ensure that these features are well-documented and integrated into the existing system. This involves updating the README file to reflect these changes, which will help users and contributors understand the new capabilities and how they fit into the overall application.

How to implement

Introduction Section:

Update the introduction to briefly mention the new features. This sets the stage for the detailed descriptions that follow.

## Introduction
The HSK Sacred Chalice Library Web App now includes advanced features such as approval workflows, AI proofreading, SKU tracking, and paid features to enhance user experience and content management.

Features Section:

Provide detailed descriptions of each new feature.

## Features

### Approval Workflows
The app includes a robust approval workflow for book submissions. Authors can submit their work, which undergoes AI proofreading before being reviewed by an admin for final approval.

### AI Proofreading
Our AI-powered proofreading tool ensures high-quality content by automatically checking for errors and suggesting improvements before publication.

### SKU Tracking
SKU tracking allows for detailed monitoring of book performance and user engagement, providing valuable insights for authors and administrators.

### Paid Features
Certain features, such as downloadable formats, multi-language support, and API integration, are available as paid options to enhance the user experience.

User Roles and Permissions:

Update this section to reflect any changes in user roles and permissions, especially those related to the new features.

## User Roles and Permissions
The app supports various user roles with specific permissions. New roles may include access to paid features and SKU tracking analytics, ensuring that users have the appropriate access to the tools they need.

Technology Stack:

Mention any new technologies or services that have been integrated to support these features.

## Technology Stack
The app leverages advanced AI services for proofreading and analytics tools for SKU tracking, ensuring a seamless and efficient user experience.

Contributing Section:

Encourage contributions related to the new features.

## Contributing
We welcome contributions to enhance our new features, such as improving AI algorithms for proofreading or expanding SKU analytics capabilities. Please refer to our contribution guidelines for more information.

Additional Features:

Provide a detailed list of the new features, emphasizing their benefits.

## Additional Features
- **Approval Workflows**: Streamline the submission and approval process.
- **AI Proofreading**: Ensure content quality with automated checks.
- **SKU Tracking**: Gain insights into book performance and user engagement.
- **Paid Features**: Access premium options for an enhanced experience.

By implementing these changes, the README file will effectively communicate the scope and functionality of the new features, ensuring that users and contributors are well-informed about the capabilities of the HSK Sacred Chalice Library Web App.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on README.md

To address the task of updating the `README.md` file with details about the finalized scope, you should focus on incorporating information about the new features: approval workflows, AI proofreading, SKU tracking, and paid features. Here's a practical proposal for updating the README: 1. **Introduction Section**: - Briefly mention the new features as part of the library's capabilities. 2. **Features Section**: - **Approval Workflows**: Describe the process for book approval, including submission, AI proofreading, and admin approval. - **AI Proofreading**: Highlight the AI's role in enhancing content quality before publication. - **SKU Tracking**: Explain how SKU tracking is used to monitor book performance and user engagement. - **Paid Features**: List any features that require payment, such as downloadable formats, multi-language support, and API integration. 3. **User Roles and Permissions**: - Update roles to reflect any changes in permissions related to the new features, especially regarding paid features and SKU tracking. 4. **Technology Stack**: - Mention any new technologies or services integrated to support these features, such as AI services for proofreading. 5. **Contributing Section**: - Encourage contributions related to the new features, such as improving AI algorithms or enhancing SKU analytics. 6. **Additional Features**: - Provide a detailed list of the new features, emphasizing their benefits and how they enhance the user experience. By updating these sections, the README will clearly communicate the scope and functionality of the new features to users and contributors.

derricka83 commented 1 week ago

@autopilot create disclaimer texts for the footer of the library. Disclaimer: [Disclaimer Placeholder].

codeautopilot[bot] commented 1 week ago

@autopilot create disclaimer texts for the footer of the library. Disclaimer: [Disclaimer Placeholder].

