data_analysis
High-performance data analysis using Polars - load, transform, aggregate, visualize and…
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data);
$ npx -y skills add melandlabs/openloomi --skill xlsx --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/xlsxContext preview
The summary Claude sees to decide when to auto-load this skill.
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data);
name: xlsx description: 'Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.' license: Proprietary. LICENSE.txt has complete terms
Unless otherwise stated by the user or existing template
A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks.
**LibreOffice Required for Formula Recalculation**: You can assume LibreOffice is installed for recalculating formula values using the `scripts/recalc.py` script. The script automatically configures LibreOffice on first run, including in sandboxed environments where Unix sockets are restricted (handled by `scripts/office/soffice.py`)
For data analysis, visualization, and basic operations, use **pandas** which provides powerful data manipulation capabilities:
import pandas as pd
# Read Excel
df = pd.read_excel('file.xlsx') # Default: first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # All sheets as dict
# Analyze
df.head() # Preview data
df.info() # Column info
df.describe() # Statistics
# Write Excel
df.to_excel('output.xlsx', index=False)**Always use Excel formulas instead of calculating values in Python and hardcoding them.** This ensures the spreadsheet remains dynamic and updateable.
# Bad: Calculating in Python and hardcoding result total = df['Sales'].sum() sheet['B10'] = total # Hardcodes 5000 # Bad: Computing growth rate in Python growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue'] sheet['C5'] = growth # Hardcodes 0.15 # Bad: Python calculation for average avg = sum(values) / len(values) sheet['D20'] = avg # Hardcodes 42.5
# Good: Let Excel calculate the sum sheet['B10'] = '=SUM(B2:B9)' # Good: Growth rate as Excel formula sheet['C5'] = '=(C4-C2)/C2' # Good: Average using Excel function sheet['D20'] = '=AVERAGE(D2:D19)'
This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.
1. **Choose tool**: pandas for data, openpyxl for formulas/formatting 2. **Create/Load**: Cr
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Repo: melandlabs/openloomi
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