mcpField
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ccg

ccg

github.com/zereightAgent workflows2.1k57 installsUpdated today
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About ccg

CCG sends a task to Codex and Gemini in parallel, then synthesizes their responses into one answer for multi-perspective review and cross-validation. If a CLI is unavailable, it can proceed with the available provider or fall back to Claude alone.

Key features

  1. 01Invoke Codex and Gemini in parallel
  2. 02Split prompts by analysis focus
  3. 03Synthesize agreements and conflicts
  4. 04Fall back when a provider is unavailable

Use cases

  1. 01Review code from multiple perspectives
  2. 02Cross-check analysis and recommendations
  3. 03Address backend and UI work in one request

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/zereight/gitlab-mcp --skill ccg

After installing, type/ccgin Claude Code to confirm it loaded.

Details

From the project README

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

ScenarioAction
One provider unavailableContinue with available + Claude synthesis
Both unavailableFall 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

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