xlsx-to-python-core-principle-excel-values-test-data

Sub-skill of xlsx-to-python: Core Principle: Excel Values = Test Data.

5 stars

Best use case

xlsx-to-python-core-principle-excel-values-test-data is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of xlsx-to-python: Core Principle: Excel Values = Test Data.

Teams using xlsx-to-python-core-principle-excel-values-test-data 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

$curl -o ~/.claude/skills/core-principle-excel-values-test-data/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/data/office/xlsx-to-python/core-principle-excel-values-test-data/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/core-principle-excel-values-test-data/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How xlsx-to-python-core-principle-excel-values-test-data Compares

Feature / Agentxlsx-to-python-core-principle-excel-values-test-dataStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Sub-skill of xlsx-to-python: Core Principle: Excel Values = Test Data.

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.

Related Guides

SKILL.md Source

# Core Principle: Excel Values = Test Data

## Core Principle: Excel Values = Test Data


When an Excel spreadsheet contains a calculation with input values and computed
results, **those cell values are the test cases**. The two-pass extraction strategy:

- **Pass 1** (`data_only=True`): read computed cell values — these become `pytest.approx()` assertions
- **Pass 2** (`data_only=False`): read formula strings — these become Python function implementations

This means every spreadsheet with formulas is simultaneously a specification,
an implementation reference, AND a test fixture.

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