Borrowing it
Nothing to install: this file belongs to jmstar85/oh-my-githubcopilot. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jmstar85/oh-my-githubcopilot/main/.github/skills/ccg/SKILL.mdgit clone --depth 1 https://github.com/jmstar85/oh-my-githubcopilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jmstar85/oh-my-githubcopilot/ccg)<a href="https://agentmods.dev/skills/jmstar85/oh-my-githubcopilot/ccg"><img src="https://agentmods.dev/badge/skills/jmstar85/oh-my-githubcopilot/ccg/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jmstar85/oh-my-githubcopilot/ccg"><img src="https://agentmods.dev/badge/skills/jmstar85/oh-my-githubcopilot/ccg.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.00615 |
| Opus 5.5 | $0.00019 | $0.00246 |
| Sonnet 5.5 | $0.00010 | $0.00123 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
ccg scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ccg — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 101 lines · 48 tokens per session scan A 6439319f98d5
ccg is a skill published in the GitHub repository jmstar85/oh-my-githubcopilot (162 stars, last pushed 5mo ago), licensed MIT. It adds 48 tokens to every session and 615 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ccg, differing in 0 lines, and is treated as a copy.
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