What is the primary purpose of the End-to-End Orchestrator?

It serves as a master controller that sequences multiple AI skills to handle the entire process of building a feature, from initial research to final quality assurance.

Can I use it for small bug fixes?

While possible, it is optimized for complete features. For quick fixes under 30 minutes, manual control or simpler skills are generally more efficient.

Does this skill require other plugins to function?

Yes, it is designed to coordinate five specific multi-ai skills: research, planning, implementation, testing, and verification.

How does it ensure code quality?

It uses automated quality gates at every stage, requiring high scores in planning and implementation, and enforcing a minimum of 95% test coverage during the verification phase.

End-to-End Development Orchestrator

Name: End-to-End Development Orchestrator
Author: adaptationio

byadaptationio

Productivity & Workflow

Orchestrates the complete software development lifecycle from research and planning to implementation and multi-layer verification.

About

The End-to-End Orchestrator is a high-level workflow manager designed to transform feature descriptions into production-ready code through an automated 5-stage pipeline. By coordinating specialized multi-AI skills for research, planning, implementation, testing, and verification, it ensures that every development cycle meets rigorous quality gates, including high test coverage and architectural integrity. It is the ideal solution for developers who need to implement complex features while maintaining production-grade standards through automatic failure recovery and state management.

Key Features

Automated 5-stage development pipeline with quality gates
0 GitHub stars
Comprehensive execution reporting and quality scoring
Coordination of multi-AI research, planning, and implementation
Independent test verification with 95%+ coverage targets
Automatic failure recovery and 'doom loop' prevention

Use Cases

Implementing complex, production-grade features from a single prompt
Automating the end-to-end lifecycle of feature development with minimal manual oversight
Standardizing high-quality engineering workflows across AI-assisted teams

About

Key Features

Automated 5-stage development pipeline with quality gates
0 GitHub stars
Comprehensive execution reporting and quality scoring
Coordination of multi-AI research, planning, and implementation
Independent test verification with 95%+ coverage targets
Automatic failure recovery and 'doom loop' prevention

Use Cases

Implementing complex, production-grade features from a single prompt
Automating the end-to-end lifecycle of feature development with minimal manual oversight
Standardizing high-quality engineering workflows across AI-assisted teams