Can I use this with any programming language?

Yes. Since the framework relies on reading from stdin and writing to stdout, the 'run' executable can be written in Python, Bash, JavaScript, Go, or any other language.

What is differential testing in the tc framework?

Differential testing in tc compares the actual JSON output of a script directly against an expected JSON file, focusing on data results through a semantic diff rather than traditional code-based assertions.

How does tc handle dynamic values like timestamps or UUIDs?

The framework supports built-in pattern matching using tokens like , , , and in the expected.json file to validate dynamic content.

Why is this approach considered AI-friendly?

It separates the 'what' (specifications) from the 'how' (implementation), allowing AI models to easily generate test data and verify outcomes without needing to navigate complex, language-specific assertion libraries.

Theodore Calvin Testing (tc)

Name: Theodore Calvin Testing (tc)
Author: a3lem

bya3lem

0•

安全与测试

Automates language-agnostic differential testing by comparing JSON outputs against specifications.

Theodore Calvin Testing (tc) implements a robust differential testing workflow that prioritizes specifications over implementation details. By treating tests as language-independent scripts that process JSON inputs and produce JSON outputs, it allows developers to validate behavior across different languages and frameworks using simple semantic diffing. This skill helps Claude assist you in creating test suites, reasoning about complex scenarios, and debugging failures within the tc framework, making it an ideal choice for AI-driven development where code implementations iterate rapidly while core specifications remain constant.

主要功能

01Automated test suite scaffolding and scaffold management

02Deep, semantic JSON comparison with order-invariant object matching

03Language-agnostic testing using standard JSON input/output pipes

040 GitHub stars

05Dynamic pattern matching for UUIDs, timestamps, and variable strings

06Implementation-independent validation perfect for AI-generated code

使用场景

01Testing complex API responses and data pipelines with dynamic fields

02Validating logic consistency during cross-language service rewrites

03Creating AI-friendly test specifications for rapid application prototyping

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add a3lem/my-claude-plugins theo-calvin-testing

For use in Claude.ai and ChatGPT

主要功能

01Automated test suite scaffolding and scaffold management

02Deep, semantic JSON comparison with order-invariant object matching

03Language-agnostic testing using standard JSON input/output pipes

040 GitHub stars

05Dynamic pattern matching for UUIDs, timestamps, and variable strings

06Implementation-independent validation perfect for AI-generated code

使用场景

01Testing complex API responses and data pipelines with dynamic fields

02Validating logic consistency during cross-language service rewrites

03Creating AI-friendly test specifications for rapid application prototyping

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add a3lem/my-claude-plugins theo-calvin-testing

For use in Claude.ai and ChatGPT