# Feasibility Check

> 验证建模方案的可行性。检查数据匹配度、计算复杂度、实现时间。当用户说'可行性检查'、'feasibility check'、'这个方案能做吗'时使用。

- Skill: `best6668/feasibility-check` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add best6668/feasibility-check`
- Raw SKILL.md: https://api.skillmd.com/api/skills/best6668/feasibility-check/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/feasibility-check

---


# 可行性检查

检查建模方案在给定数据和时间约束下是否可行: **$ARGUMENTS**

## Constants

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

## Instructions

Given a modeling approach description, systematically verify its feasibility:

### Phase A: 提取关键要素
1. Read the user's modeling approach description
2. Identify 3-5 core feasibility dimensions that need verification:
   - What mathematical method is proposed?
   - What data does it require? Does the available data match?
   - What is the computational complexity?
   - What is the implementation difficulty and time estimate?

### Phase B: 多源可行性检索
For EACH feasibility dimension, search using ALL available sources:

1. **Web Search** (via `WebSearch`):
   - Search for similar competition problems that used this method
   - Use specific technical terms from the approach
   - Try at least 3 different query formulations per dimension
   - Focus on: 数模竞赛优秀论文, 经典教材案例, 方法适用条件

2. **Known method references**: Check against:
   - Past CUMCM/MCM winning papers using similar methods
   - Classic textbook examples and applicable conditions

3. **Read details**: For each relevant reference, WebFetch its method section and results to assess real-world performance

### Phase C: 交叉模型验证
Call REVIEWER_MODEL via Codex MCP (`mcp__codex__codex`) with xhigh reasoning:
```
config: {"model_reasoning_effort": "xhigh"}
```
Prompt should include:
- The proposed modeling approach
- All references found in Phase B
- Ask: "这个建模方案在给定数据和时间约束下可行吗？数据是否匹配？计算复杂度是否可控？实现时间是否合理？"

### Phase D: 可行性报告
Output a structured report:

```markdown
## 可行性检查报告

### 建模方案
[1-2 sentence description]

### 可行性评估维度
1. [数据匹配度] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
2. [计算复杂度] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
3. [实现时间] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
4. [方法适用性] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
...

### 类似赛题案例
| 赛题 | 年份 | 竞赛 | 方法 | 效果 |
|------|------|------|------|------|

### 综合可行性评估
- Score: X/10
- Recommendation: PROCEED / PROCEED WITH CAUTION / ABANDON
- 关键优势: [what makes this approach feasible]
- 风险点: [what could go wrong and how to mitigate]

### 改进建议
[How to adjust the approach to improve feasibility if needed]
```

### Important Rules
- Be BRUTALLY honest — an infeasible approach wastes precious competition time
- "Standard method X applied directly" is feasible but may not score high — note the trade-off
- Check both the method suitability AND the data compatibility
- If the method is feasible but unlikely to score well, say so explicitly and suggest improvements
- Always check whether the competition data actually supports the proposed method