Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Write literature reviews and survey papers from collected papers
.claude/skills/brycewang-stanford-scientify-write-review-paper/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-22 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-19 | ✓→✗ | ▼ Worse | 24% | 0% |
| case-07 | ✓→✓ | = Same ✓ | 40% | 0% |
| case-10 | ✓→✓ | = Same ✓ | 42% | 0% |
| case-11 | ✓→✓ | = Same ✓ | 56% | 0% |
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:
bashcat ~/.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:
markdown# 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:
markdown# 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:
markdown# 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:
markdown# 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:
markdown# 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| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 28,001 | 14,423 | -48% | 1 | 1 | 0% | 4,795 | 1,893 | -61% | 0 | 0 | — |
case-02 | fail→fail | 29,912 | 7,721 | -74% | 1 | 1 | 0% | 4,876 | 1,803 | -63% | 0 | 0 | — |
case-03 | fail→fail | 44,894 | 7,001 | -84% | 1 | 1 | 0% | 7,768 | 1,629 | -79% | 0 | 0 | — |
case-04 | fail→fail | 17,960 | 5,594 | -69% | 1 | 1 | 0% | 3,496 | 1,655 | -53% | 0 | 0 | — |
case-05 | fail→fail | 6,947 | 5,678 | -18% | 1 | 1 | 0% | 1,044 | 1,830 | +75% | 0 | 0 | — |
case-06 | fail→fail | 9,016 | 7,576 | -16% | 1 | 1 | 0% | 1,441 | 1,814 | +26% | 0 | 0 | — |
case-07 | pass→pass | 14,020 | 11,250 | -20% | 1 | 1 | 0% | 2,258 | 3,154 | +40% | 0 | 0 | — |
case-08 | fail→fail | 12,129 | 11,961 | -1% | 1 | 1 | 0% | 1,894 | 2,738 | +45% | 0 | 0 | — |
case-09 | fail→fail | 15,734 | 8,831 | -44% | 1 | 1 | 0% | 2,329 | 2,685 | +15% | 0 | 0 | — |
case-10 | pass→pass | 14,046 | 13,598 | -3% | 1 | 1 | 0% | 1,893 | 2,685 | +42% | 0 | 0 | — |
case-11 | pass→pass | 14,568 | 11,578 | -21% | 1 | 1 | 0% | 1,893 | 2,949 | +56% | 0 | 0 | — |
case-12 | pass→pass | 16,110 | 11,345 | -30% | 1 | 1 | 0% | 2,316 | 2,940 | +27% | 0 | 0 | — |
case-13 | pass→pass | 11,874 | 7,895 | -34% | 1 | 1 | 0% | 1,713 | 2,821 | +65% | 0 | 0 | — |
case-14 | pass→pass | 11,243 | 12,882 | +15% | 1 | 1 | 0% | 2,085 | 3,327 | +60% | 0 | 0 | — |
case-15 | pass→pass | 12,894 | 10,549 | -18% | 1 | 1 | 0% | 2,086 | 2,977 | +43% | 0 | 0 | — |
case-16 | pass→pass | 13,334 | 8,152 | -39% | 1 | 1 | 0% | 2,119 | 2,593 | +22% | 0 | 0 | — |
case-17 | pass→pass | 16,762 | 12,178 | -27% | 1 | 1 | 0% | 2,615 | 3,418 | +31% | 0 | 0 | — |
case-18 | pass→pass | 19,018 | 21,875 | +15% | 1 | 1 | 0% | 3,156 | 5,178 | +64% | 0 | 0 | — |
case-19 | pass→fail | 13,856 | 6,340 | -54% | 1 | 1 | 0% | 1,960 | 2,439 | +24% | 0 | 0 | — |
case-20 | pass→pass | 21,715 | 15,924 | -27% | 1 | 1 | 0% | 3,092 | 4,200 | +36% | 0 | 0 | — |
case-21 | pass→pass | 13,031 | 8,803 | -32% | 1 | 1 | 0% | 2,091 | 2,777 | +33% | 0 | 0 | — |
case-22 | fail→pass | 12,776 | 5,009 | -61% | 1 | 1 | 0% | 1,825 | 2,233 | +22% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 16 counted toward the lift figure. The other 6 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of 0 percentage points is the difference between those two pass rates over the 16 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.