/review

SkillDocs & knowledge

General cross-model review: the LLM independently reviews any research artifact, producing structured scores, wiki entity mappings, and improvement suggestions

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the /review skill

What this skill tells your AI

The instructions your AI receives, as published by lambenthan/empiricalwiki in .claude/skills/review/SKILL.md and read by ahel’s review.

对任意研究制品(idea、proposal、experiment plan、paper draft、claim)进行跨模型审查。 使用 Review LLM 作为独立审稿人,输出结构化评分、可操作的改进建议,以及与 wiki 实体的映射 (哪些 claims 需要加强,哪些 gaps 被发现)。 支持三种难度级别(standard / hard / adversarial)和四种审查焦点。 可独立使用,也被 /ideate、/refine、/exp-design 调用。

Inputs

  • artifact:要审查的制品,以下之一:
    • wiki 页面的 slug(如 sparse-lora-for-edge-devices,从 ideas/experiments/claims/ 中查找)
    • 文件路径(如 wiki/outputs/paper-draft-v1.md
    • 自由文本(直接粘贴的 proposal 或 idea 描述)
  • --difficulty(可选,默认 standard):
    • standard:单轮审查,给出结构化反馈
    • hard:多轮对话(最多 3 轮),Claude 对每个 weakness 进行 rebuttal
    • adversarial:多轮对话(最多 3 轮),Review LLM 额外尝试找致命缺陷,模拟最严苛的审稿人
  • --focus(可选,默认全面审查):
    • method:聚焦方法设计的正确性、创新性、可行性
    • evidence:聚焦证据是否充分、实验是否严谨、claim 是否 well-supported
    • writing:聚焦表达清晰度、结构组织、论证逻辑
    • completeness:聚焦是否遗漏关键内容(相关工作、ablation、baseline)

Outputs

  • Review Report(输出到终端):
    • Overall Score(1-10)
    • Strengths(优点列表)
    • Weaknesses(缺点列表,按严重程度排序)
    • Questions(审稿人的疑问)
    • Actionable Suggestions(可操作的改进建议,按优先级排序)
    • Wiki Entity Mapping(哪些 claims 需要加强,哪些 gaps 被发现)
    • Verdict:ready / needs-work / major-revision / rethink
  • --difficulty >= hard:额外包含多轮对话记录和最终修正后的评分
  • 该 skill 不直接修改 wiki,但会输出建议的 wiki 更新列表

Wiki Interaction

Reads

  • wiki/papers/*.md — 查找制品引用的论文,验证引用正确性
  • wiki/concepts/*.md — 理解制品涉及的技术概念
  • wiki/claims/*.md — 检查制品依赖的 claims 当前状态和 confidence
  • wiki/experiments/*.md — 查找相关实验结果
  • wiki/ideas/*.md — 如果审查的是 idea,检查其上下文
  • wiki/graph/context_brief.md — 获取全局上下文
  • wiki/graph/open_questions.md — 对照 gap map 检查完整性
  • .claude/skills/shared-references/cross-model-review.md — 审稿独立性原则

Writes

  • 。Review 是只读查询操作。
    • 审查结果输出到终端,由用户或调用方(如 /refine)决定是否应用。

Graph edges created

Workflow

前置:确认工作目录为 wiki 项目根(包含 wiki/raw/tools/ 的目录)。

Step 1: 加载上下文

  1. 解析 artifact

    • 若为 slug:按顺序在 wiki/ideas/wiki/experiments/wiki/claims/wiki/papers/wiki/outputs/ 中查找 {slug}.md
    • 若为文件路径:直接读取
    • 若为自由文本:直接使用
  2. 确定 artifact 类型:idea / experiment / claim / paper-draft / proposal / other

  3. 加载相关 wiki 上下文

    • 读取 wiki/graph/context_brief.md 获取全局视角
    • 读取 wiki/graph/open_questions.md 获取知识缺口列表
    • 根据 artifact 类型,加载相关 wiki 页面:
      • idea → 其 origin_gaps 对应的 claims,相关 papers
      • experiment → 其 target_claim,相关 experiments
      • claim → 其 evidence 来源,相关 papers 和 experiments
      • paper-draft → 其引用的所有 wiki 页面
  4. 读取 cross-model-review.md:确认 Review LLM 独立性原则

