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Get Started Free →Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.
.claude/skills/brycewang-stanford-medical-imaging-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 137% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 12% | 0% |
<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档,收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝
来源仓库: https://github.com/luwill/research-skills 项目名称: research-skills 开源协议: MIT License 收录日期: 2026-04-02
声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权,请联系删除。 -->
Write comprehensive literature reviews following a systematic 7-phase workflow.
CLAUDE.md - Writing guidelines and terminologyIMPLEMENTATION_PLAN.md - Staged execution planmanuscript_draft.md - Main manuscriptTopic sentence (main claim)
→ Supporting evidence (citations + data)
→ Analysis (critical evaluation)
→ Transition to next paragraphUse multi-source strategy for comprehensive coverage:
| Source | Best For | Tools | |--------|----------|-------| | ArXiv | Latest DL methods, preprints | search_papers, read_paper | | PubMed | Clinical validation, peer-reviewed | pubmed_search_articles | | Zotero | Existing library, organized refs | zotero_search_items |
For MCP configuration details, see references/MCP_SETUP.md.
markdown# [Title]: State of the Art and Future Directions ## Key Points - [3-5 bullets summarizing main findings] ## Abstract ## 1. Introduction ### 1.1 Clinical Background ### 1.2 Technical Challenges ### 1.3 Scope and Contributions ## 2. Datasets and Evaluation Metrics ### 2.1 Public Datasets (Table 1) ### 2.2 Evaluation Metrics ## 3. Deep Learning Methods ### 3.1 [Category 1] ### 3.2 [Category 2] (Table 2: Method Comparison) ## 4. Downstream Applications ## 5. Commercial Products & Clinical Translation (Table 3) ## 6. Discussion ### 6.1 Current Limitations ### 6.2 Future Directions ## 7. Conclusion ## References
markdown### 3.X [Method Category] [1-2 paragraph introduction with motivation] **[Method Name]:** [Author] et al. [ref] proposed [method], which [innovation]: - [Key component 1] - [Key component 2] Achieves Dice of X.XX on [dataset]. **Limitations:** Despite advantages, [category] methods face: (1) [limit 1]; (2) [limit 2].
markdown# Data citation "...achieved Dice of 0.89 [23]" # Method citation "Gu et al. [45] proposed..." # Multi-citation "Several studies demonstrated... [12, 15, 23]" # Comparative "While [12] focused on..., [15] addressed..."
| File | Purpose | |------|---------| | references/WORKFLOW.md | Detailed 7-phase workflow | | references/TEMPLATES.md | CLAUDE.md and IMPLEMENTATION_PLAN.md templates | | references/DOMAINS.md | Domain-specific method categories | | references/MCP_SETUP.md | MCP server configuration | | references/QUALITY_CHECKLIST.md | Pre-submission quality checklist |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 91,700 | 33,890 | -63% | 1 | 1 | 0% | 6,190 | 7,371 | +19% | 0 | 0 | — |
case-02 | fail→fail | 31,100 | 30,398 | -2% | 1 | 1 | 0% | 6,196 | 7,377 | +19% | 0 | 0 | — |
case-03 | fail→pass | 30,191 | 39,690 | +31% | 1 | 1 | 0% | 6,185 | 7,366 | +19% | 0 | 0 | — |
case-04 | fail→pass | 7,302 | 5,172 | -29% | 1 | 1 | 0% | 1,419 | 2,345 | +65% | 0 | 0 | — |
case-05 | pass→pass | 13,925 | 10,388 | -25% | 1 | 1 | 0% | 3,110 | 3,414 | +10% | 0 | 0 | — |
case-06 | fail→pass | 4,249 | 4,868 | +15% | 1 | 1 | 0% | 888 | 2,104 | +137% | 0 | 0 | — |
case-07 | fail→pass | 16,300 | 10,187 | -38% | 1 | 1 | 0% | 3,117 | 3,091 | -1% | 0 | 0 | — |
case-08 | pass→pass | 18,826 | 8,689 | -54% | 1 | 1 | 0% | 2,872 | 2,991 | +4% | 0 | 0 | — |
case-09 | pass→pass | 9,651 | 4,557 | -53% | 1 | 1 | 0% | 1,965 | 1,899 | -3% | 0 | 0 | — |
case-10 | fail→pass | 21,909 | 13,229 | -40% | 1 | 1 | 0% | 3,311 | 3,698 | +12% | 0 | 0 | — |
case-11 | fail→fail | 17,100 | 30,860 | +80% | 1 | 1 | 0% | 3,475 | 7,344 | +111% | 0 | 0 | — |
case-12 | fail→fail | 18,166 | 20,301 | +12% | 1 | 1 | 0% | 3,234 | 4,496 | +39% | 0 | 0 | — |
case-13 | fail→pass | 17,754 | 15,965 | -10% | 1 | 1 | 0% | 3,130 | 3,789 | +21% | 0 | 0 | — |
case-14 | pass→pass | 11,652 | 8,785 | -25% | 1 | 1 | 0% | 2,349 | 2,881 | +23% | 0 | 0 | — |
case-15 | fail→fail | 7,641 | 8,872 | +16% | 1 | 1 | 0% | 1,369 | 2,716 | +98% | 0 | 0 | — |
case-16 | fail→pass | 13,486 | 1,480 | -89% | 1 | 1 | 0% | 1,937 | 1,418 | -27% | 0 | 0 | — |
case-17 | fail→pass | 9,365 | 1,099 | -88% | 1 | 1 | 0% | 1,815 | 1,311 | -28% | 0 | 0 | — |
case-18 | pass→pass | 14,131 | 5,851 | -59% | 1 | 1 | 0% | 2,202 | 2,097 | -5% | 0 | 0 | — |
case-19 | fail→pass | 11,387 | 1,257 | -89% | 1 | 1 | 0% | 1,950 | 1,361 | -30% | 0 | 0 | — |
case-20 | pass→pass | 13,199 | 8,518 | -35% | 1 | 1 | 0% | 2,776 | 2,946 | +6% | 0 | 0 | — |
case-21 | pass→pass | 22,328 | 29,670 | +33% | 1 | 1 | 0% | 4,452 | 6,911 | +55% | 0 | 0 | — |
case-22 | pass→pass | 16,766 | 21,498 | +28% | 1 | 1 | 0% | 4,656 | 6,580 | +41% | 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. The headline lift of +41 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.