xlsx-to-python-using-formulas-for-complex-workbooks
Sub-skill of xlsx-to-python: Using `formulas` for Complex Workbooks.
Best use case
xlsx-to-python-using-formulas-for-complex-workbooks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of xlsx-to-python: Using `formulas` for Complex Workbooks.
Teams using xlsx-to-python-using-formulas-for-complex-workbooks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/using-formulas-for-complex-workbooks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How xlsx-to-python-using-formulas-for-complex-workbooks Compares
| Feature / Agent | xlsx-to-python-using-formulas-for-complex-workbooks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of xlsx-to-python: Using `formulas` for Complex Workbooks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Using `formulas` for Complex Workbooks
## Using `formulas` for Complex Workbooks
For spreadsheets with VLOOKUP, INDEX/MATCH, nested IF, or cross-sheet chains,
use the `formulas` library instead of manual parsing:
```python
import formulas
def compile_xlsx_to_function(filepath: str, input_range: str, output_range: str):
"""Compile an Excel workbook into a callable Python function."""
xl_model = formulas.ExcelModel().loads(filepath).finish()
# Get full dependency graph
solution = xl_model.calculate()
# Compile reusable function
func = xl_model.compile(
inputs=[input_range],
outputs=[output_range],
)
return func, solution
# Usage:
func, solution = compile_xlsx_to_function(
"pile_capacity.xlsx",
"'Inputs'!B2:B15",
"'Results'!C5:C10",
)
# The solution dict contains all computed values — use as test assertions
expected = solution["'Results'!C5"]
```
**Dependency graph visualization:**
```python
# Requires: uv add formulas[plot]
xl_model = formulas.ExcelModel().loads("calculation.xlsx").finish()
xl_model.calculate()
# Access the internal dispatcher for graph analysis
dsp = xl_model.dsp
dsp.plot() # matplotlib visualization
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