  5. 构建 reviewer system prompt(根据 --focus):

    Base prompt(所有 focus):

    You are a senior ML researcher reviewing a research artifact.
    Be thorough, specific, and constructive. For every weakness, suggest a concrete fix.
    Score on a 1-10 scale where:
    - 1-3: Fundamental flaws, not salvageable in current form
    - 4-5: Significant issues but core idea may have merit
    - 6-7: Solid work with clear areas for improvement
    - 8-9: Strong work, minor issues only
    - 10: Exceptional, publication-ready
    

    Focus-specific additions:

    • method:额外要求评估 technical correctness, novelty of approach, feasibility, comparison to alternatives
    • evidence:额外要求评估 experimental rigor, statistical significance, claim-evidence alignment, missing controls
    • writing:额外要求评估 clarity, logical flow, notation consistency, figure quality, related work coverage
    • completeness:额外要求评估 missing baselines, missing ablations, missing datasets, missing related work, reproducibility

    Adversarial addition(仅 adversarial 模式):

    Additionally: actively search for fatal flaws. A fatal flaw is anything that,
    if true, would make the entire contribution invalid (incorrect proof, data leakage,
    unfair comparison, published prior work). If you find one, flag it clearly.
    

Step 2: Review LLM 首轮审查

遵循 cross-model-review.md:不向 Review LLM 发送任何 Claude 的预判。

mcp__llm-review__chat:
  system: {reviewer system prompt from Step 1}
  message: |
    ## Artifact to Review
    {artifact full text}

    ## Context from Knowledge Base
    {relevant wiki context: related claims with status/confidence, related experiments, gap map entries}

    ## Review Instructions
    Please provide:
    1. **Strengths** (3-5 bullet points)
    2. **Weaknesses** (ranked by severity, each with a concrete suggestion to fix)
    3. **Questions** (things that are unclear or need clarification)
    4. **Score** (1-10 with one-sentence justification)
    5. **Verdict**: ready / needs-work / major-revision / rethink
    6. **Claim-level feedback**: For each claim referenced in the artifact, assess whether the evidence is sufficient. List any claims that need stronger support.
    7. **Knowledge gaps identified**: Any open questions or missing knowledge that would strengthen this work.

记录 Review LLM 返回的 threadId(用于 Step 3 多轮对话)。

Step 3: 多轮对话(hard / adversarial 模式)

--difficultystandard,跳过此步。

对 Review LLM 的每个 weakness 进行回应(最多 3 轮):

Round N(N = 1, 2, 3):

  1. Claude 分析 Review LLM 的 weaknesses,对每个 weakness 分类:

    • 可反驳(rebuttal):Claude 有充分理由或 wiki 证据反驳 → 写出 rebuttal
    • 承认(acknowledge):weakness 确实存在 → 承认并提出修复方案
    • 需要更多信息(clarify):weakness 基于误解 → 提供澄清
  2. 将 Claude 的回应发送给 Review LLM:

    mcp__llm-review__chat-reply:
      threadId: {from Step 2}
      message: |
        Thank you for the review. Here are my responses:
    
        {for each weakness: rebuttal / acknowledgment / clarification}
    
        Please re-evaluate considering these responses. Update your score if warranted.
        If --difficulty == adversarial: Also, please try harder to find any remaining
        fatal flaws I may have missed.
    
  3. Review LLM 回应新的评估和修正后的评分

  4. 若 Review LLM 的评分变化 < 0.5 且无新 weakness → 停止对话(收敛)

  5. 若已达 3 轮 → 停止对话

Step 4: 结构化输出

综合 Step 2 + Step 3 结果,生成结构化 Review Report:

# Review Report: {artifact title}

## Meta
- **Artifact type**: {idea / experiment / claim / paper-draft / proposal}
- **Difficulty**: {standard / hard / adversarial}
- **Focus**: {method / evidence / writing / completeness / 全面}
- **Reviewer**: Review LLM
- **Rounds**: {1 for standard, N for hard/adversarial}

