Uses three AI models to analyze tasks and synthesize a unified, cross-validated result.
Install
mkdir -p .claude/skills/ccg-zereight && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18718" && unzip -o skill.zip -d .claude/skills/ccg-zereight && rm skill.zipInstalls to .claude/skills/ccg-zereight
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Route tasks through Claude, Codex, and Gemini in parallel
- →Synthesize outputs from multiple AI models
- →Identify agreed and conflicting recommendations
- →Cross-validate technical decisions
- →Generate action checklists from synthesized feedback
How it works
The skill decomposes a request into prompts for Codex and Gemini, runs them in parallel, collects their results, and then synthesizes the outputs into a single answer.
Inputs & outputs
When to use ccg
- →Compare architectural approaches
- →Get multi-perspective code reviews
- →Cross-validate technical decisions
- →Synthesize feedback from multiple models
About this skill
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
When not to use it
- →For simple, straightforward tasks
- →When the approach is already clear
- →When coordinated multi-agent work is needed
Prerequisites
Limitations
- →Requires Codex CLI to be available
- →Requires Gemini CLI to be available
- →Falls back to Claude-only if both are unavailable
How it compares
This skill provides multi-perspective analysis by orchestrating three AI models in parallel and synthesizing their responses, unlike using a single model.
Compared to similar skills
ccg side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| ccg (this skill) | 0 | 3mo | Review | Intermediate |
| software-architecture | 333 | 6mo | No flags | Intermediate |
| codex | 32 | 2mo | Review | Advanced |
| game-development | 70 | 6mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by zereight
View all by zereight →You might also like
software-architecture
davila7
Guide for quality focused software architecture. This skill should be used when users want to write code, design architecture, analyze code, in any case that relates to software development.
codex
Lucklyric
Invoke Codex CLI for complex coding tasks requiring high reasoning capabilities. This skill should be invoked when users explicitly mention "Codex", request complex implementation challenges, advanced reasoning, or need high-reasoning model assistance. Automatically triggers on codex-related requests and supports session continuation for iterative development.
game-development
davila7
Game development orchestrator. Routes to platform-specific skills based on project needs.
jupyter-notebook
davila7
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
senior-fullstack
davila7
Comprehensive fullstack development skill for building complete web applications with React, Next.js, Node.js, GraphQL, and PostgreSQL. Includes project scaffolding, code quality analysis, architecture patterns, and complete tech stack guidance. Use when building new projects, analyzing code quality, implementing design patterns, or setting up development workflows.
cursor-explorer-mcp
sepiabrown
Use for token-expensive operations requiring multi-file analysis - codebase exploration, broad searches, architecture understanding, tracing flows, finding implementations across files. Uses MCP cursor-agent server (company pays) with clean async interface. Do NOT use for single-file analysis, explaining code already in immediate context, or pure reasoning tasks.