ai-coding-factory

🥈Silver

AI Coding Factory replaces outsourcing vendors with internal AI for .NET development. Operations teams use it to build and maintain software in-house, with traceability and governance. It integrates with GitHub and Azure DevOps for version control and CI/CD.

134500Updated Today

Intermediate30min to implementautomation

Saves ~480 min per use

Quick InstallView Source

git clone https://github.com/mitkox/ai-coding-factory.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/mitkox/ai-coding-factory

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Act as an AI Coding Factory. I need to replace my software outsourcing vendor contracts with my own AI. I work at [COMPANY] in the [INDUSTRY] sector. I need to [SPECIFIC TASK, e.g., develop a customer management system, automate data entry, etc.]. Provide a step-by-step plan to achieve this, including the technologies needed, estimated time, and potential challenges.

Example Output

# AI Coding Factory Plan for [COMPANY]

## Overview
This plan outlines the steps to replace your software outsourcing vendor contracts with an in-house AI solution for [SPECIFIC TASK].

## Step-by-Step Plan
1. **Requirement Analysis**
   - Gather detailed requirements from stakeholders.
   - Identify key features and functionalities needed.
   - Estimate the scope and complexity of the project.

2. **Technology Stack Selection**
   - **Frontend**: React.js for a dynamic user interface.
   - **Backend**: Node.js with Express for server-side operations.
   - **Database**: PostgreSQL for reliable data storage.
   - **AI Integration**: TensorFlow for machine learning capabilities.

3. **Development Timeline**
   - **Phase 1**: Initial setup and basic functionality (4 weeks).
   - **Phase 2**: Integration of AI components (6 weeks).
   - **Phase 3**: Testing and deployment (4 weeks).

4. **Potential Challenges**
   - **Data Privacy**: Ensure compliance with data protection regulations.
   - **Integration**: Seamless integration with existing systems.
   - **Scalability**: Design the system to handle future growth.

5. **Cost Estimation**
   - Initial setup cost: $50,000.
   - Ongoing maintenance cost: $10,000 annually.

## Conclusion
By following this plan, [COMPANY] can successfully transition from outsourcing to an in-house AI solution, reducing costs and increasing control over the development process.