# Problem Analysis

> 数模赛题分析与经典方法检索。分析赛题结构、识别子问题、检索经典建模方法。当用户说'分析赛题'、'problem analysis'、'赛题解读'时使用。

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

---


# 赛题分析与方法检索

Competition problem: $ARGUMENTS

## Constants

- **METHOD_LIBRARY** — Local directory containing reference materials (past solutions, method notes). Check these paths in order:
  1. `data/` in the current project directory
  2. `reference/` in the current project directory
  3. Custom path specified by user in `CLAUDE.md` under `## Method Library`
- **MAX_LOCAL_REFS = 10** — Maximum number of local reference files to scan. If more are found, prioritize by filename relevance to the problem.
- **SEARCH_DEPTH = standard** — When `deep`, perform extended web search for similar competition problems and classic methods. When `standard` (default), focus on the most relevant methods and past solutions.

> 💡 Overrides:
> - `/problem-analysis "赛题" — method library: ~/my_refs/` — custom reference path
> - `/problem-analysis "赛题" — sources: local` — only search local references
> - `/problem-analysis "赛题" — sources: web` — only search the web (skip local)
> - `/problem-analysis "赛题" — sources: local, web` — search local + web
> - `/problem-analysis "赛题" — search depth: deep` — extended search for similar problems and methods

## Data Sources

This skill checks multiple sources **in priority order**. All are optional — if a source is not configured or not available, skip it silently.

### Source Selection

Parse `$ARGUMENTS` for a `— sources:` directive:
- **If `— sources:` is specified**: Only search the listed sources (comma-separated). Valid values: `local`, `web`, `all`.
- **If not specified**: Default to `all` — search every available source in priority order.

Examples:
```
/problem-analysis "赛题描述"                                         → all (default)
/problem-analysis "赛题描述" — sources: all                          → all (default)
/problem-analysis "赛题描述" — sources: local                        → local references only
/problem-analysis "赛题描述" — sources: web                          → web search only
/problem-analysis "赛题描述" — sources: local, web                   → local + web
```

### Source Table

| Priority | Source | ID | How to detect | What it provides |
|----------|--------|----|---------------|-----------------|
| 1 | **Local references** | `local` | `Glob: data/**/*.*, reference/**/*.*` | 赛题数据文件、参考资料、过往优秀论文 |
| 2 | **Web search** | `web` | Always available (WebSearch) | 数模优秀论文库、CNKI、经典教材方法、赛题案例库 |

> **Graceful degradation**: If no local references exist, the skill still works via web search alone.

## Workflow

### Step 0a: 读取赛题与数据

1. **读取赛题描述**: 从 `$ARGUMENTS` 或 `PROBLEM_BRIEF.md` 获取完整赛题文本
2. **扫描数据文件**: 检查 `data/` 目录下的数据文件（.csv, .xlsx, .txt 等）
   ```
   Glob: data/**/*.{csv,xlsx,xls,txt,dat,json}
   ```
3. **初步数据概览**: 对每个数据文件，快速提取：
   - 文件名、大小、行数/列数
   - 列名/字段名
   - 数据类型和基本统计量
4. **识别赛题类型**: 判断属于哪一类数模问题（优化、预测、评价、分类、调度、博弈等）

> 📋 赛题数据是建模的基础——先理解数据结构，再决定建模方法。

### Step 0b: 扫描本地参考资料

检查用户是否已有相关参考资料：

1. **Locate references**: Check METHOD_LIBRARY paths for reference files
   ```
   Glob: reference/**/*.{pdf,md,txt,docx}, data/**/*.{pdf,md,txt}
   ```

2. **Filter by relevance**: Match filenames and content against the competition problem. Skip clearly unrelated files.

3. **Summarize relevant references**: For each relevant file (up to MAX_LOCAL_REFS):
   - Read content (first 3 pages for PDFs)
   - Extract: title, core method, applicability to current problem
   - Flag references that are directly related vs tangentially related

4. **Build local knowledge base**: Compile summaries into a "references you already have" section. This becomes the starting point — web search fills the gaps.

> 📚 If no local references are found, skip to Step 1. If the user has relevant materials, the web search can be more targeted (focus on what's missing).

### Step 1: 检索经典方法与类似赛题 (external)
- Use WebSearch to find classic modeling methods for this type of problem
- Search for: 数模竞赛优秀论文, CNKI 数学建模, 经典教材方法, 赛题案例库
- Focus on: (1) classic textbook methods, (2) past competition winning solutions, (3) applicable mathematical techniques
- **De-duplicate**: Skip methods already found in local references

**Web search strategy** (multi-query):

```
Query 1: "[问题类型] 数学建模方法" (e.g., "优化问题 数学建模方法")
Query 2: "[关键词] 数学建模竞赛 优秀论文" (e.g., "交通流 数学建模竞赛 优秀论文")
Query 3: "[问题类型] mathematical modeling approach" (English search for international methods)
Query 4: "CUMCM/MCM [类似赛题关键词]" (search for similar past competition problems)
```

For each search, extract:
- Method name and category (统计/优化/微分方程/图论/机器学习/模拟)
- Key references or textbook sources
- Applicability to the current problem
- Complexity and implementation difficulty

**Method categorization**:

Organize found methods into a **方法地图** (method map):
| Category | Methods | Applicability | Difficulty |
|----------|---------|--------------|------------|
| 统计分析 | 回归分析, 时间序列, 主成分分析... | | |
| 优化方法 | 线性规划, 整数规划, 动态规划... | | |
| 微分方程 | ODE, PDE, 差分方程... | | |
| 图论/网络 | 最短路, 网络流, 图着色... | | |
| 机器学习 | 分类, 聚类, 神经网络... | | |
| 模拟仿真 | Monte Carlo, 元胞自动机, Agent-based... | | |

### Step 2: 分析赛题结构
For each sub-problem identified, extract:
- **问题类型**: 属于哪类数学问题（优化/预测/评价/分类/调度/博弈等）
- **关键变量**: 决策变量、参数、约束条件
- **数据需求**: 需要哪些数据，现有数据是否满足
- **可行方法**: 基于 Step 1 的方法地图，列出候选方法
- **难点**: 该子问题的主要建模难点

### Step 3: 综合分析
- Group methods by problem type and applicability
- Identify which methods are most suitable for each sub-problem
- Find potential innovation points (方法组合、改进、创新应用)
- Assess data-method compatibility (数据是否支持所选方法)

### Step 4: Output
Save as `PROBLEM_ANALYSIS.md`:

```
| 子问题 | 问题类型 | 候选方法 | 推荐方法 | 难度 | 创新空间 |
|--------|---------|---------|---------|------|---------|
```

Plus a narrative analysis (3-5 paragraphs) covering:
1. 赛题整体解读
2. 子问题之间的逻辑关系
3. 推荐的建模路线（主方法 + 备选方法）
4. 潜在的创新点和加分项

Include a simple reference list for methods and sources consulted.

### Step 5: Save
- Save `PROBLEM_ANALYSIS.md` to the project root
- Update any reference materials in `reference/`
- Log the analysis in project memory for downstream skills

## Key Rules
- Always cite method sources (textbook, past competition paper, or reference)
- Distinguish between classic methods (well-established) and novel approaches (higher risk, higher reward)
- Be honest about limitations of each method for this specific problem
- Note which methods are commonly used in award-winning papers for similar problems
- **Never fail because local references are missing** — always fall back gracefully to web search
- Prioritize methods that match the available data and time constraints of the competition