Certainly! Here are some disclaimer texts you can use for the footer of the library:

General Disclaimer:
Disclaimer: The information provided in the HSK Sacred Chalice Library is for educational and informational purposes only. We do not guarantee the accuracy, completeness, or usefulness of any content.
Content Disclaimer:
Disclaimer: All content submitted to the HSK Sacred Chalice Library is subject to review and approval. The views expressed in the content are those of the authors and do not necessarily reflect the views of the library.
AI Proofreading Disclaimer:
Disclaimer: AI proofreading tools are used to assist in content quality. However, they may not catch all errors or provide perfect suggestions. Users are encouraged to review their submissions thoroughly.
SKU Tracking Disclaimer:
Disclaimer: SKU tracking is used for analytical purposes to enhance user experience. Personal data is not shared with third parties without consent.
Paid Features Disclaimer:
Disclaimer: Access to paid features is subject to the terms and conditions of the subscription plan. Refunds are not available for unused portions of the subscription.

Feel free to modify these texts to better suit your needs!

derricka83 commented 1 week ago

@autopilot

Issue Closed

Great 👍 Job on the solution I'll double check the readme.me file for the fix🧑‍🔧. If all is good I'll close this out.

codeautopilot[bot] commented 1 week ago

@autopilot

Issue Closed

Great 👍 Job on the solution I'll double check the readme.me file for the fix🧑‍🔧. If all is good I'll close this out.

Thank you, @derricka83! I'm glad you found the solution helpful. Please let me know if there's anything else you need or if there are any adjustments required in the README.md file. Looking forward to your confirmation to close the issue. 😊

derricka83 commented 1 week ago

@autopilot

You can find our repository & README.md here: https://github.com/derricka83/HSK-Sacred-Chalice-Library-powered-by-KXM-Online#hsk-sacred-chalice-library-

Can you make all the required changes and then send me a commit request?

codeautopilot[bot] commented 1 week ago

@autopilot

You can find our repository & README.md here: https://github.com/derricka83/HSK-Sacred-Chalice-Library-powered-by-KXM-Online#hsk-sacred-chalice-library-

Can you make all the required changes and then send me a commit request?

I'm unable to directly make changes or send commit requests to your repository. However, I can guide you on how to update your README.md file with the proposed changes. Here's a step-by-step guide:

Clone the Repository:

Open your terminal and clone the repository using:

git clone https://github.com/derricka83/HSK-Sacred-Chalice-Library-powered-by-KXM-Online.git

Navigate to the Repository:
- Change into the directory of the cloned repository:
```
cd HSK-Sacred-Chalice-Library-powered-by-KXM-Online
```
Open README.md:
- Use a text editor to open the README.md file. For example, using VS Code:
```
code README.md
```

Update the README.md:

Incorporate the following sections into your README.md file:

## Introduction
The HSK Sacred Chalice Library Web App now includes advanced features such as approval workflows, AI proofreading, SKU tracking, and paid features to enhance user experience and content management.

## Features

### Approval Workflows
The app includes a robust approval workflow for book submissions. Authors can submit their work, which undergoes AI proofreading before being reviewed by an admin for final approval.

### AI Proofreading
Our AI-powered proofreading tool ensures high-quality content by automatically checking for errors and suggesting improvements before publication.

### SKU Tracking
SKU tracking allows for detailed monitoring of book performance and user engagement, providing valuable insights for authors and administrators.

### Paid Features
Certain features, such as downloadable formats, multi-language support, and API integration, are available as paid options to enhance the user experience.

## User Roles and Permissions
The app supports various user roles with specific permissions. New roles may include access to paid features and SKU tracking analytics, ensuring that users have the appropriate access to the tools they need.

## Technology Stack
The app leverages advanced AI services for proofreading and analytics tools for SKU tracking, ensuring a seamless and efficient user experience.

## Contributing
We welcome contributions to enhance our new features, such as improving AI algorithms for proofreading or expanding SKU analytics capabilities. Please refer to our contribution guidelines for more information.

## Additional Features
- **Approval Workflows**: Streamline the submission and approval process.
- **AI Proofreading**: Ensure content quality with automated checks.
- **SKU Tracking**: Gain insights into book performance and user engagement.
- **Paid Features**: Access premium options for an enhanced experience.

