Create, edit, and analyze Excel spreadsheets with formulas, formatting, and visualizations.
npx skills add https://github.com/botassembly/jn --skill xlsx-botassembly
Please help me install this Agent Skill. Skill: xlsx Source: https://github.com/botassembly/jn/tree/main/demos/markdown-skills/data/xlsx Command: npx skills add https://github.com/botassembly/jn --skill xlsx-botassembly
This Skill enforces Excel modeling discipline: zero formula errors, template preservation, color/number conventions, and thorough documentation for hardcoded values.
Open the workbook and enforce the guidelines; when formulas update, run recalc.py to recompute values.
High-intent search queries and answers about installing and using this skill.
Create spreadsheets by enforcing modeling discipline: apply color coding for hard inputs, calculations, internal links, and external links; use strict number formatting conventions; document all hardcoded values; and run recalc.py for LibreOffice-based formula recalculation to eliminate errors.
Update templates by preserving the existing formula structure, applying consistent color and number formatting standards, documenting any hardcoded changes, and running recalc.py to recompute all dependent values across the workbook.
Apply color coding to distinguish hard inputs, calculations, internal links, and external references; format years as text, use currency formats, and display negative numbers in parentheses; document conventions so models remain auditable and maintainable.
Yes. Read, analyze, and generate data visualizations across financial models, dashboards, and scenario analyses while maintaining formula accuracy through recalc.py and enforcing consistent formatting and color conventions throughout.
After editing formulas, run recalc.py to trigger LibreOffice-based recalculation, ensuring all dependent cells update correctly and formula errors are caught before the model is used in analysis or reporting.
Documenting hardcoded values prevents formula errors, enables auditing, reduces calculation mistakes, and ensures other users understand model assumptions and can update scenarios accurately without breaking dependent calculations.
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