用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/tomevault-io/tomes --skill cross-modal-review命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
> Use when this capability is needed.
Use when writing kernel, account, or note MASM code that reads from or writes to the advice provider (advice stack / advice map) — validate advice data.
Use when writing a Rust test that exercises a failure path or a MASM test that expects a `panic` / `assert` — assert on the specific expected error variant or error code.
基于 SOC 职业分类
正在显示 SKILL.md
| name | cross-modal-review |
| description | | Use when this capability is needed. |
Convention: see conventions/cross-modal.yaml for the review pairs and refusal routing chain.
Relationship to
gbrain eval cross-modal: This skill is the manual mid-flow gate (one model reviews work product before commit, with refusal routing). Thegbrain eval cross-modalcommand (v0.27.x) is a sibling surface: 3 different-provider frontier models score-and-iterate on a documented dimension list before tests cement behavior. Use this skill for ad-hoc second opinions; usegbrain eval cross-modalfor the skillify Phase 3 quality gate. The two are complementary, not redundant.
This skill guarantees:
Invoke this skill when:
Do NOT invoke for:
For diff review specifically, gstack ships a /codex skill that wraps
the OpenAI Codex CLI. Two modes:
Independent diff review from a different AI system. The user invokes
/codex review (gstack-shipped); cross-modal-review's job is to
RECOGNIZE when this is the right tool and recommend it explicitly.
When to recommend /codex review:
Output framing (when cross-modal-review surfaces Codex output):
CODEX REVIEW (independent second opinion):
══════════════════════════════════════════
<full codex output, verbatim>
══════════════════════════════════════════
CROSS-MODEL ANALYSIS:
Both found: [overlapping findings]
Only Codex: [findings unique to Codex]
Only Claude: [findings unique to my analysis]
Agreement: X% (N/M findings overlap)
User decides what to act on. Cross-model agreement is signal, not permission.
Same shape, different prompt. Used on security-sensitive changes: the reviewer is asked to find injection vectors, race conditions, auth bypasses, data leaks, privilege escalation paths.
Output adds an exploitability rating (CRITICAL / HIGH / MEDIUM / LOW) and recommended mitigations.
If the primary review model refuses:
conventions/cross-modal.yaml).Cross-Modal Review
==================
Reviewer: {model name}
Contract: {originating skill}
Verdict: PASS | ISSUES FOUND
Findings:
- {finding with evidence}
Agreement with primary: {X}%
Cross-Modal Review (code)
==========================
Mode: Codex Review | Adversarial Challenge
Files changed: N
Lines changed: +N / -N
{mode-specific output above}
Reviewer findings are INFORMATIONAL until the user explicitly approves each one. Do NOT incorporate reviewer recommendations into the work product without presenting each finding and getting explicit approval. This applies even when the reviewer is correct. Cross-model consensus is a strong signal — present it as such — but the user makes the decision.
/codex — the actual Codex CLI wrapper this skill hands off
to for diff-review mode. Cross-modal-review knows WHEN to invoke;
/codex knows HOW.references/testing/SKILL.md — runs the project test suite; complementary
signal for "is this commit safe to land"references/conventions/cross-modal.yaml — review pairs + refusal routingThe skill's output shape is documented inline in the body sections above (see "Output", "Brain page format", or equivalent). The literal section header here exists for the conformance test (test/skills-conformance.test.ts).
Source: beyonai/ByClaw — distributed by TomeVault.