How does this skill group different test failures?

The skill analyzes test output to group errors by type (such as ImportErrors or Assertions), by the specific module affected, and by identified root causes to solve multiple failures at once.

Why does it prioritize infrastructure fixes first?

Fixing infrastructure issues like missing dependencies or configuration errors often resolves a large volume of failures simultaneously, allowing the AI to focus on genuine logic bugs later.

Can I use this with any test runner?

Yes, while the workflow is optimized for common tools like pytest and make, the systematic logic is applicable to any environment where structured error output is available.

How does it prevent introducing new bugs?

The workflow mandates a focused test run after every individual fix and a final comprehensive suite run to ensure no regressions were introduced during the process.

Intelligent Test Fixer

Name: Intelligent Test Fixer
Author: Krosebrook

byKrosebrook

•

Seguridad y Pruebas

Systematically identifies, groups, and resolves failing tests using a smart prioritization strategy to restore suite health.

Acerca de

The Intelligent Test Fixer skill empowers Claude to handle complex test failures by moving beyond a simple trial-and-error approach. It implements a rigorous, structured workflow that begins by grouping failures by error type and root cause, ensuring that foundational infrastructure and API issues are resolved before addressing individual logic bugs. This systematic method minimizes redundant work, speeds up the path to a green test suite, and ensures that every fix is verified through targeted regression testing.

Características Principales

Incremental verification of fixes using focused testing
Regressions checking via final full-suite validation
Prioritized fix strategy (Infrastructure > API > Logic)
Smart error grouping by type and root cause
Automated failure analysis from test output
1 GitHub stars

Casos de Uso

Resolving persistent CI/CD pipeline failures across large monorepos
Restoring test suite health after major code refactors
Systematically cleaning up technical debt in legacy test suites

Acerca de

Características Principales

Incremental verification of fixes using focused testing
Regressions checking via final full-suite validation
Prioritized fix strategy (Infrastructure > API > Logic)
Smart error grouping by type and root cause
Automated failure analysis from test output
1 GitHub stars

Casos de Uso

Resolving persistent CI/CD pipeline failures across large monorepos
Restoring test suite health after major code refactors
Systematically cleaning up technical debt in legacy test suites