skill-testing-framework

Generate, execute, and verify test cases for AI skills.

49|7|Updated Oct 18, 2025
One-click install
npx skills add https://github.com/Exploration-labs/Nates-Substack-Skills --skill skill-testing-framework
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-testing-framework
Source: https://github.com/Exploration-labs/Nates-Substack-Skills/tree/main/skill-testing-framework
Command: npx skills add https://github.com/Exploration-labs/Nates-Substack-Skills --skill skill-testing-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyYAML, and includes scripts (resource) and assets (resource) and references (resource) components.

What problem does it solve?

Ensuring AI skills function correctly and consistently across updates is challenging. This framework provides structured testing to prevent regressions, validate behavior, and maintain high-quality, reliable AI tools.

Core Features & Use Cases

  • Multi-Level Testing: Supports unit tests for individual components, integration tests for complete workflows, and regression tests against known baselines.
  • Test Case Generation: Automatically generates test templates based on skill structure, simplifying the initial setup and creation of test suites.
  • Output Validation: Compares actual outputs against expected results using various methods like exact match, substring containment, or regex patterns.
  • Baseline Management: Helps create, validate, and update baselines for robust regression testing, ensuring unintended changes are caught early.
  • Use Case: After updating your 'pdf-processor' skill, you can run its test suite to verify that all PDF extraction, merging, and form-filling functions still work as expected, and that new features integrate seamlessly without breaking existing functionality.

Quick Start

Generate a test template for the skill located at '/path/to/my-new-skill' and save it as 'my-skill-tests.json'.

Frequently Asked Questions about skill-testing-framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up automated testing for my AI skills?

Automated testing for AI skills uses a framework to generate test cases, execute them against your skill, and validate outputs. This framework supports unit, integration, and regression tests with JSON/YAML definitions and automated runners, helping catch regressions and ensure consistent behavior across updates.

What are the different types of output validation methods available?

Output validation methods include exact_match for identical results, contains for substring matching, pattern for regex-based validation, and structural_match for comparing data structure. These methods let you define test assertions at the right level of strictness for your skill's expected outputs.

Can I generate test cases automatically for my skill?

Yes, the framework automatically generates test templates based on your skill's structure, simplifying setup and reducing manual test creation. Generated templates use JSON/YAML format and can be customized for unit, integration, or regression testing scenarios.

How does baseline management work for regression testing?

Baseline management creates and maintains known-good outputs for your skill. When you update a skill, the framework compares new outputs against baselines using validate_test_results.py, catching unintended changes early and ensuring updates don't break existing functionality.

Does this framework work with workflow scripts and API interactions?

Yes, the testing framework applies to multiple skill types including scripts, workflows, and API interactions. It handles JSON/YAML test definitions across these components and provides structured validation methods suited to each integration pattern.

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