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

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.

Skill2k starsChanged 6 months ago
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What's in it

  1. CCG - Claude-Codex-Gemini Tri-Model Orchestration
  2. When to Use
  3. When NOT to Use
  4. Requirements
  5. Execution Protocol
  6. 1. Decompose Request
  7. 2. Invoke Advisors
  8. 3. Collect Results
  9. 4. Synthesize
  10. Fallbacks
  11. Example
---
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.
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:

```bash
# 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
```

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