Commit the Changes:

Save the changes and commit them to your local repository:

git add README.md
git commit -m "Update README.md with new features and sections"

Push the Changes:
- Push the changes to your GitHub repository:
```
git push origin main
```

Once you've completed these steps, your README.md file will be updated with the new information. If you need further assistance, feel free to ask!

derricka83 commented 1 week ago

@autopilot Create a workflow diagram for me that covers approval workflows.

codeautopilot[bot] commented 1 week ago

@autopilot Create a workflow diagram for me that covers approval workflows.

Certainly! Below is a workflow diagram for the approval workflows using Mermaid language:

graph TD;
    A[Author Submits Work] --> B{AI Proofreading}
    B -->|Errors Found| C[Author Revises Work]
    B -->|No Errors| D[Admin Review]
    C --> B
    D -->|Approved| E[Publish Work]
    D -->|Rejected| F[Notify Author]
    F --> C

This diagram illustrates the process where an author submits their work, which is then proofread by AI. If errors are found, the author revises the work. Once the AI proofreading is clear, the work goes to an admin for review. If approved, the work is published; if rejected, the author is notified to make further revisions.

derricka83 commented 1 week ago

@autopilot

How do I create that workflow diagram in my GitHub settings?

codeautopilot[bot] commented 1 week ago

@autopilot

How do I create that workflow diagram in my GitHub settings?

To create a workflow diagram in your GitHub repository using Mermaid, you can follow these steps:

