ccg — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited ccg (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Route a task through three AI models in parallel, then synthesize their outputs into one unified answer.
/omg-autopilot/teamnpm install -g @openai/codexnpm install -g @google/gemini-cliSplit the user request into:
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)
])Gather outputs from both advisors.
Return one unified answer with:
| Scenario | Action |
|---|---|
| One provider unavailable | Continue with available + Claude synthesis |
| Both unavailable | Fall back to Claude-only answer |
/ccg Review this PR - architecture/security via Codex and UX/readability via GeminiOutput:
=== 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~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.