| name | mm-analysis |
| description | Stage 1 of the mathematical modeling pipeline. Performs deep problem analysis using Actor-Critic self-improvement with percent-based scoring. Invoked by the math-modeling skill during Stage 1. Do not invoke directly — use /math-model instead.
|
| version | 0.4.4 |
Stage 1: Problem Analysis
Purpose
Deeply analyze the mathematical modeling problem, understanding its background, objectives, assumptions, constraints, and potential challenges. Use Actor-Critic mechanism to iteratively improve the analysis quality.
Input
- Problem text (from conversation or file)
- Dataset files (if any, from
mm-workspace/raw_problem.txt or user-provided paths)
Process
Step 1: Problem Extraction
Read the problem and extract structured information:
- Background: The domain, motivation, and context of the problem
- Requirements: Specific questions to answer, constraints to satisfy
- Data Available: List all dataset files and their locations
- Addendum: Any supplementary information, notes, or clarifications
If the problem is in a PDF or image, use the Read tool to extract the content.
Step 2: Data Summary (if dataset exists)
If the problem includes data files:
-
Determine data format and load appropriately:
- CSV:
pd.read_csv('path/to/data.csv')
- Excel (single or multi-sheet):
pd.read_excel('path/to/data.xlsx', sheet_name=None)
- JSON:
pd.read_json('path/to/data.json')
- Image-based tables: Note in summary that OCR/extraction is needed
-
Run a Python script to examine the data:
import pandas as pd
import numpy as np
import json
data = pd.read_csv('path/to/data.csv')
summary = {
'shape': list(data.shape),
'columns': list(data.columns),
'dtypes': {col: str(dt) for col, dt in data.dtypes.items()},
'head': data.head(3).to_dict(),
'describe': data.describe().to_dict(),
'null_counts': data.isnull().sum().to_dict(),
'null_pct': (data.isnull().sum() / len(data) * 100).to_dict()
}
print(json.dumps(summary, indent=2, default=str))
for col in data.select_dtypes(include=[np.number]).columns:
Q1, Q3 = data[col].quantile(0.25), data[col].quantile(0.75)
IQR = Q3 - Q1
outliers = ((data[col] < Q1 - 1.5*IQR) | (data[col] > Q3 + 1.5*IQR)).sum()
if outliers > 0:
print(f"WARNING: {col} has {outliers} potential outliers ({outliers/len(data)*100:f}%)")
col data.columns:
data[col].isnull().() > :
pct = data[col].isnull().() / (data) *
()
-
Generate a concise text summary of the data covering:
- Number of records and fields
- Data types and ranges
- Missing values and anomalies
- Key statistics
- Potential data quality issues and cleaning suggestions
Step 3: Problem Analysis (Actor-Critic, 自适应 1-3 轮, 通过线 75 分)
Load references/actor_critic.md for the scoring criteria.
Actor: Generate Initial Analysis
Produce a deep analysis covering:
- Core objectives: What is the model trying to achieve?
- Implicit assumptions: What beliefs or constraints are embedded in the problem?
- Interdependencies: How do different problem components relate?
- Hidden complexities: What challenges arise from component interactions?
- Temporal/scale considerations: How might the problem evolve?
- Alternative perspectives: Different ways to frame the problem
- Risks and uncertainties: Inherent in choosing modeling approaches
Write as structured analysis: use numbered lists for assumptions, tables for variable definitions, and numbered LaTeX for key equations. Use coherent paragraphs for reasoning and discussion.
Critic: Score the Analysis (Independent Subagent)
Use the Agent tool to dispatch an independent subagent for the Critic role. The subagent must NOT inherit the Actor's reasoning context — it only receives the Actor's final output.
Dispatch an Agent with the following prompt structure:
你是一名严格的数学建模评审专家（Critic 角色）。请对以下问题分析按维度量化评分。
## 评分标准（百分制，加权求和）
| 维度 | 权重 | 60分线 | 满分要求 |
|------|------|--------|---------|
| 需求完整性 | 25 | 所有显式子问题均已识别 | 显式+隐式需求全部挖掘 |
| 深度洞察 | 25 | 超越表面，识别隐含约束 | 发现非常规难点和跨问题关联 |
| 数据感知 | 20 | 数据已加载、缺失值/异常值已报告 | 数据特征深度分析+清洗方案 |
| 假设识别 | 15 | 关键假设已列出 | 假设合理性论证充分 |
| 结构清晰度 | 15 | 分析有结构、可读 | 建模导向明确，直接指导下一阶段 |
通过线：75 分
硬性否决：需求完整性 < 10 分 → 总分封顶 59
## 问题背景
{Insert problem text summary, 1-2 paragraphs}
## 被审查的分析内容
{Insert Actor's complete analysis output}
## 输出要求
严格按以下 JSON 格式输出，不要有多余文字：
```json
{
"scores": {
"requirement_completeness": {"score": 0-100, "note": "具体说明"},
"depth_insight": {"score": 0-100, "note": "具体说明"},
"data_awareness": {"score": 0-100, "note": "具体说明"},
"assumption_identification": {"score": 0-100, "note": "具体说明"},
"structure_clarity": {"score": 0-100, "note": "具体说明"}
},
"total": 加权总分,
"improvement_directions": ["具体改进方向1", "改进方向2"],
"fatal_issue": null 或 "具体说明（仅当触发硬性否决时填写）"
}
Receive the Critic scores and process:
- If `total >= 75`: Accept the analysis, proceed to Step 4
- If `total < 75` and round < 3: Run Improvement, then repeat Actor → Critic
- If `total < 60` and round == 3: Pause pipeline, present to user for decision
#### Improvement: Refine the Analysis
Based on Critic's `improvement_directions`, produce an improved version:
- Address all identified weaknesses
- Do not reference the previous version or its flaws
- Write as a complete, standalone analysis
### Step 4: Save Output
Write the final output to `mm-workspace/01_analysis.json`:
```json
{
"problem_text": "complete problem text",
"data_summary": "data summary text or empty string",
"analysis": "final improved analysis text",
"has_dataset": true,
"dataset_files": ["file1.csv"],
"stage": "analysis_complete"
}
Use the Write tool to save this file.
Then commit: cd mm-workspace && git add -A && git commit -m "feat(s1): problem analysis complete"
Step 5: Present to User
Display a concise summary:
- Problem type and key challenges identified
- Main objectives and assumptions
- Data availability summary
- Key insights from the analysis
Then ask: "问题分析完成。是否需要修改或补充？确认后进入建模阶段。"
