What testing frameworks does this skill support?

This skill is optimized for environments using scripts/run-tests and the riot version management tool, commonly used in complex Python projects to manage multi-environment testing.

Does it handle external dependencies like databases?

Yes, the skill automatically manages Docker services required for specific test suites, starting services like Redis or Postgres before tests run and stopping them afterward.

How does it help with debugging failing tests?

When a test fails, the skill identifies the specific environment responsible, provides detailed logs, and can re-run specific failing tests with increased verbosity (-vv) to isolate the issue.

Does this skill run every test in the repository every time?

No, the skill is designed for efficiency. It analyzes your specific code changes and selects the minimal set of test suites and virtual environments required to validate those changes.

Smart Test Suite Runner

Name: Smart Test Suite Runner
Author: Microck

byMicrock

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Security & Testing

Validates code changes by intelligently selecting and executing the minimal necessary test suites and virtual environments.

About

The Smart Test Suite Runner is a specialized Claude Code skill designed to optimize the development lifecycle by automating test discovery and execution. Instead of running exhaustive, time-consuming test batteries, this skill analyzes modified files to identify relevant test suites, selects the most appropriate Python virtual environments (venvs) via riot, and manages required Docker services automatically. It provides a systematic approach to validation, supporting everything from core library changes to specific third-party integrations, ensuring high code quality with minimal computational overhead.

Key Features

Minimal venv selection logic to optimize iteration speed and resource usage
Intelligent discovery of affected test suites based on git status and file changes
Support for dry-run execution to preview validation plans
Granular test filtering using pytest arguments for focused debugging
81 GitHub stars
Automatic lifecycle management for Docker-based test dependencies

Use Cases

Verifying bug fixes in specific library integrations like Flask or Django without running unrelated tests
Validating core architectural changes across a strategic subset of Python versions
Smoke testing changes made to testing infrastructure or configuration files

About

Key Features

Minimal venv selection logic to optimize iteration speed and resource usage
Intelligent discovery of affected test suites based on git status and file changes
Support for dry-run execution to preview validation plans
Granular test filtering using pytest arguments for focused debugging
81 GitHub stars
Automatic lifecycle management for Docker-based test dependencies

Use Cases

Verifying bug fixes in specific library integrations like Flask or Django without running unrelated tests
Validating core architectural changes across a strategic subset of Python versions
Smoke testing changes made to testing infrastructure or configuration files