ai-coding-factory
🥈SilverAI 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.
151500Updated 2mo ago
Intermediate30min to implementautomation
Saves ~480 min per use
Quick InstallView Source
git clone https://github.com/mitkox/ai-coding-factory.gitWorks with:
Claude
Overview
About This Skill
AI Coding Factory is an open-source platform that enables organizations to build and maintain .NET software in-house using local AI inference, eliminating dependency on external vendors. It enforces enterprise governance and traceability by default, with built-in Definition of Done templates, automated traceability reports, and release notes. The platform integrates with GitHub or Azure DevOps for version control, CI/CD, and backlog management, and supports offline-capable deployments through vLLM or LM Studio. Operations and development teams use AI Coding Factory to deliver auditable, private software with quality gates and security policies enforced throughout the story-to-release workflow. It includes .NET 8+ Clean Architecture templates, a Scrum Team as Code framework with specialized agents, and 12 reusable skills for automated development tasks.
How to Use
1. **Initialize Repository**: Clone your .NET project from GitHub/Azure DevOps. Ensure CI/CD pipelines are configured with your preferred triggers (e.g., PR merges, scheduled builds). 2. **Generate PR**: Use the prompt template to create a PR for a new feature, bug fix, or refactor. Replace placeholders with your specific context (e.g., [FEATURE_NAME] = "Payment Gateway Integration"). 3. **Review & Iterate**: Paste the AI-generated PR into your Git client or PR description. Review the code snippets, tests, and benchmarks. Ask the AI to refine any section (e.g., "Improve the error handling in UserActivityService.cs"). 4. **Merge & Deploy**: Use Azure DevOps/GitHub Actions to run the pipeline. The AI will automatically update the PR with CI/CD results and performance metrics. Tag releases with semantic versioning (e.g., v1.2.0). 5. **Governance**: Leverage GitHub/Azure DevOps traceability features to link PRs to tickets, track deployments, and audit changes. Use the AI to generate release notes from changelog entries.
Use Cases
Automating .NET software delivery with full audit trails and governance compliance
Running private AI-driven development without relying on external cloud vendors
Enforcing enterprise policy across story planning, development, testing, and release
Building microservices with Kubernetes-ready templates and automated quality gates
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-factoryRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Act as an AI Coding Factory engineer. Generate a pull request for [FEATURE_NAME] in our .NET [PROJECT_NAME] repository. Include: 1) A concise summary of changes, 2) Code snippets for critical logic, 3) Unit tests covering edge cases, 4) Performance benchmarks if applicable, 5) A changelog entry. Use [GITHUB_USERNAME] as the author. Reference ticket [TICKET_NUMBER].
Example Output
## Pull Request: Implement Real-Time User Activity Dashboard
**Summary**
This PR adds a real-time dashboard to track active users in our SaaS platform, reducing manual monitoring overhead by 40%. The feature integrates with Azure SignalR Service and updates metrics every 5 seconds.
**Key Changes**
- Added `UserActivityService.cs` with SignalR hub implementation
- Created `DashboardController.cs` with REST endpoints for metrics
- Implemented `ActivityTrackerMiddleware.cs` to log user sessions
- Updated `appsettings.json` with SignalR and Redis configurations
```csharp
// UserActivityService.cs
public class UserActivityService : Hub {
private readonly IRedisService _redis;
public async Task SendActivityUpdate(string userId) {
await _redis.SetAsync($"user:{userId}:active", DateTime.UtcNow);
await Clients.All.SendAsync("ReceiveActivityUpdate", userId);
}
}
```
**Unit Tests**
- `UserActivityServiceTests.cs`: Verifies SignalR message broadcasting
- `DashboardControllerTests.cs`: Tests endpoint responses with mock Redis
- `ActivityTrackerMiddlewareTests.cs`: Validates session logging for 10k concurrent users
**Performance Benchmarks**
- SignalR message latency: <50ms (95th percentile)
- Redis memory usage: 1.2MB for 100k active users
- Endpoint response time: 85ms average
**Changelog Entry**
[FEATURE] Real-time user activity dashboard with SignalR integration
[IMPROVEMENT] Reduced manual monitoring time by 40%
[BUG FIX] Fixed memory leak in session tracking (GitHub #1234)
**References**
- Ticket: PROJ-456
- Azure DevOps Pipeline: https://dev.azure.com/company/proj/_build?definitionId=12Apply to these tools
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