# Model Review

> 模拟数模竞赛评委进行深度评审。当用户说'评审方案'、'review model'、'帮我评审'、'评委视角'时使用。

- Skill: `best6668/model-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add best6668/model-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/best6668/model-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: best6668 (https://skillmd.com/u/best6668)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/best6668/model-review

---


# 数模方案评审 via Codex MCP (xhigh reasoning)

通过外部 LLM 以最高推理深度对建模方案进行多轮评审，模拟数模竞赛评委视角。

## Constants

- REVIEWER_MODEL = `gpt-5.4` — Model used via Codex MCP. Must be an OpenAI model (e.g., `gpt-5.4`, `o3`, `gpt-4o`)

## Context: $ARGUMENTS

## Prerequisites

- **Codex MCP Server** configured in Claude Code:
  ```bash
  claude mcp add codex -s user -- codex mcp-server
  ```
- This gives Claude Code access to `mcp__codex__codex` and `mcp__codex__codex-reply` tools

## Workflow

### Step 1: 收集建模上下文
Before calling the external reviewer, compile a comprehensive briefing:
1. Read project documents (e.g., PROBLEM_BRIEF.md, MODEL_REPORT.md, PROBLEM_ANALYSIS.md, paper drafts)
2. Read any result files for key findings and solving history
3. Identify: core modeling approach, mathematical methods, key results, known weaknesses

### Step 2: Initial Review (Round 1)
Send a detailed prompt with xhigh reasoning:

```
mcp__codex__codex:
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [完整建模方案 + 赛题描述 + 具体问题]
    请以数模竞赛资深评委/阅卷专家的身份评审。按以下维度打分和评价:
    1. 数学建模严谨性（模型假设是否合理、推导是否正确）
    2. 方法创新性（是否有亮点、是否超越简单套用）
    3. 结果可靠性（计算结果是否合理、是否有验证）
    4. 论文规范性（结构是否完整、表述是否清晰）
    请以国赛一等奖/美赛O奖标准严格评审。
```

### Step 3: Iterative Dialogue (Rounds 2-N)
Use `mcp__codex__codex-reply` with the returned `threadId` to continue the conversation:

For each round:
1. **Respond** to criticisms with evidence/counterarguments
2. **Ask targeted follow-ups** on the most actionable points
3. **Request specific deliverables**: model improvements, analysis suggestions, problem-method-result mapping

Key follow-up patterns:
- "If we change model assumption X to Y, does that change your assessment?"
- "What's the minimum additional analysis to satisfy concern Z?"
- "Please suggest the highest-impact improvements within the remaining competition time"
- "Please write a mock competition review with scores for each dimension"
- "Give me a problem-method-result mapping for each sub-problem"

### Step 4: Convergence
Stop iterating when:
- Both sides agree on the modeling approach and its validation requirements
- A concrete improvement plan is established
- The paper structure and narrative are settled

### Step 5: Document Everything
Save the full interaction and conclusions to a review document in the project root:
- Round-by-round summary of criticisms and responses
- Final consensus on modeling approach, validation, and improvements
- Problem-method-result mapping (问题-方法-结果对应表)
- Prioritized TODO list with estimated time costs
- Paper outline if discussed

Update project memory/notes with key review conclusions.

## Key Rules

- ALWAYS use `config: {"model_reasoning_effort": "xhigh"}` for reviews
- Send comprehensive context in Round 1 — the external model cannot read your files
- Be honest about weaknesses — hiding them leads to worse feedback
- Push back on criticisms you disagree with, but accept valid ones
- Focus on ACTIONABLE feedback — "what analysis or improvement would fix this?"
- Document the threadId for potential future resumption
- The review document should be self-contained (readable without the conversation)

## Prompt Templates

### For initial review:
"我将展示一个完整的数学建模方案，请以数模竞赛资深评委的身份进行严格评审。按国赛一等奖/美赛O奖标准..."

### For improvement design:
"请设计在剩余竞赛时间内能最大幅度提分的改进方案。当前时间余量: [describe]。请给出具体的改进步骤。"

### For paper structure:
"请将建模方案转化为数模论文大纲，包含各章节的核心论点和图表规划。"

### For problem-method-result mapping:
"请给出问题-方法-结果对应表：每个子问题使用什么方法、预期什么结果、实际得到什么结果。"

### For mock review:
"请模拟数模竞赛评审，给出: 总体评价、数学严谨性评分、创新性评分、结果可靠性评分、论文规范性评分、改进建议、获奖预估等级。"