data-export-excel_skill

Overview

This skill exports bioinformatics analysis results, tables, and formatted reports to Excel files using openpyxl. It runs locally and is provider-agnostic, so it works with any LLM provider. The tool produces multi-sheet workbooks, applies styling and conditional formatting, and handles AnnData, DEG tables, and other pandas DataFrames.

How this skill works

The skill converts input data (AnnData .obs/.var, pandas DataFrames, numpy arrays) into workbook sheets using openpyxl and dataframe_to_rows. It applies optional styling: header colors, borders, column width autosizing, and conditional fills for values like log2FC or percent mitochondrial. The workbook is saved to a local path; for very large tables the workflow falls back to chunked exports or CSV.

When to use it

• Export AnnData observations (.obs) or variables (.var) to Excel
• Save and style DEG results for sharing or publication
• Build multi-sheet reports combining metadata, QC, and DEG tables
• Generate Excel-ready marker gene lists per cluster
• Highlight quality-control flags (high mito, low counts) with conditional colors

Best practices

• Always include clear column headers in the first row
• Convert numpy/pandas-specific dtypes to native Python types before writing
• Limit heavy styling for very large sheets to reduce file size
• Use absolute output paths and ensure the output directory exists
• For datasets >100k rows prefer CSV or chunked Excel exports

Example use cases

• Create a multi-sheet workbook with cell metadata, gene metadata, and DEG results for collaborator review
• Export QC metrics and highlight cells with percent_mito >20% for quick filtering
• Produce cluster-specific sheets listing marker genes for downstream validation
• Format DEG tables with color coding for up/down regulation and save a publication-ready table