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research-survey

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Source SKILL.md: https://github.com/tsingyuai/scientify/blob/HEAD/skills/research-survey/SKILL.md

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Research Survey (Deep Analysis)

Don't ask permission. Just do it.

Prerequisites

Read and verify these files exist before starting:

FileSource
papers//research-collect 或 /metabolism

If papers are missing, STOP: "需要先运行 /research-collect 完成论文下载"

Output

FileContent
knowledge/paper_{id}.mdPer-paper structured notes with frontmatter, formulas, and code mapping
survey_res.mdSynthesis report with method comparison, scope boundary, and concrete next-step suggestions

Workflow

Step 1: 收集论文列表

ls papers/

列出所有论文目录（arXiv 源文件）和 PDF 文件。

Step 2: 逐篇深度分析

对每篇论文：

2.1 读 .tex 源码

找到论文的 .tex 文件（在 papers/{arxiv_id}/ 下），重点读取：

  • Method / Approach section
  • Model Architecture section
  • 数学公式定义

对于大型论文（>2000 行），分段读取关键 section，避免上下文溢出。

如果没有 .tex（只有 PDF），基于 abstract 分析。

2.2 提取核心内容

从 .tex 中提取：

  • 核心方法：1-2 段描述
  • 数学公式：至少 1 个关键公式（保留 LaTeX 格式）
  • 创新点：与同领域其他方法的区别

2.3 映射到参考代码

⚠️ 强制性步骤（当 repos/ 存在时） — 代码映射是下游 plan 和 implement 的关键输入。

读取 prepare_res.md 中的仓库列表，对每个公式/核心概念：

  1. 在对应仓库中搜索实现代码（用 grep 关键类名/函数名）
  2. 记录文件路径、行号、代码片段
  3. 如果多个仓库有不同实现，记录差异

2.4 写入笔记

写入 knowledge/paper_{id}.md：

---
paper_id: "{arxiv_id}"
title: "{Paper Title}"
evidence_level: "full_text"
method_family: "{method family}"
key_formula_count: 1
code_mapping_count: 1
---

# {Paper Title}

- **arXiv:** {arxiv_id}
- **核心方法:** {1-2 sentences}

## 数学公式

$
{key formula in LaTeX}
$

含义：{解释}

## 代码映射

文件：`repos/{repo}/path/to/file.py:L42-L60`
```python
# relevant code excerpt (< 20 lines)

与本研究的关系

{如何应用到当前研究}


### Step 3: 综合报告

读取所有 `knowledge/paper_*.md`，写入 `survey_res.md`：

```markdown
# Survey Synthesis

## 论文总览
- 分析论文数: {N}
- 涉及方向: {list}

## 核心方法对比

| 论文 | 方法 | 核心公式 | 复杂度 | 优势 |
|------|------|----------|--------|------|
| ... | ... | ... | ... | ... |

## Scope Boundary

- Preconditions: {what conditions this method relies on}
- Not recommended when: {where this method should not be directly applied}
- Evidence strength: {full text / PDF / metadata}

## 技术路线建议

基于以上分析，推荐的技术路线是：
{建议}

## 关键公式汇总

**每个公式附带代码映射，供下游 plan 和 implement 参考。**

| 公式名称 | LaTeX | 参考代码 |
|----------|-------|----------|
| {name} | $...$ | `repos/{repo}/path.py:L42` |
| ... | ... | ... |

## 参考代码架构摘要

基于 repos/ 中的参考实现，推荐的代码结构：
- 数据加载: 参考 `repos/{repo}/data/`
- 模型实现: 参考 `repos/{repo}/model/`
- 训练循环: 参考 `repos/{repo}/train.py`

Rules

  1. 每篇论文必须读 .tex 原文（如有），不能只读 abstract
  2. 每篇笔记必须包含至少 1 个数学公式
  3. 如果有 repos/，必须尝试找到公式到代码的映射
  4. survey_res.md 必须包含方法对比表
  5. survey_res.md must include a scope-boundary section and concrete guidance for the current project
  6. Before writing the final synthesis, write at least 2 real paper notes to disk