reporting

Generate publication-quality figures and markdown reports for biomedical ML experiments. Use when: (1) Creating heatmaps, forest plots, radar charts, or sensitivity-specificity scatter plots, (2) Plotting ROC curves, precision-recall curves, or Kaplan-Meier survival curves, (3) Writing structured experiment reports, (4) Exporting results to LaTeX tables for paper submission, (5) Producing figures meeting publication DPI and formatting standards.

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Repositório: 1nslyn/biomed-agent-kit
Última atividade na origem: 16 de março de 2026 às 07:15
Idioma detectado do SKILL.md: inglês
Estrelas: 1
Forks: 1

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npx skills add https://github.com/1nslyn/biomed-agent-kit --skill reporting

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4 arquivos

SKILL.md

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clinical-nlp

Process and analyze biomedical text data (clinical notes, radiology reports, PubMed abstracts). Use when: (1) Loading biomedical language models (PubMedBERT, ClinicalBERT, BioBERT, BioGPT) and extracting embeddings, (2) Named entity recognition for medical concepts with scispaCy, (3) UMLS concept linking, (4) Text classification of clinical documents, (5) Extracting structured data from unstructured clinical text (vitals, lab values, findings).

2026-03-16

commit

Smart granular commit workflow that groups changes into separate, logically-grouped commits by feature, functionality, or type. Never commits everything at once. No co-author line. Uses Conventional Commits. Use when user says commit, /commit, or commit my changes, or asks to save/checkpoint work, or at the end of a task when changes need to be committed.

2026-03-16

data-preprocessing

Load and preprocess biomedical data (DICOM, NIfTI, whole-slide images, tabular clinical data). Use when: (1) Loading DICOM/NIfTI/WSI files and extracting metadata, (2) Intensity normalization for CT/MRI, (3) Stain normalization for histopathology, (4) Building PyTorch Dataset classes with caching or HDF5, (5) Designing augmentation pipelines for medical imaging (2D or 3D).

2026-03-16

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name

reporting

description

Scientific Reporting

Workflow

Generating a report involves these steps:

Choose figure types -- based on what dimensions you're comparing
Generate figures -- at appropriate DPI and formatting
Export tables -- to markdown and/or LaTeX
Write report -- structured markdown with figures and tables
Save to dated directory -- docs/<YYYYMMDD>/ to prevent overwrites

Decision Tree

What are you visualizing?

Performance across 2 dimensions (e.g., model x feature set) → Heatmap. See reporting-patterns.md
Point estimates with confidence intervals → Forest plot. See reporting-patterns.md
Multiple metrics for top configurations → Radar/spider plot. See reporting-patterns.md
Sensitivity vs specificity trade-off → Scatter plot. See reporting-patterns.md
Model discrimination (classification) → ROC curve or PR curve. See curve-plots.md
Survival analysis → Kaplan-Meier curve with log-rank test. See curve-plots.md
Results for paper submission → LaTeX table. See latex-tables.md

What is the target venue?

Internal review → DPI 150, PNG format
Journal/conference submission → DPI 300+, PDF/SVG format
ASK the user about target venue and formatting requirements

ASK the user before starting:

What figures do they need?
Target venue (determines DPI, formatting)?
Color-blind-friendly palette needed?
What file format (PNG, PDF, SVG)?

References

File	Read When
references/reporting-patterns.md	Heatmaps, forest plots, radar/spider charts, sensitivity-specificity scatter, dated output directories, markdown report template, figure quality guidelines
references/curve-plots.md	ROC curves (single and with bootstrap CI), precision-recall curves, Kaplan-Meier survival curves with log-rank test
references/latex-tables.md	Exporting results to LaTeX tables with booktabs formatting, standalone preview files

reporting

Informações da origem

Opções de instalação

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Mais deste repositório

Mais deste repositório

Scientific Reporting

Workflow

Decision Tree

References

Scientific Reporting

Workflow

Decision Tree

References