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- brycewang-stanford/Auto-Empirical-Research-Skills
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- 2026年4月3日 02:07
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安装方式
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
检查来源文件
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
菜单
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill scientify-write-review-paper命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
正在显示 SKILL.md
| name | scientify-write-review-paper |
| description | Write literature reviews and survey papers from collected papers |
| metadata | {"openclaw":{"emoji":"📝","category":"research","subcategory":"paper-review","keywords":["paper summarization","paper comparison","research gap analysis","narrative review","systematic review methodology"],"source":"wentor-research-plugins"}} |
Don't ask permission. Just do it.
Guide for writing a structured literature review or survey paper from papers you've already collected. This skill helps with reading strategy, note organization, and academic writing.
Workspace: See ../_shared/workspace-spec.md for directory structure. Outputs go to $WORKSPACE/review/.
Before starting, ensure you have:
$WORKSPACE/papers//literature-survey in $WORKSPACE/survey/clusters.jsonCheck active project:
cat ~/.openclaw/workspace/projects/.active 2>/dev/null
ls $WORKSPACE/papers/
Based on clusters from survey, prioritize reading:
| Priority | Criteria | Reading Depth |
|---|---|---|
| P1 (Must-read) | High citation, foundational, directly relevant | Full read |
| P2 (Important) | Key methodology, major experimental results | Abstract + methods + experiments |
| P3 (Reference) | Supporting material, tangentially related | Abstract only |
Create $WORKSPACE/review/reading_plan.md:
# Reading Plan
## P1 - Must-read (Full read)
- [ ] [paper_id]: [title] - [reason]
- [ ] ...
## P2 - Important (Selective read)
- [ ] ...
## P3 - Reference (Skim)
- [ ] ...
For each paper, create $WORKSPACE/review/notes/{paper_id}.md using template in references/note-template.md.
Create $WORKSPACE/review/comparison.md:
# Method Comparison
| Paper | Year | Category | Key Innovation | Dataset | Metric | Result |
|-------|------|----------|----------------|---------|--------|--------|
| [A] | 2023 | Data-driven | ... | ... | RMSE | 0.05 |
| [B] | 2022 | Hybrid | ... | ... | RMSE | 0.08 |
Create $WORKSPACE/review/timeline.md:
# Research Timeline
## 2018-2019: Early Exploration
- [Paper A]: First proposal of method X
- [Paper B]: Introduction of technique Y
## 2020-2021: Method Maturation
- [Paper C]: Proposed SOTA method
- ...
## 2022-2023: New Trends
- [Paper D]: Began addressing problem Z
- ...
## Key Milestones
1. [Year]: [Event/Paper] - [Significance]
Create $WORKSPACE/review/taxonomy.md:
# Taxonomy of Approaches
## Dimension 1: Method Type
- Data-driven
- Statistical (e.g., GPR, SVM)
- Deep Learning
- CNN-based
- RNN/LSTM-based
- Transformer-based
- Hybrid
- Model-based
- Electrochemical
- Equivalent Circuit
## Dimension 2: Data Source
- Laboratory Data
- Real-world Driving Data
- Synthetic Data
## Dimension 3: Prediction Horizon
- Short-term (< 100 cycles)
- Medium-term (100-500 cycles)
- Long-term (> 500 cycles)
Create $WORKSPACE/review/draft.md using template in references/survey-template.md.
Key sections: Abstract -> Introduction -> Background -> Taxonomy -> Comparison -> Datasets -> Future Directions -> Conclusion
For a thesis chapter:
# Chapter 2: Literature Review
## 2.1 Introduction
## 2.2 [Topic Area 1]
## 2.3 [Topic Area 2]
## 2.4 Summary and Research Gaps
| Section | Citation Density |
|---|---|
| Abstract | 0 citations |
| Introduction | 10-20 citations |
| Background | 5-10 citations |
| Main Survey | 50-100+ citations |
| Conclusion | 2-5 citations |
Introducing similar work:
Introducing contrasting work:
Summarizing:
$WORKSPACE/review/
├── reading_plan.md # Reading plan
├── notes/ # Reading notes
│ ├── {paper_id}.md
│ └── ...
├── comparison.md # Comparison table
├── timeline.md # Timeline analysis
├── taxonomy.md # Taxonomy
├── draft.md # Review draft
└── bibliography.bib # References