skills/research/paper-review/scientify-write-review-paper/SKILL.md
Write literature reviews and survey papers from collected papers
npx skillsauth add wentorai/research-plugins scientify-write-review-paperInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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
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