codex

When to invoke

Use when asked to "codex review", "codex challenge", "ask codex", "second opinion", or "consult codex".

Preamble

eval "$("${VIBESTACK_HOME:-$HOME/.vibestack}/bin/vibe-slug" 2>/dev/null)" 2>/dev/null || SLUG="unknown"
_LEARN_FILE="${VIBESTACK_HOME:-$HOME/.vibestack}/projects/${SLUG:-unknown}/learnings.jsonl"
if [ -f "$_LEARN_FILE" ]; then
  _LEARN_COUNT=$(wc -l < "$_LEARN_FILE" 2>/dev/null | tr -d ' ')
  echo "LEARNINGS: $_LEARN_COUNT entries loaded"
  if [ "$_LEARN_COUNT" -gt 5 ] 2>/dev/null; then
    "${VIBESTACK_HOME:-$HOME/.vibestack}/bin/vibe-learnings-search" --limit 5 2>/dev/null || true
  fi
else
  echo "LEARNINGS: none yet"
fi

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{{include lib/snippets/decision-brief.md}}

{{include lib/snippets/working-protocols.md}}

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/codex — Multi-AI Second Opinion

You are running the /codex skill. This wraps the OpenAI Codex CLI to get an independent, brutally honest second opinion from a different AI system.

Codex is a direct, terse, technically precise reviewer — challenges assumptions, catches things you might miss. Present its output faithfully, not summarized.

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Step 0.4: Check codex binary

CODEX_BIN=$(command -v codex 2>/dev/null || echo "")
[ -z "$CODEX_BIN" ] && echo "NOT_FOUND" || echo "FOUND: $CODEX_BIN"

If NOT_FOUND: stop and tell the user: "Codex CLI not found. Install it: npm install -g @openai/codex or see https://github.com/openai/codex"

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Step 0.5: Nesting probe + usability probe + version check

Before building expensive prompts, verify this session is not already running inside Codex, that Codex actually answers, and that the installed CLI version isn't in the known-bad list.

Running-under-Codex probe. A live Codex session exports CODEX_THREAD_ID and CODEX_SANDBOX into every shell it spawns, so this block can tell that the host IS Codex. vibestack ships Codex as a first-class runtime, which makes that a normal case rather than an exotic one — and the entire value of this skill is a second opinion from a different model. Spawning codex exec from inside Codex is the same model reviewing its own work, at full token cost and with zero cross-model signal.