What is the 'quick' test preset?

The quick preset uses fail-fast logic to stop at the first failure and excludes slow-running tests to provide immediate feedback.

How does the AI consultation phase help?

For complex failures, the skill triggers a mandatory consultation with external AI providers like Gemini to provide deeper diagnostic insights than standard error logs.

Which testing frameworks are supported?

This skill is specifically optimized for the Pytest framework, offering deep integration for unit and integration test suites.

How does it handle long-running tests?

The skill uses foreground execution with configurable timeouts to prevent background process polling and conversation clutter.

Can I run individual tests?

Yes, you can target specific test files or individual functions using the 'run' action with a targeted path parameter.

Pytest Runner & Debugger

Name: Pytest Runner & Debugger
Author: tylerburleigh

bytylerburleigh

安全与测试

Automates comprehensive Pytest execution and systematic debugging workflows for Python applications.

关于

The Pytest Runner & Debugger skill provides a structured 5-phase workflow for testing and error resolution using the Foundry MCP server. It enables developers to execute various test suites—including quick, unit, and integration tests—while providing a systematic framework for categorizing failures and forming debugging hypotheses. By integrating external AI consultation and leveraging specialized subagents for context gathering, this skill ensures that complex bugs are diagnosed accurately and verified through rigorous regression testing.

主要功能

Regression testing and verification strategies to ensure code stability
Hypothesis-driven failure categorization for rapid error diagnosis
Systematic 5-phase investigation workflow for efficient debugging
0 GitHub stars
External AI tool integration for consulting advanced models on complex failures
Automated Pytest execution with presets for quick, unit, and integration runs

使用场景

Automating fail-fast test cycles during active feature development
Debugging complex test failures that require external AI reasoning
Investigating flaky tests and shared state issues using specialized subagents

关于

主要功能

Regression testing and verification strategies to ensure code stability
Hypothesis-driven failure categorization for rapid error diagnosis
Systematic 5-phase investigation workflow for efficient debugging
0 GitHub stars
External AI tool integration for consulting advanced models on complex failures
Automated Pytest execution with presets for quick, unit, and integration runs

使用场景

Automating fail-fast test cycles during active feature development
Debugging complex test failures that require external AI reasoning
Investigating flaky tests and shared state issues using specialized subagents