Agent Skills › zereight/gitlab-mcp › ccg

ccg

GitHub

通过并行调用Codex和Gemini模型，从架构、后端及UX等多视角分析任务，最终综合输出统一建议。适用于代码审查、跨领域分析及需要多视角验证的场景。

.github/skills/ccg/SKILL.md zereight/gitlab-mcp

Trigger Scenarios

ccg tri-model three models multi-model cross-validate get multiple opinions compare models

Install

npx skills add zereight/gitlab-mcp --skill ccg -g -y

More Options

Non-standard path

npx skills add https://github.com/zereight/gitlab-mcp/tree/main/.github/skills/ccg -g -y

Use without installing

npx skills use zereight/gitlab-mcp@ccg

指定 Agent (Claude Code)

npx skills add zereight/gitlab-mcp --skill ccg -a claude-code -g -y

安装 repo 全部 skill

npx skills add zereight/gitlab-mcp --all -g -y

预览 repo 内 skill

npx skills add zereight/gitlab-mcp --list

SKILL.md

Frontmatter

{
    "name": "ccg",
    "description": "Claude-Codex-Gemini tri-model orchestration for multi-perspective analysis. Activate when user says: ccg, tri-model, three models, multi-model, cross-validate, get multiple opinions, compare models.\n",
    "argument-hint": "<task description>"
}

CCG - Claude-Codex-Gemini Tri-Model Orchestration

Route a task through three AI models in parallel, then synthesize their outputs into one unified answer.

When to Use

Backend/analysis + frontend/UI work in one request
Code review from multiple perspectives
Cross-validation where models may disagree
Fast parallel input without full team orchestration

When NOT to Use

Simple, straightforward tasks → execute directly
Already clear on approach → use /omg-autopilot
Need coordinated multi-agent work → use /team

Requirements

Codex CLI: npm install -g @openai/codex
Gemini CLI: npm install -g @google/gemini-cli
If either CLI is unavailable, continue with whichever provider works

Execution Protocol

1. Decompose Request

Split the user request into:

Codex prompt: architecture, correctness, backend, risks, test strategy
Gemini prompt: UX/content clarity, alternatives, edge-case usability, docs polish
Synthesis plan: how to reconcile conflicts

2. Invoke Advisors

Run both advisors via CLI in parallel:

# Run in terminal
codex "<codex prompt>"
gemini "<gemini prompt>"

Or via VS Code's selectChatModels() API if available:

Promise.all([
  model_openai.sendRequest(codex_prompt),
  model_google.sendRequest(gemini_prompt)
])

3. Collect Results

Gather outputs from both advisors.

4. Synthesize

Return one unified answer with:

Agreed recommendations
Conflicting recommendations (explicitly called out)
Chosen final direction + rationale
Action checklist

Fallbacks

Scenario	Action
One provider unavailable	Continue with available + Claude synthesis
Both unavailable	Fall back to Claude-only answer

Example

/ccg Review this PR - architecture/security via Codex and UX/readability via Gemini

Output:

=== CCG Synthesis ===

## Agreed
- Authentication middleware needs rate limiting
- Error messages should be more user-friendly

## Conflicting
- Codex: Use middleware pattern for validation
- Gemini: Use inline validation for simplicity
→ Chosen: Middleware pattern (consistency with existing codebase)

## Action Checklist
- [ ] Add rate limiting middleware
- [ ] Improve error messages in auth flow
- [ ] Extract validation to middleware layer