pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
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
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-review --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-review -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-review -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-review -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-review -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-review -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-review -a windsurf -y
name: medical-imaging-review description: > 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. metadata: author: user version: "2.0.0" allowed-tools: - Read - Write - Edit - Glob - Grep - Bash - WebSearch - WebFetch - Task - mcp__arxiv-mcp-server__search_papers - mcp__arxiv-mcp-server__download_paper - mcp__arxiv-mcp-server__read_paper - mcp__pubmed-mcp-server__pubmed_search_articles - mcp__zotero__zotero_search_items - mcp__zotero__zotero_get_item_fulltext
<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 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.
1. **Initialize project** with three core files:
2. **Follow the 7-phase workflow** (see [references/WORKFLOW.md](references/WORKFLOW.md))
3. **Use domain-specific templates** (see [references/DOMAINS.md](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](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 ## 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](references/WORKFLOW.md) | Detailed 7-phase workflow | | [references/TEMPLATES.md](references/TEMPLATES.md) | CLAUDE.md and IMPLEMENTATION_PLAN.md templates | | [references/DOMAINS.md](references/DOMAINS.md) | Domain-specific method categories | | [references/MCP_SETUP.md](references/MCP_SETUP.md) | MCP server configuration | | [references/QUALITY_CHECKLIST.md](references/QUALITY_CHECKLIST.md) | Pre-submission quality checklist |
## 🔥 力荐 · StatsPAI — Stata 与 R 的 Python 平替,为 AI Agent 而生 🔁 Stata / R 平替:reghdfe、csdid、rdrobust、synth、fixest、did 等常用命令在 Python 中一站式替代,一个 import statspai as sp 搞定 DID / RD / IV / SCM / DML / matching 🤖 面向 Agent 设计:结构化结果对象 + 内置 MCP Server,Claude / Codex
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