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Cross Eval DOCX

Runs a high-stakes business memo through multiple AI models (Claude, Codex, Gemini) to generate independent reviews, reconcile divergences, and produce a consensus vote (GO/PAUSE/STOP) with identified risks and supports.

2 uses this weeksource alirezarezvanilicence MIT
Quality gate: FAIL · better answers · measured 2026-10-23
Blind-judged against the bare model on 2 requests: average lift -9.5 points (specificity, structure, method, fit).
won 0/2, lift -9.5, tokens ×276.82
Security scan: clean · 2026-10-24 · 1 files · ran in sandbox
no network calls, shell tricks, credential access or instruction tampering found in its files.

Try asking

Say it in your own words; the assistant finds this recipe by itself. Examples:

“Kør en cross-eval på vedhæftede term sheet til vores næste runde, før jeg sender den videre.”
“Jeg har brug for en uafhængig tjek på memoet om vores pivot til B2B. Bestyrelsen var uenige sidst.”
“Gennemgå udkastet til opsigelsesvarslerne. Vil modellerne flagge store risici, før vi går i produktion?”
“Run a cross-eval on the attached term sheet for the Series B fundraise before I send it to the investors.”
“I need a multi-model sanity check on this memo regarding the pivot to enterprise SaaS. The board was split last week.”
“Please review the layoff announcement draft. I want to know if any model flags significant legal or morale risks before we finalize.”
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Good for

multi-modelconsensusboard-memorisk-assessmentstrategy-reviewc-suitedecision-supportcross-validation

Not for

Do not use this for routine operational updates, low-stakes internal communications, or creative brainstorming sessions. It is not a substitute for legal counsel or financial audit services. For simple code reviews or technical architecture checks, use a dedicated code-review skill instead.

Details

When it is the right choice

Use this skill when a critical, irreversible business decision (like a pivot, M&A, or major fundraise) requires an independent sanity check before finalizing. It is specifically designed for board memos or strategy briefs where single-model bias could lead to costly errors. It is appropriate when the cost of reversal is high, such as committing to a regulated market or announcing layoffs.

The full recipe the assistant reads

/c-level-agents:cross-eval — Multi-Model Consensus

Command: /c-level-agents:cross-eval <memo-or-brief>

Runs the same memo through multiple model providers and reconciles divergences. Use for high-stakes, irreversible decisions where single-model bias is too costly: M&A, major fundraises, layoffs, strategic pivots, regulatory commitments.

Adapted from gstack's /codex cross-review pattern, generalized to business memos instead of code PRs.

When to Run

  • Before signing a term sheet
  • Before announcing a layoff
  • Before committing to a regulated market
  • Before any decision where reversing costs > 6 months of company time
  • When the boardroom vote was split or had a CRITICAL dissent

Models Used (graceful degradation)

The command tries to invoke each available model in order:

  1. Claude (primary, always available) — the boardroom's native voice
  2. Codex / OpenAI (if OPENAI_API_KEY or codex CLI available)
  3. Gemini (if GEMINI_API_KEY or gemini CLI available)

If only Claude is available, the command runs Claude-only with adversarial mode — same model, different prompt seeds — and clearly labels the output as single-model.

Workflow

  1. Read the memo / brief
  2. Probe environment for available model CLIs / API keys
  3. For each available model:
  4. Send the memo with this prompt prefix: > "You are an independent C-suite reviewer. The following is a board memo from another company's boardroom. Identify the top 3 concerns, the top 3 supports, and your vote (APPROVE / REJECT / DEFER). Do not deferentially agree — assume the memo's reasoning is flawed until proven otherwise."
  5. Collect three independent reviews
  6. Reconcile: where do they agree? Where do they diverge?
  7. Surface the divergences as questions for the founder

Output Format

Saved to ~/.claude/cross-eval/YYYY-MM-DD-<slug>.md:

# Cross-Eval: <memo title>
**Date:** YYYY-MM-DD
**Memo reviewed:** <link>
**Models invoked:** Claude / Codex / Gemini (or noted fallbacks)

## Vote Tally
| Model | Vote | Confidence |
|---|---|---|
| Claude | APPROVE | High |
| Codex | DEFER | Med |
| Gemini | APPROVE | Low |

## Consensus Concerns (≥2 models flagged)
1. <concern> — flagged by Claude + Codex
2. <concern> — flagged by all 3

## Divergent Concerns (1 model flagged)
- <Codex only:> <concern> — worth a second look
- <Gemini only:> <concern> — likely noise, but check

## Consensus Supports (≥2 models endorsed)
1. <support>
2. <support>

## Recommendation
- 🟢 GO if 2+ models APPROVE and no CRITICAL concerns from any model
- 🟡 PAUSE if any model is DEFER or any concern is CRITICAL
- 🔴 STOP if 2+ models REJECT

## Open Questions for Founder
1. <question raised by divergence>
2. <question raised by divergence>

Why This Matters

Single-model recommendations have systematic biases. Claude trends helpful and may under-weight risk. Codex (OpenAI) trends more cautious on emerging-market and regulatory topics. Gemini trends more cautious on technical scale claims. Disagreement is signal, not noise.

This is the safety net before irreversibility — not a replacement for outside counsel or a real board.

Graceful Degradation

If only Claude is available:

**Models available:** Claude only
**Mode:** ADVERSARIAL — running 3 independent Claude passes with different system prompts:
  1. Standard reviewer
  2. Devil's advocate (must find 3 critical concerns)
  3. Steelman (must find 3 strongest reasons to approve)

This is weaker than true multi-model. Treat the result as suggestive, not conclusive.

Routing

  • /c-level-agents:decide — if consensus is GO
  • /c-level-agents:freeze — if consensus is PAUSE
  • /c-level-agents:boardroom (re-run) — if consensus is STOP

Related


Version: 1.0.0

Files in this skill (2)
  • LICENSE
  • SKILL.md
Where it comes from

Vendored from alirezarezvani/claude-skills, commit 27cfcd0fb0a4, path c-level-agents/skills/cross-eval, licence MIT. The upstream licence file ships inside the skill.