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 protect-my-hair/nucleus-marketplace --skill multi-reviewgit clone --depth 1 https://github.com/protect-my-hair/nucleus-marketplaceWrote 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/protect-my-hair/nucleus-marketplace/multi-review)<a href="https://agentmods.dev/skills/protect-my-hair/nucleus-marketplace/multi-review"><img src="https://agentmods.dev/badge/skills/protect-my-hair/nucleus-marketplace/multi-review/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/protect-my-hair/nucleus-marketplace/multi-review"><img src="https://agentmods.dev/badge/skills/protect-my-hair/nucleus-marketplace/multi-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00030 | $0.00866 |
| Opus 5 | $0.00015 | $0.00433 |
| Sonnet 5 | $0.00006 | $0.00173 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
multi-review 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 13d 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.
What it actually says
多角色独立评审
核心原则
评审报告只能记录审查发现，不能制造人工批准、PMS 状态、PR 审查通过、合入或发布事实。
评审领域
按产物类型读取对应 reference：
- 需求分析：
references/requirement-analysis-review.md - 方案设计：
references/solution-design-review.md - 需求分解：
references/requirement-decomposition-review.md - 特性设计：
references/feature-design-review.md - 实施计划：
references/implementation-plan-review.md - 代码：
references/code-review.md - 测试资产：
references/test-asset-review.md - 交付证据：
references/delivery-evidence-review.md - 特性完成：
references/feature-completion-review.md
无法确定领域时，先读上下文并提出一个明确问题，不得默认用代码评审代替设计评审。
客观评审纪律
方案一致性评审不是机械要求实现逐字贴合原方案。评审者必须判断：
- 实现是否满足特性设计和实施计划承诺的能力、边界、接口、数据、权限和验证要求。
- 实现差异是必要改进、等价实现、计划外扩 scope，还是破坏原方案约束。
- 如果实现明显更简单、更稳健或更符合现有架构，且没有越界或遗漏，应保留实现，并记录“实现优于原方案”的证据和后续是否需要同步设计 / 计划。
- 如果实现改变了用户可见能力、数据 / 接口 / 权限边界、外部依赖或风险假设，必须阻塞并要求回到计划或特性设计评审节点，不能由 reviewer 直接批准扩 scope。
输出纪律
评审必须区分：
blocking：阻塞下游任务。important：进入下游前应处理。suggestion：可记录但不阻塞。unverifiable：当前证据无法判断，需要人工或外部系统确认。
特性开发完成后的评审必须同时覆盖：
- code-review。
- 方案一致性评审。
- 计划满足度评审。
向用户呈现评审结果时，按 _shared/references/interaction-format.md 的确认型格式，第一句先给结论（通过 / 有阻塞 / 需整改），再展开 findings。
子代理预审
所有人工审查 / 人工评审 / 人工复核节点在提交给人之前，必须先按 _shared/references/subagent-precheck-protocol.md 调度独立 reviewer 子代理完成预审，并取得“材料可提交人工审查”结论；子代理 prompt 基于 references/subagent-review-prompt.md 组织。
multi-review 自身的 review report 仍只记录发现、风险、整改建议和复核证据，不能替代人工审批、PMS 状态、PR 审查通过、合入或发布事实。
Runtime 边界
scripts/multi_review.py 只校验 review report schema 和语义，不创建评审结论、不替人审查、不改代码。
What ships with it
13 files 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.
- agents/openai.yaml 542 B
- assets/nucleus-multi-review-report.schema.json 5.5 KB
- references/code-review.md 1.7 KB
- references/delivery-evidence-review.md 522 B
- references/feature-completion-review.md 3.1 KB
- references/feature-design-review.md 1.1 KB
- references/implementation-plan-review.md 3.5 KB
- references/requirement-analysis-review.md 4.5 KB
- references/requirement-decomposition-review.md 2.4 KB
- references/solution-design-review.md 959 B
- references/subagent-review-prompt.md 3.1 KB
- references/test-asset-review.md 437 B
- scripts/multi_review.py 4.1 KB runs code
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.
- 13d ago First seen · 71 lines · 30 tokens per session scan A b39dd13f4070
multi-review is a skill published in the GitHub repository protect-my-hair/nucleus-marketplace (162 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 866 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…