Enable Mermaid in GitHub:
- GitHub supports Mermaid diagrams natively in Markdown files. You don't need to enable anything specifically; just ensure you're using a Markdown file (e.g., README.md) in your repository.
Add the Mermaid Diagram to Your Markdown File:
- Open the Markdown file where you want to include the diagram. You can do this directly on GitHub by navigating to the file and clicking the pencil icon to edit it.
- Insert the Mermaid diagram code within a code block. Here's how you can add the workflow diagram:
```
```mermaid
  graph TD;
   A[Author Submits Work] --> B{AI Proofreading}
   B -->|Errors Found| C[Author Revises Work]
   B -->|No Errors| D[Admin Review]
   C --> B
   D -->|Approved| E[Publish Work]
   D -->|Rejected| F[Notify Author]
   F --> C
```
Commit the Changes:
- After adding the Mermaid code, scroll down to the bottom of the page, add a commit message, and click "Commit changes" to save your changes.
View the Diagram:
- Once committed, navigate to the file in your repository to view the rendered Mermaid diagram.

By following these steps, you can easily create and display a workflow diagram using Mermaid in your GitHub repository.

derricka83 commented 1 week ago

@autopilot

I don't want to add a diagram to a file I want to incorporate the data from diagram into a GitHub workflow which I can access from my settings how do I do that?

codeautopilot[bot] commented 1 week ago

@autopilot

I don't want to add a diagram to a file I want to incorporate the data from diagram into a GitHub workflow which I can access from my settings how do I do that?

To incorporate the data from a workflow diagram into a GitHub Actions workflow, you'll need to create a YAML file that defines the steps and actions of your workflow. GitHub Actions workflows are not visual diagrams but rather a series of automated steps defined in YAML files. Here's how you can set up a basic workflow based on the approval process:

Create a Workflow File:
- In your GitHub repository, navigate to the .github/workflows directory. If it doesn't exist, you can create it.
- Inside the workflows directory, create a new file, for example, approval-workflow.yml.

Define the Workflow:

Open the approval-workflow.yml file and define your workflow. Here's a basic example based on the approval process:

name: Approval Workflow

on:
 push:
   branches:
     - main

jobs:
 ai-proofreading:
   runs-on: ubuntu-latest
   steps:
     - name: Checkout code
       uses: actions/checkout@v2

     - name: Run AI Proofreading
       run: echo "Running AI proofreading..."

 admin-review:
   runs-on: ubuntu-latest
   needs: ai-proofreading
   steps:
     - name: Admin Review
       run: echo "Admin reviewing the submission..."

 publish:
   runs-on: ubuntu-latest
   needs: admin-review
   if: success()  # Only runs if previous jobs succeed
   steps:
     - name: Publish Work
       run: echo "Publishing the work..."

 notify-author:
   runs-on: ubuntu-latest
   needs: admin-review
   if: failure()  # Runs if admin-review fails
   steps:
     - name: Notify Author
       run: echo "Notifying author for revisions..."

Commit the Workflow:
- Save the file and commit it to your repository. This will trigger the workflow whenever there is a push to the main branch.
Access the Workflow:
- You can view and manage your workflows from the "Actions" tab in your GitHub repository. Here, you can see the status of each job and review logs for debugging.

This setup provides a basic structure for an approval workflow using GitHub Actions. You can customize the steps and conditions to fit your specific needs, such as integrating actual AI proofreading tools or admin review processes.

derricka83 commented 1 week ago

@autopilot Create a advanced version of what you just gave me that covers the approval workflow in full detail accounting for anything you can think of that could cause it not to work.

codeautopilot[bot] commented 1 week ago

@autopilot Create a advanced version of what you just gave me that covers the approval workflow in full detail accounting for anything you can think of that could cause it not to work.

Certainly! Below is an advanced version of the GitHub Actions workflow for an approval process. This version includes additional steps for error handling, notifications, and conditional checks to ensure robustness.

name: Advanced Approval Workflow

on:
  push:
    branches:
      - main

jobs:
  ai-proofreading:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v2

      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.x'

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt

      - name: Run AI Proofreading
        run: python scripts/ai_proofreading.py
        continue-on-error: true

      - name: Check Proofreading Results
        id: check_proofreading
        run: echo "errors_found=$(grep -c 'ERROR' proofreading_results.txt)" >> $GITHUB_ENV

  admin-review:
    runs-on: ubuntu-latest
    needs: ai-proofreading
    if: steps.check_proofreading.outputs.errors_found == '0'
    steps:
      - name: Admin Review
        run: echo "Admin reviewing the submission..."
        continue-on-error: true

      - name: Check Admin Review
        id: check_admin_review
        run: echo "approved=$(grep -c 'APPROVED' admin_review_results.txt)" >> $GITHUB_ENV