## Score: {final score}/10 — {verdict}

| Verdict | 含义 |
|---------|------|
| ready | 可直接使用或提交 |
| needs-work | 有明确的改进点,修复后可用 |
| major-revision | 核心部分需要重大修改 |
| rethink | 基本方向可能有问题,需重新考虑 |

## Strengths
1. {strength 1}
2. {strength 2}
...

## Weaknesses (by severity)

### Critical
- {weakness}: {具体描述} → **Fix**: {具体修复建议}

### Major
- {weakness}: {具体描述} → **Fix**: {具体修复建议}

### Minor
- {weakness}: {具体描述} → **Fix**: {具体修复建议}

## Questions
1. {question}
...

## Wiki Entity Mapping

### Claims needing stronger support
| Claim | Current confidence | Issue | Suggested action |
|-------|-------------------|-------|------------------|
| [[claim-slug]] | 0.6 | Evidence is indirect | Run targeted experiment |

### Knowledge gaps identified
| Gap | Related to | Suggested action |
|-----|-----------|------------------|
| {描述} | [[slug]] | /ingest, /exp-run, or /query |

### Suggested wiki updates
- `wiki/claims/{slug}.md`: update confidence, add evidence note
- `wiki/ideas/{slug}.md`: add risk factor from review
- `wiki/graph/open_questions.md`: will be updated on next rebuild

## Dialogue History (hard/adversarial only)

### Round 1
**Review LLM**: {summary of initial review}
**Claude**: {summary of rebuttals/acknowledgments}

### Round 2
**Review LLM**: {updated assessment}
...

## Actionable Items (ranked)
1. [CRITICAL] {action item}
2. [MAJOR] {action item}
3. [MINOR] {action item}

Constraints

  • 审稿独立性:严格遵循 shared-references/cross-model-review.md,不向 Review LLM 泄露 Claude 的预判
  • 不修改 wiki:review 只输出建议,不直接修改任何 wiki 页面。wiki 修改由调用方(如 /refine)执行
  • score 必须有 justification:不接受没有理由的分数
  • weakness 必须有 fix:每个 weakness 必须附带具体的、可操作的修复建议,不接受空洞批评
  • claim-level mapping 必须:输出必须包含 Wiki Entity Mapping 部分,将 review 发现映射到具体 wiki 实体
  • adversarial 模式必须搜索致命缺陷:如已发表的完全相同工作、证明错误、数据泄露等
  • 多轮对话最多 3 轮:防止无限循环,若 3 轮后仍未收敛则以当前状态输出
  • 引用 wiki 页面时使用 [[slug]]:所有对 wiki 页面的引用使用 wikilink 语法

Error Handling

  • artifact 找不到:提示用户检查 slug 或路径,列出可能的候选页面
  • Review LLM 不可用:降级为 Claude 自我审查模式,报告标注「single-model review, cross-model verification unavailable」,建议用户稍后用 Review LLM 重新审查
  • wiki 为空:正常执行审查,但 Wiki Entity Mapping 部分标注「wiki empty, no entity mapping available」
  • artifact 太长:若超过 Review LLM 上下文窗口,按 section 分段审查,最后合并
  • Review LLM 返回无效响应:重试一次,若仍无效则使用 Claude 自审降级方案
  • 多轮对话中 Review LLM 不收敛:3 轮后强制结束,输出最后一轮的评分和总结

Dependencies

Tools(via Bash)

  • 无直接工具调用(review 不需要确定性工具)

MCP Servers

  • mcp__llm-review__chat — Review LLM 首轮审查(Step 2)
  • mcp__llm-review__chat-reply — Review LLM 多轮对话(Step 3)

Claude Code Native

  • Read — 读取 artifact 和 wiki 页面
  • Glob — 查找 artifact 对应的 wiki 页面

Shared References

  • .claude/skills/shared-references/cross-model-review.md — 审稿独立性原则(必读)

Called by

  • /ideate Phase 4(审查 top ideas)
  • /refine 每轮迭代(审查当前版本)
  • /exp-design --review(审查实验计划)

Signals

GitHub stars
83
Forks
17
Last commit
Jun 2026
Hacker News mentions
20
Advanced
Catalog kind
skill
Gateway key
review-lambenthan
Source
github.com/lambenthan/empiricalwiki