reporting/jupyter-reports/SKILL.md
Creates reproducible Jupyter notebooks for bioinformatics analysis with parameterization using papermill. Use when generating automated analysis reports, running notebook-based pipelines, or creating shareable computational notebooks.
npx skillsauth add GPTomics/bioSkills bio-reporting-jupyter-reportsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Reference examples tested with: jupyter 1.0+, papermill 2.5+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Generate reproducible analysis reports" -> Execute parameterized Jupyter notebooks programmatically and export as HTML/PDF reports.
papermill.execute_notebook(input, output, parameters={...})jupyter nbconvert --to html notebook.ipynbimport papermill as pm
# Execute notebook with parameters
pm.execute_notebook(
'analysis_template.ipynb',
'output_report.ipynb',
parameters={
'input_file': 'data/counts.csv',
'condition_col': 'treatment',
'fdr_threshold': 0.05
}
)
Mark a cell with the parameters tag in Jupyter:
# Parameters (tag this cell as "parameters")
input_file = 'default.csv'
output_dir = 'results/'
fdr_threshold = 0.05
import papermill as pm
from pathlib import Path
samples = ['sample1', 'sample2', 'sample3']
for sample in samples:
pm.execute_notebook(
'qc_template.ipynb',
f'reports/{sample}_qc.ipynb',
parameters={'sample_id': sample}
)
# Single notebook
jupyter nbconvert --to html report.ipynb
# With execution
jupyter nbconvert --execute --to html report.ipynb
# PDF (requires pandoc + LaTeX)
jupyter nbconvert --to pdf report.ipynb
testing
Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.
data-ai
Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.
development
Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters.
development
Reconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories.