orchestr8

🥈Silver

Orchestr8 transforms Claude code into a complete software engineering team. It automates development workflows, enabling operations teams to streamline code generation, review, testing, and deployment. Integrates with CI/CD pipelines and developer tools for efficient software engineering.

6430Updated 1mo ago

Intermediate30min to implementautomation

Saves ~480 min per use

Quick InstallView Source

git clone https://github.com/seth-schultz/orchestr8.git

Works with:

Claude

Overview

About This Skill

How to Use

1. **Prepare Your Repository**: Clone your project into a clean directory or use an existing repository. Ensure it follows a standard structure (src/, tests/, docs/). 2. **Define Requirements**: Clearly specify the features, programming language, and target environment in your prompt. Include any existing constraints or preferences. 3. **Run Orchestration**: Paste the prompt into your AI assistant and execute. Monitor the generation process and review initial outputs for alignment with your expectations. 4. **Iterate and Refine**: Review the generated code, tests, and documentation. Provide feedback to the AI to adjust implementations, fix issues, or add missing components. 5. **Integrate CI/CD**: Connect the generated pipeline to your version control system. Configure environment variables, secrets, and deployment targets according to your infrastructure. Tips: - For complex projects, break the orchestration into phases (e.g., backend first, then frontend) - Use version control to track changes and roll back if needed - Validate generated tests against your actual requirements - Customize the CI/CD pipeline to match your team's existing tools and processes

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/seth-schultz/orchestr8

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 engineering team orchestrator. Use the [REPOSITORY_PATH] to generate a complete software solution for [PROJECT_NAME]. Implement [SPECIFIC_FEATURES] with best practices in [PROGRAMMING_LANGUAGE]. Create unit tests, integration tests, and documentation. Set up a CI/CD pipeline using [CI_TOOL] for automated testing and deployment. Provide a deployment strategy for [TARGET_ENVIRONMENT].

Example Output

For the project 'E-commerce Analytics Dashboard' in the repository '/home/user/ecommerce-dashboard', I've implemented the following:

**Architecture:** A React frontend with TypeScript, Node.js backend with Express, and PostgreSQL database. The system follows a microservices approach with clear separation between API gateway, user service, product service, and analytics service.

**Features Implemented:**
1. User authentication with JWT tokens and role-based access control
2. Product catalog with search and filtering capabilities
3. Real-time sales analytics dashboard using Chart.js
4. Order processing workflow with Stripe integration
5. Admin panel for inventory management

**Testing:**
- 47 unit tests covering core business logic (92% coverage)
- 15 integration tests for API endpoints (88% coverage)
- 8 end-to-end tests using Cypress
- Performance tests with k6 showing 99.9% uptime under load

**CI/CD Pipeline:**
- GitHub Actions workflow triggered on PRs to main branch
- Automated linting with ESLint and Prettier
- Security scanning with Snyk
- Docker image building and pushing to AWS ECR
- Automated deployment to staging environment on successful tests
- Manual approval required for production deployment

**Documentation:**
- Comprehensive README with setup instructions
- API documentation using Swagger
- Architecture decision records in docs/adr/
- Deployment guide for AWS EKS

The system is ready for production deployment with all security checks passing and performance benchmarks meeting requirements. All services are containerized and can be deployed with a single command using Docker Compose or Kubernetes manifests.