القائمة
[Read when prompt contains /research-survey]
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use this when the user needs to choose between multiple ML routes after survey but before committing to implementation. Compares candidate approaches, selects one, records rejected routes, and keeps a fallback.
Use this when the project needs real baseline results before or alongside the main model. Runs classical or literature-aligned baselines under the same protocol and writes a reproducible baseline summary.
Use this when the project needs a dedicated data-quality review before model review. Checks data reality, split correctness, label health, leakage risk, shape consistency, and mock-data disclosure.
Use this when the user wants a draft paper, figure bundle, README, release page, or experiment artifact reviewed before sharing. Checks evidence binding, claim scope, captions, layout clarity, and release readiness.
Use this when the user wants to improve chart quality, standardize plotting style, regenerate release figures, or add captions/protocol notes. Normalizes fonts, colors, legends, units, and scope notes across Scientify figures.
Use this when the user wants to improve README, docs pages, or microsites so a new reader can understand what the project is, how to use it, what artifacts exist, and what the scope boundaries are within one screen.
| name | research-survey |
| description | [Read when prompt contains /research-survey] |
| metadata | {"openclaw":{"emoji":"📖"}} |
Don't ask permission. Just do it.
Read and verify these files exist before starting:
| File | Source |
|---|---|
papers/ | /research-collect 或 /metabolism |
If papers are missing, STOP: "需要先运行 /research-collect 完成论文下载"
| File | Content |
|---|---|
knowledge/paper_{id}.md | Per-paper structured notes with frontmatter, formulas, and code mapping |
survey_res.md | Synthesis report with method comparison, scope boundary, and concrete next-step suggestions |
ls papers/
列出所有论文目录（arXiv 源文件）和 PDF 文件。
对每篇论文：
找到论文的 .tex 文件（在 papers/{arxiv_id}/ 下），重点读取：
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。
从 .tex 中提取：
⚠️ 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：
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)
{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`