What happens if a review fails?

If a reviewer like Codex rejects a plan or implementation, the skill identifies the issues, prompts for fixes, and restarts the review cycle automatically.

How does it manage task state?

The skill uses a local .task/ directory to store JSON files representing the current state, refined plans, and review results, allowing for precise tracking.

What is the primary benefit of the Multi-AI pipeline?

It ensures code quality and architectural consistency by requiring two different AI models to approve plans and implementations before they are finalized.

Can I customize the review standards?

Yes, the skill references a standards.md file within the skill directory where you can define specific coding criteria and architectural rules for your project.

Multi-AI Pipeline Orchestrator

Name: Multi-AI Pipeline Orchestrator
Author: cskwork

bycskwork

0•

Productivity & Workflow

Automates a structured multi-agent development workflow through sequential planning, implementation, and multi-model code reviews.

The Multi-AI Pipeline Orchestrator transforms Claude into a sophisticated project manager and lead developer by implementing a rigorous plan-review-implement-review cycle. By leveraging specialized roles—using Claude for planning and coding while utilizing Codex for objective reviewing—this skill ensures all technical approaches and code changes meet predefined project standards before being finalized. It maintains a persistent state using a dedicated .task directory, providing a robust and repeatable process for complex engineering tasks that require high accuracy and multi-step validation.

Key Features

010 GitHub stars

02Structured multi-stage pipeline: Plan, Refine, Implement, and Review

03State-driven task management via localized JSON tracking files

04Automated enforcement of project-specific coding standards

05Dual-AI verification using both Sonnet and Codex for objective validation

06Iterative fix-and-retry logic for both planning and implementation phases

Use Cases

01Standardizing coding practices across a repository by enforcing a common review-gate pipeline

02Executing complex feature requests that require thorough architectural planning and multi-pass reviews

03Reducing technical debt by catching implementation errors through automated cross-model validation

Key Features

010 GitHub stars

02Structured multi-stage pipeline: Plan, Refine, Implement, and Review

03State-driven task management via localized JSON tracking files

04Automated enforcement of project-specific coding standards

05Dual-AI verification using both Sonnet and Codex for objective validation

06Iterative fix-and-retry logic for both planning and implementation phases

Use Cases

01Standardizing coding practices across a repository by enforcing a common review-gate pipeline

02Executing complex feature requests that require thorough architectural planning and multi-pass reviews

03Reducing technical debt by catching implementation errors through automated cross-model validation