xlsx-to-python-checklist
Sub-skill of xlsx-to-python: Checklist.
Best use case
xlsx-to-python-checklist is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of xlsx-to-python: Checklist.
Teams using xlsx-to-python-checklist 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/checklist/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How xlsx-to-python-checklist Compares
| Feature / Agent | xlsx-to-python-checklist | 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: Checklist.
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
# Checklist ## Checklist - [ ] Dual-pass load (values + formulas) — verify cached values exist - [ ] All formula cells extracted with both formula text and computed value - [ ] Named ranges mapped to variable names - [ ] Dependency graph built and classified (inputs/intermediates/outputs) - [ ] Calculation blocks identified (vs data/lookup tables) - [ ] **Row patterns detected** — normalize formulas via `Translator`, group by pattern - [ ] **Loop collapse** — repeated patterns (≥3 rows) emit Python loops, not per-cell code - [ ] **Formula → Python translation** — simple arithmetic auto-translated; complex via `formulas` lib - [ ] Baseline tests generated using Excel cell values as `pytest.approx()` assertions - [ ] 10 parametric variations generated per calculation - [ ] Parametric tests verify at minimum: result is finite, within physical bounds - [ ] Dark-intelligence archive YAML produced with `legal_scan_passed: false` - [ ] Legal scan passed — set `legal_scan_passed: true` - [ ] Calc-report YAML generated from archive
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