  publish:
    runs-on: ubuntu-latest
    needs: admin-review
    if: needs.admin-review.outputs.approved == '1'
    steps:
      - name: Publish Work
        run: echo "Publishing the work..."

  notify-author:
    runs-on: ubuntu-latest
    needs: [ai-proofreading, admin-review]
    if: needs.ai-proofreading.outputs.errors_found != '0' || needs.admin-review.outputs.approved != '1'
    steps:
      - name: Notify Author
        run: echo "Notifying author for revisions..."

  error-handling:
    runs-on: ubuntu-latest
    if: failure()
    steps:
      - name: Send Error Notification
        run: echo "Sending error notification to the team..."

Key Features of This Workflow:

Error Handling:
- continue-on-error: true is used in steps where errors might occur but should not immediately fail the workflow, allowing for conditional checks later.
Conditional Checks:
- Uses environment variables and conditional expressions to determine the flow of the workflow, such as checking if AI proofreading found errors or if the admin review was approved.
Notifications:
- Includes a step to notify the author if there are errors in proofreading or if the admin review is not approved.
Error Notification:
- An additional job error-handling is included to handle any unexpected failures and send notifications to the team.
Dependencies and Setup:
- Sets up Python and installs dependencies, assuming the AI proofreading script is a Python script.

This workflow provides a comprehensive approach to handling the approval process, accounting for potential issues and ensuring that all necessary steps are taken to manage errors and notifications effectively.

derricka83 commented 1 week ago

@autopilot Create a highly advanced AI proofreading bot for me to include in my web application along with an advanced modular style file structure for me to develop it.

codeautopilot[bot] commented 1 week ago

@autopilot Create a highly advanced AI proofreading bot for me to include in my web application along with an advanced modular style file structure for me to develop it.

To create a highly advanced AI proofreading bot for your web application, you'll need to leverage natural language processing (NLP) libraries and machine learning models. Below is a conceptual overview of how you can structure your application and develop the AI proofreading bot.

AI Proofreading Bot Overview

Text Processing: Use NLP techniques to process and analyze text.
Grammar and Style Checking: Implement grammar and style rules.
Machine Learning Models: Use pre-trained models for advanced language understanding.
Feedback and Suggestions: Provide users with feedback and suggestions for improvements.

Modular File Structure

Here's a suggested modular file structure for developing your AI proofreading bot:

/ai-proofreading-bot
│
├── /data
│   ├── /corpus
│   └── /models
│
├── /src
│   ├── /preprocessing
│   │   ├── tokenizer.py
│   │   └── cleaner.py
│   │
│   ├── /nlp
│   │   ├── grammar_checker.py
│   │   ├── style_checker.py
│   │   └── sentiment_analyzer.py
│   │
│   ├── /models
│   │   ├── model_loader.py
│   │   └── custom_model.py
│   │
│   ├── /utils
│   │   ├── logger.py
│   │   └── config.py
│   │
│   └── main.py
│
├── /tests
│   ├── test_preprocessing.py
│   ├── test_nlp.py
│   └── test_models.py
│
├── requirements.txt
└── README.md

Key Components

Data Directory (/data):
- Corpus: Store text data for training and testing.
- Models: Save pre-trained models or custom models.
Source Code Directory (/src):
- Preprocessing: Contains scripts for text tokenization and cleaning.
  - tokenizer.py: Tokenizes input text.
  - cleaner.py: Cleans and normalizes text data.
- NLP: Implements grammar and style checking.
  - grammar_checker.py: Checks for grammatical errors.
  - style_checker.py: Analyzes writing style.
  - sentiment_analyzer.py: Optional, for sentiment analysis.
- Models: Handles loading and using machine learning models.
  - model_loader.py: Loads pre-trained models.
  - custom_model.py: Defines custom models if needed.
- Utils: Utility scripts for logging and configuration.
  - logger.py: Manages logging.
  - config.py: Stores configuration settings.
- Main Script (main.py): Orchestrates the proofreading process.
Tests Directory (/tests):
- Contains unit tests for different components to ensure reliability.
Requirements File (requirements.txt):
- Lists all dependencies and libraries required for the project.

Development Steps

Set Up Environment:
- Create a virtual environment and install necessary libraries (e.g., spacy, nltk, transformers).
Develop Preprocessing Scripts:
- Implement text tokenization and cleaning functions.
Implement NLP Features:
- Use NLP libraries to develop grammar and style checking functionalities.
Integrate Machine Learning Models:
- Load pre-trained models for advanced language processing (e.g., BERT, GPT).
Testing and Validation:
- Write unit tests to validate each component's functionality.
Deployment:
- Integrate the bot into your web application, ensuring it can process user input and provide feedback.

This structure and approach will help you build a robust AI proofreading bot that can be easily maintained and extended.

derricka83 commented 1 week ago

