ccg

Orchestrates parallel Codex and Gemini advisor prompts into a single synthesized answer.

Updated Apr 11, 2026
One-click install
npx skills add https://github.com/Pryma-Tech/iris --skill ccg-pryma-tech
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ccg
Source: https://github.com/Pryma-Tech/iris/tree/main/cli/skills/ccg
Command: npx skills add https://github.com/Pryma-Tech/iris --skill ccg-pryma-tech

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinates multiple AI advisors (Codex and Gemini) within a single user request and synthesizes their outputs into one coherent answer.

Core Features & Use Cases

  • Parallel advisor routing: runs Codex and Gemini prompts in parallel and combines results.
  • Unified synthesis: Claude integrates the outputs into a single recommended plan or decision.
  • Use Case: Rapid architecture/code review with cross-validated perspectives in a single interaction.

Quick Start

Provide a task description and invoke the skill to run Codex and Gemini prompts and synthesize a final answer.

Frequently Asked Questions about ccg

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

FAQPage Schema
How do I orchestrate multi-agent AI prompts for parallel code review?

Multi-agent AI orchestration runs Codex and Gemini prompts in parallel and synthesizes their outputs into a single, coherent architecture recommendation or code review decision.

What is parallel advisor synthesis for backend architecture assessments?

Parallel advisor synthesis routes a single user request to multiple AI models simultaneously, integrating their cross-validated perspectives into one unified backend architecture review.

Do I need Codex and Gemini CLIs installed to run multi-agent prompt orchestration?

Yes, multi-agent prompt orchestration requires both the Codex and Gemini CLIs installed via npm to execute parallel advisor prompts and synthesize the combined output.

Can I use parallel AI advisors for UX and documentation feedback?

Yes, parallel AI advisors support multi-perspective code quality assessments, UX feedback, and documentation reviews by routing a single request to multiple models and synthesizing the results.

What's the best way to get cross-validated perspectives from multiple AI models in one response?

The best way to get cross-validated perspectives is running parallel AI advisors that synthesize multiple model outputs into a single coherent recommended plan or decision within one interaction.