pietz Peer Review
Query multiple LLM CLIs in parallel and collect their responses.
Available CLIs
All three should be invoked via the Bash tool as background tasks, run in parallel:
- Claude:
unset CLAUDECODE && claude --model opus -p "$PROMPT" - Codex:
codex exec -m "gpt-5.5" --skip-git-repo-check "$PROMPT" - Gemini:
gemini -m "gemini-3.1-pro-preview" -p "$PROMPT"
Self-invocation rule
The orchestrating agent must NOT call its own CLI via Bash — it will fail or produce empty output. Use a subagent for your own model's contribution and Bash CLI commands for the other models.
Workflow
- Craft a clear, self-contained prompt. The CLIs have no history — fully re-brief them each time.
- Identify which agent you are (the orchestrator) so you know which CLI to skip.
- Check which of the remaining CLIs are installed (
which claude codex gemini). - Launch the other CLIs as parallel background Bash tasks and your own model as a subagent, all in parallel.
- Collect results from all models.
- Report transparently: before synthesizing, tell the user which models responded, which failed or timed out, and any issues encountered.
- Present each model's response in its own section with clear attribution.
- Provide a unified synthesis highlighting where the models agree, disagree, and what unique insights each contributed.
Guidelines
- Think about how much you want to reveal about your own opinion when crafting the prompt.
- If a CLI fails or times out, report it and continue with the results you have.
- Responses will arrive at different speeds. As each result comes in, consider giving the user a brief update or early summary rather than waiting silently for all models to finish. The final synthesis still needs all responses, but intermediate progress keeps the user informed.