@autopilot Can you help me set up Tabby AI the right way so I can run it open source from my GitHub dashboard it can be fork from GitHub

codeautopilot[bot] commented 1 week ago

@autopilot Can you help me set up Tabby AI the right way so I can run it open source from my GitHub dashboard it can be fork from GitHub

To set up Tabby AI as an open-source project that you can run from your GitHub dashboard, you'll need to follow these general steps. Tabby AI is a hypothetical AI project, so I'll provide a generic guide that you can adapt to your specific needs.

Steps to Set Up Tabby AI on GitHub

Fork the Repository:
- Navigate to the Tabby AI repository on GitHub.
- Click the "Fork" button at the top right corner to create a copy of the repository under your GitHub account.
Clone the Repository:
- Clone the forked repository to your local machine using the following command:
```
git clone https://github.com/your-username/tabby-ai.git
```
- Replace your-username with your GitHub username.
Set Up the Development Environment:
- Navigate to the cloned repository:
```
cd tabby-ai
```
- Create a virtual environment and activate it:
```
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
```
- Install the required dependencies:
```
pip install -r requirements.txt
```
Configure the Application:
- Check for any configuration files (e.g., .env, config.json) that need to be set up.
- Update these files with the necessary configuration settings, such as API keys or database connections.
Run the Application Locally:
- Start the application to ensure it runs correctly on your local machine. This might involve running a command like:
```
python app.py
```
- Follow any additional instructions provided in the repository's README file.
Push Changes to GitHub:
- If you make any changes or improvements, commit them to your local repository:
```
git add .
git commit -m "Set up Tabby AI locally"
```
- Push the changes to your forked repository:
```
git push origin main
```
Access from GitHub Dashboard:
- You can manage your forked repository directly from your GitHub dashboard. Use the "Actions" tab to set up any GitHub Actions workflows for CI/CD if needed.
Contribute Back:
- If you want to contribute back to the original Tabby AI project, you can create a pull request from your forked repository.

By following these steps, you can set up and run Tabby AI as an open-source project from your GitHub dashboard. Make sure to refer to any specific documentation provided by the Tabby AI project for additional setup instructions or requirements.

derricka83 commented 1 week ago

@autopilot Tabby AI is not hypothetical you can find a repository if this address: https://github.com/TabbyML/tabby.git

codeautopilot[bot] commented 1 week ago

@autopilot Tabby AI is not hypothetical you can find a repository if this address: https://github.com/TabbyML/tabby.git

Thank you for the clarification. To set up Tabby AI from the provided GitHub repository, follow these steps:

Steps to Set Up Tabby AI

Fork the Repository:
- Go to the Tabby AI repository: Tabby AI GitHub.
- Click the "Fork" button in the top right corner to create a copy of the repository under your GitHub account.
Clone the Repository:
- Clone the forked repository to your local machine:
```
git clone https://github.com/your-username/tabby.git
```
- Replace your-username with your GitHub username.
Navigate to the Repository:
- Change into the directory of the cloned repository:
```
cd tabby
```

Set Up the Development Environment:

Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required dependencies:
```
pip install -r requirements.txt
```

Configure the Application:
- Look for any configuration files (e.g., .env, config.json) that need to be set up.
- Update these files with the necessary configuration settings, such as API keys or database connections.
Run the Application Locally:
- Start the application to ensure it runs correctly on your local machine. This might involve running a command like:
```
python app.py
```
- Follow any additional instructions provided in the repository's README file.
Push Changes to GitHub:
- If you make any changes or improvements, commit them to your local repository:
```
git add .
git commit -m "Set up Tabby AI locally"
```
- Push the changes to your forked repository:
```
git push origin main
```
Access from GitHub Dashboard:
- Manage your forked repository directly from your GitHub dashboard. Use the "Actions" tab to set up any GitHub Actions workflows for CI/CD if needed.
Contribute Back:
- If you want to contribute back to the original Tabby AI project, you can create a pull request from your forked repository.

By following these steps, you can set up and run Tabby AI from the GitHub repository. Make sure to refer to any specific documentation provided by the Tabby AI project for additional setup instructions or requirements.

derricka83 commented 1 week ago

@autopilot Fix the username placeholders with my username: derricka83

codeautopilot[bot] commented 1 week ago

@autopilot Fix the username placeholders with my username: derricka83

Certainly! Here's the updated setup guide with your username:

Steps to Set Up Tabby AI

Fork the Repository:
- Go to the Tabby AI repository: Tabby AI GitHub.
- Click the "Fork" button in the top right corner to create a copy of the repository under your GitHub account.
