gsd-review

Orchestrate cross-AI peer reviews of project phase plans via external CLI tools.

1|Updated Apr 4, 2026
One-click install
npx skills add https://github.com/nnexai/git-stacks --skill gsd-review-nnexai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gsd-review
Source: https://github.com/nnexai/git-stacks/tree/main/.codex/skills/gsd-review
Command: npx skills add https://github.com/nnexai/git-stacks --skill gsd-review-nnexai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the lack of objective validation in AI-generated project plans by orchestrating multi-model peer reviews to identify gaps, risks, and logical inconsistencies before execution.

Core Features & Use Cases

  • Multi-CLI Orchestration: Automatically invokes various AI agents (Gemini, Claude, Codex, Qwen, etc.) to critique phase plans.
  • Structured Feedback: Aggregates diverse AI perspectives into a unified REVIEWS.md file for actionable planning.
  • Use Case: When developing a complex software architecture, use this skill to have multiple specialized AI models review your proposed phase plan to ensure technical feasibility and security compliance.

Quick Start

Invoke the gsd-review skill with the --all flag to trigger a comprehensive peer review of the current phase plan across all available AI models.

Frequently Asked Questions about gsd-review

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

FAQPage Schema
How do I run peer code review on AI-generated phase plans using multiple models?▼

To run peer code review on AI-generated phase plans, invoke the gsd-review skill with the --all flag, which orchestrates multi-agent execution across various AI CLI tools to validate technical workflows. It automatically aggregates diverse model feedback into a unified report.

What is multi-agent orchestration for collaborative validation of project workflows?▼

Multi-agent orchestration for collaborative validation involves invoking multiple external AI CLI tools to critique project phase plans. This mechanism aggregates diverse AI perspectives into a structured REVIEWS.md file to identify gaps, risks, and logical inconsistencies before execution.

Can I use multiple AI agents like Gemini and Claude to validate software architecture feasibility?▼

Yes, you can use multiple AI agents like Gemini and Claude to validate software architecture feasibility. The skill orchestrates cross-AI peer reviews by invoking specialized models to ensure technical feasibility and security compliance for complex phase plans.

Do I need specific AI CLI environments to aggregate feedback from diverse models?▼

Yes, you need specific AI CLI environments installed to aggregate feedback from diverse models. The multi-CLI orchestration requires integration with these environments and adherence to defined agent-spawning protocols to execute multi-model reviews successfully.

What is the best way to identify logical inconsistencies in technical workflows before execution?▼

The best way to identify logical inconsistencies in technical workflows before execution is cross-AI peer review. This approach automatically invokes various AI agents to critique phase plans, aggregating objective feedback into a structured REVIEWS.md file for actionable planning.

Why does multi-model code review require defined agent-spawning protocols?▼

Multi-model code review requires defined agent-spawning protocols to safely facilitate collaborative validation across different external AI CLI tools. These protocols ensure the multi-agent orchestration correctly invokes targets and aggregates structured feedback without execution conflicts.