Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
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 f...
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Write comprehensive literature reviews following a systematic 7-phase workflow.
Initialize project with three core files:
CLAUDE.md - Writing guidelines and terminologyIMPLEMENTATION_PLAN.md - Staged execution planmanuscript_draft.md - Main manuscriptFollow the 7-phase workflow (see references/WORKFLOW.md)
Use domain-specific templates (see references/DOMAINS.md)
Topic sentence (main claim)
→ Supporting evidence (citations + data)
→ Analysis (critical evaluation)
→ Transition to next paragraph
Use 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.
# [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
## Input Validation
This skill accepts requests that match the documented purpose of `medical-imaging-review` and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
> `medical-imaging-review` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
## References
### 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].
# 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 |
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概要と使いどころ
Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
日本語の概要は準備中です。原文の説明を表示しています。
Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.
日本語の概要は準備中です。原文の説明を表示しています。
Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.
日本語の概要は準備中です。原文の説明を表示しています。
Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
日本語の概要は準備中です。原文の説明を表示しています。