원클릭으로
colleague
Consult Codex as a second local AI coding agent for a one-shot peer review when explicitly requested.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
메뉴
Consult Codex as a second local AI coding agent for a one-shot peer review when explicitly requested.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | colleague |
| description | Consult Codex as a second local AI coding agent for a one-shot peer review when explicitly requested. |
| disable-model-invocation | true |
| argument-hint | [question] |
Use this skill only when the user explicitly asks to "ask Codex", "ask a colleague", or "get a second opinion". It gets a one-shot second opinion from Codex without handing control of the repo to it.
Create a private, unpredictable temp file with mktemp, and clean it up after use:
ctx="$(mktemp -t colleague-ctx.XXXXXX.md)"
trap 'rm -f "$ctx"' EXIT
Write a compact briefing into $ctx. Include only what Codex needs:
Do not dump the raw conversation transcript, tool logs, .env files, API keys, private URLs, tokens, or credentials. Summarize aggressively.
Ask Codex with the installed bridge:
colleague ask codex \
--context "$ctx" \
--question "What is the strongest critique of my current plan?"
Use --model <model> only when the user asks for a specific model. The bridge runs Codex non-interactively (codex exec) in a read-only sandbox by default for the one-shot consult.
Treat the response as peer advice, not ground truth. Compare it to your own analysis.
In your response to the user, say that you consulted Codex, then explain where you agree, disagree, or remain uncertain. Continue with the implementation or recommendation using your own judgment.
# Goal
# Relevant files and snippets
# What I tried
# Current error or dilemma
# Constraints / non-goals
Do not use it for simple questions you can answer directly. Do not use it to outsource user-private or secret material. Do not start a multi-turn agent-to-agent loop; if the answer misses, improve the briefing and ask once more.