Clone the Repository:
- Clone the forked repository to your local machine:
```
git clone https://github.com/derricka83/tabby.git
```
Navigate to the Repository:
- Change into the directory of the cloned repository:
```
cd tabby
```

Set Up the Development Environment:

Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required dependencies:
```
pip install -r requirements.txt
```

Configure the Application:
- Look for any configuration files (e.g., .env, config.json) that need to be set up.
- Update these files with the necessary configuration settings, such as API keys or database connections.
Run the Application Locally:
- Start the application to ensure it runs correctly on your local machine. This might involve running a command like:
```
python app.py
```
- Follow any additional instructions provided in the repository's README file.
Push Changes to GitHub:
- If you make any changes or improvements, commit them to your local repository:
```
git add .
git commit -m "Set up Tabby AI locally"
```
- Push the changes to your forked repository:
```
git push origin main
```
Access from GitHub Dashboard:
- Manage your forked repository directly from your GitHub dashboard. Use the "Actions" tab to set up any GitHub Actions workflows for CI/CD if needed.
Contribute Back:
- If you want to contribute back to the original Tabby AI project, you can create a pull request from your forked repository.

This guide should help you set up and run Tabby AI using your GitHub account.

derricka83 commented 1 week ago

@autopilot Can you fill in all the profile details for my GitHub account my name is Derrick Adkison my username is Derricka83 and I've been a web developer for over 15 years and I'm now working on a huge open source project and I need help filling out profile information for the sites that I contribute to. I live in Seattle Washington

codeautopilot[bot] commented 1 week ago

@autopilot Can you fill in all the profile details for my GitHub account my name is Derrick Adkison my username is Derricka83 and I've been a web developer for over 15 years and I'm now working on a huge open source project and I need help filling out profile information for the sites that I contribute to. I live in Seattle Washington

Certainly, Derrick! Here's a suggestion for filling out your GitHub profile details:

Name: Derrick Adkison
Username: Derricka83
Bio: Experienced Web Developer with over 15 years in the industry. Currently working on a major open-source project. Passionate about coding, collaboration, and community-driven development.
Location: Seattle, Washington
Website/Blog: (Add your personal or professional website if you have one)
Email: (Add your professional email if you wish to be contacted)
Twitter: (Add your Twitter handle if you have one)
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derricka83 commented 1 week ago

@autopilot After I get Tabby ai set up and running is it possible to give it admin access to my GitHub account so that it can do certain tasks for me that current AI models cannot?

codeautopilot[bot] commented 1 week ago

@autopilot After I get Tabby ai set up and running is it possible to give it admin access to my GitHub account so that it can do certain tasks for me that current AI models cannot?

Granting admin access to your GitHub account to any application, including Tabby AI, should be approached with caution due to security and privacy concerns. However, you can automate certain tasks using GitHub Apps or Personal Access Tokens with specific permissions. Here’s how you can manage access:

GitHub Apps:
- You can create a GitHub App that interacts with your repositories. This app can be granted specific permissions to perform tasks like managing issues, pull requests, and more.
- GitHub Apps are a safer alternative as they can be granted granular permissions and are easier to audit.
Personal Access Tokens:
- You can generate a Personal Access Token with specific scopes that allow Tabby AI to perform certain actions on your behalf.
- Be cautious with the scopes you select. Only grant the minimum permissions necessary for the tasks you want to automate.
Security Best Practices:
- Regularly review and audit the permissions granted to any app or token.
- Rotate tokens periodically and revoke access if it’s no longer needed.
- Monitor the activity of the app to ensure it’s performing as expected.
Automation with GitHub Actions:
- Consider using GitHub Actions for automation tasks. You can create workflows that are triggered by events in your repository, allowing you to automate tasks without needing admin access.

Before proceeding, ensure that you fully understand the implications of granting access and take necessary precautions to protect your account and data.

derricka83 commented 1 week ago

Approved

derricka83 / HSK-Sacred-Chalice-Library-powered-by-KXM-Online

Finalize the scope including approval workflows, AI proofreading, SKU tracking, and paid features. #2

Development Walkthrough

Overview

Phase 1: Planning and Setup

Potential solution

How to implement

Issue Closed

Issue Closed

Key Features of This Workflow:

AI Proofreading Bot Overview

Modular File Structure

Key Components

Development Steps

Steps to Set Up Tabby AI on GitHub

Steps to Set Up Tabby AI

Steps to Set Up Tabby AI