Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bestagentkits/agency-skills --skill cross-evalgit clone --depth 1 https://github.com/bestagentkits/agency-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bestagentkits/agency-skills/cross-eval)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/cross-eval"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/cross-eval/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/cross-eval"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/cross-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.01080 |
| Opus 5 | $0.00034 | $0.00540 |
| Sonnet 5 | $0.00013 | $0.00216 |
| Haiku 4.5 | $0.00007 | $0.00108 |
Grade A, and why
cross-eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cs:cross-eval — Multi-Model Consensus
Command: /cs: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:
- Claude (primary, always available) — the boardroom's native voice
- Codex / OpenAI (if
OPENAI_API_KEYorcodexCLI available) - Gemini (if
GEMINI_API_KEYorgeminiCLI 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
- Read the memo / brief
- Probe environment for available model CLIs / API keys
- For each available model:
- 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."
- Send the memo with this prompt prefix:
- Collect three independent reviews
- Reconcile: where do they agree? Where do they diverge?
- 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>
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 116 lines · 67 tokens per session scan A 77669ec195f1
cross-eval is a skill published in the GitHub repository bestagentkits/agency-skills (12 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 1,080 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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