multi-ai

Coordinate two AI reviewers to automate code review workflows.

Updated Jan 16, 2026
One-click install
npx skills add https://github.com/cskwork/llm-review --skill multi-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: multi-ai
Source: https://github.com/cskwork/llm-review/tree/main/skill/multi-ai
Command: npx skills add https://github.com/cskwork/llm-review --skill multi-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes reference (resource) components.

What problem does it solve?

Coordinates a two-AI code-review pipeline to ensure thorough, reproducible software evaluations across teams.

Core Features & Use Cases

  • Sequential two-AI review workflow (Sonnet then Codex) with planning, refinement, implementation, and validation.
  • Stateful task tracking across stages using .task artifacts to enable restart and auditing.
  • Audit-friendly outputs and deterministic execution suitable for compliance and team governance.

Quick Start

Provide a user request to start the two-AI pipeline and guide the system through planning, review, implementation, and completion.

Frequently Asked Questions about multi-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate a code review pipeline with multiple AI reviewers?

Automate a code review pipeline by coordinating two independent AI reviewers sequentially. The system enforces deterministic steps for planning, iterative reviews, implementation, and validation across multiple files, ensuring reproducible software evaluations.

What is a two-AI review workflow and how does it work?

A two-AI review workflow sequentially coordinates independent AI models to evaluate code. It progresses through structured planning, refinement, automated implementation, and validation, enforcing deterministic execution and state tracking via .task artifacts for auditability.

How do I track state across multiple stages of an automated code review?

Track state across automated code review stages using .task artifacts. These artifacts enable stateful tracking throughout the planning, review, implementation, and completion phases, supporting workflow restarts and compliance auditing.

Can I use automated AI code review for compliance and team governance?

Use automated AI code review for compliance and team governance because it produces audit-friendly outputs. The pipeline enforces deterministic execution and standardized outputs across planning, reviews, implementation, and completion stages for reproducible evaluations.

What is the best way to orchestrate iterative code reviews across multiple files?

Orchestrate iterative code reviews across multiple files by automating an end-to-end workflow. A coordinated pipeline applies structured plans, executes sequential reviews, and performs automated implementation and verification to ensure thorough software evaluations.