testing-python

Create and correct Python tests from story specifications.

1|Updated Jan 6, 2026
One-click install
npx skills add https://github.com/outcomeeng/claude --skill testing-python-outcomeeng
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: testing-python
Source: https://github.com/outcomeeng/claude/tree/main/plugins/python/skills/testing-python
Command: npx skills add https://github.com/outcomeeng/claude --skill testing-python-outcomeeng

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Writing and maintaining Python tests is error-prone; this skill ensures tests are created and fixed as part of story development, preventing silent test gaps.

Core Features & Use Cases

  • WRITE mode creates new tests from a story specification and related context.
  • FIX mode reads reviewer feedback, patches failing tests, and re-runs tests.
  • Supports property-based tests for parsers/serializers using Hypothesis.
  • Enforces type hints, explicit constants, and dependency-injection patterns to avoid mocks.

Quick Start

Provide the path to a story spec to begin writing or fixing Python tests.

Frequently Asked Questions about testing-python

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

FAQPage Schema
How do I automate writing Python tests from a story specification?

You can automate Python test creation by providing a path to a story specification, triggering WRITE mode to generate new tests while enforcing typing, test structure standards, and property-based testing.

What is the best way to fix failing pytest tests and address reviewer feedback?

The best way to fix failing pytest tests is using a FIX mode that reads reviewer feedback, patches the failing tests, and re-runs the tests to verify corrections against the story specification.

Does property-based testing with Hypothesis work for Python parsers and serializers?

Property-based testing with Hypothesis works for Python parsers and serializers by generating test cases that validate structural invariants, supported within standard test guidelines for story development.

How do I structure Python unit tests to avoid mocks and enforce dependency injection?

Structuring Python unit tests to avoid mocks involves enforcing type hints, explicit constants, and dependency-injection patterns within the test generation process to ensure tests are created and fixed correctly.

Why do my Python tests have silent gaps during story development?

Python tests develop silent gaps during story development when test creation and correction are not automated, allowing errors in test structure, typing, and property-based testing standards to go unnoticed.