How to Run Tests for Leonxlnx/taste-skill: Manual Verification Guide

The Leonxlnx/taste-skill repository contains no automated test suite, so verifying functionality requires manually inspecting Markdown skill definitions and using the skill.sh helper script or npx skills add CLI to confirm proper retrieval.

The Leonxlnx/taste-skill repository houses AI agent skill definitions written in Markdown rather than executable code, which explains why it lacks conventional testing infrastructure like Jest or Pytest. Since there are no automated tests to run for this repository, validation relies on manual techniques that confirm skill files are correctly formatted and accessible to AI agent platforms.

Why Automated Tests Do Not Exist in taste-skill

Unlike standard software projects containing package.json, pytest, or CI configurations, this repository stores declarative skill definitions in plain SKILL.md files. The codebase deliberately contains no test directories or assertion libraries because the skills are prompts meant for LLM consumption rather than executable programs. Consequently, commands like npm test return no results here.

Verifying Skills with the skill.sh Helper Script

The skill.sh file at the repository root provides the only executable validation mechanism through a Bash associative array that maps skill names to Markdown file paths.

To retrieve the path for a specific skill:

source ./skill.sh taste-skill

This outputs the file location:

skills/taste-skill/SKILL.md

To list all available skills defined in the associative array:

source ./skill.sh

This prints all skill keys, including taste-skill and taste-skill-v1, revealing which Markdown definitions are available in the skills/ directory.

Testing CLI Integration with npx skills

Validate that AI agent tools can fetch and parse the repository by using the official agent-skills CLI. This confirms the repository structure meets the expected specification for AI consumption.

npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend"

A successful execution proves the CLI located and processed skills/taste-skill/SKILL.md without parsing errors.

Manual Content Validation Checklist

Since no automated test runner exists, inspect the Markdown files directly to ensure quality:

• Review formatting constraints: Check CHANGELOG.md for rules like the em-dash ban and design-variance dials that govern skill syntax.
• Verify skill definitions: Open skills/taste-skill/SKILL.md and the legacy skills/taste-skill-v1/SKILL.md to ensure design directives align with your requirements.
• Test with LLMs: Feed the skill content to ChatGPT, Claude, or Codex and evaluate the generated output against expected behavior patterns documented in the skill files.

Summary

• The repository contains no automated tests—verification is manual only.
• Use source ./skill.sh <skill-name> to confirm skill file paths via the Bash helper.
• Execute npx skills add to test CLI compatibility and fetch capability.
• Inspect SKILL.md files manually and validate them against LLM outputs to ensure adherence to design constraints.

Frequently Asked Questions

Does Leonxlnx/taste-skill include automated tests?

No. The repository consists of Markdown-based skill definitions without any test framework configuration. You cannot run npm test, pytest, or similar commands because the project contains only declarative AI prompts in files like skills/taste-skill/SKILL.md.

How do I validate a skill file manually?

Open the SKILL.md file to verify it contains proper design directives, then use source ./skill.sh <skill-name> to confirm the file exists at the expected path. You can also test the skill by loading it into an AI agent and inspecting the generated results for adherence to documented constraints like the em-dash ban.

What does the skill.sh script do?

The skill.sh script implements an associative array that maps logical skill names to physical Markdown file paths within the skills/ directory. It serves as a path resolver to quickly locate skill definitions without manually browsing the folder structure.

Can I add automated testing to this repository?

Yes, though it requires building a custom harness that parses SKILL.md files, sends them to an LLM endpoint, and compares outputs against expected patterns. The repository does not provide testing infrastructure out-of-the-box, so any automated validation would need to be implemented separately.

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