Skills Collection — Medical AI PhD Research Workflows
A curated collection of 50 Claude Code skills organized by research workflow stage, tailored for AI and Medical AI PhD researchers.
Documentation
Plugins
| Plugin | Skills | Description |
|---|
literature-and-topic-selection | 12 | Topic ideation, literature review, academic databases |
data-preparation-and-processing | 12 | Medical imaging, EHR, physiological signals, data processing |
model-development-and-experiments | 11 | Deep learning, GNNs, explainability, time series |
results-analysis-and-visualization | 6 | Plotting, statistical analysis, interactive visualization |
paper-writing-and-submission | 10 | Academic writing, slides, posters, peer review |
Installation
Step 1 — Add this repository as a marketplace
/plugin marketplace add youyinnn/skills-collection
Step 2 — Install the plugins you need
/plugin install literature-and-topic-selection@skills-collection
/plugin install data-preparation-and-processing@skills-collection
/plugin install model-development-and-experiments@skills-collection
/plugin install results-analysis-and-visualization@skills-collection
/plugin install paper-writing-and-submission@skills-collection
You can install only the plugins relevant to your current work.
Recommended minimal install for Medical AI PhD
/plugin install literature-and-topic-selection@skills-collection
/plugin install data-preparation-and-processing@skills-collection
/plugin install model-development-and-experiments@skills-collection
/plugin install results-analysis-and-visualization@skills-collection
/plugin install paper-writing-and-submission@skills-collection
Skills by Stage
Stage 1 — Literature & Topic Selection
| Skill | Purpose |
|---|
scientific-brainstorming | Research ideation, finding gaps, cross-disciplinary exploration |
hypothesis-generation | Derive testable hypotheses from data or literature |
literature-review | Systematic review across PubMed / arXiv / bioRxiv |
scientific-critical-thinking | Evaluate study design, identify bias, evidence grading (GRADE) |
citation-management | Reference management workflows |
pyzotero | Programmatic Zotero library management |
arxiv-database | Search arXiv preprints (CS / AI / Statistics) |
pubmed-database | PubMed REST API with Boolean / MeSH queries |
biorxiv-database | Search bioRxiv life science preprints |
openalex-database | Query 240M+ scholarly works, citation analysis |
bgpt-paper-search | Extract structured experiment data from full-text papers |
research-grants | NSF / NIH / DARPA grant writing |
Stage 2 — Data Preparation & Processing
| Skill | Purpose |
|---|
pydicom | Read/write DICOM files (CT / MRI / X-Ray / Ultrasound) |
pathml | Computational pathology WSI analysis, 160+ formats |
histolab | Lightweight H&E tile extraction |
imaging-data-commons | Access NIH public medical imaging datasets |
pyhealth | Medical AI toolkit: MIMIC-III/IV, eICU, OMOP |
clinicaltrials-database | Query ClinicalTrials.gov |
clinvar-database | Query ClinVar genetic variants database |
neurokit2 | Physiological signal processing: ECG / EEG / EDA / PPG |
polars | Fast in-memory dataframe processing |
dask | Distributed computing for larger-than-RAM workflows |
exploratory-data-analysis | EDA reports for scientific data formats |
statistical-analysis | Statistical testing and modeling |
Stage 3 — Model Development & Experiments
| Skill | Purpose |
|---|
pytorch-lightning | Structured PyTorch training with logging and checkpointing |
transformers | HuggingFace transformers for NLP and vision tasks |
torch-geometric | Graph neural networks for molecular / biomedical data |
scikit-learn | Classical ML: classification, regression, clustering |
pymc | Bayesian statistical modeling |
shap | Model explainability via Shapley values |
umap-learn | Dimensionality reduction and embedding visualization |
aeon | Time series classification and regression |
networkx | Graph analysis and network science |
primekg | Biomedical knowledge graph for drug discovery |
stable-baselines3 | Reinforcement learning algorithms |
Stage 4 — Results Analysis & Visualization
anthropics
Aperivue
mims-harvard
neuromechanist
arjunrajlaboratory