How does the skill identify coverage gaps?

It runs the repository's coverage suite using 'make coverage' and parses the resulting artifacts, such as coverage.xml and console reports, to identify files and specific lines with low statement coverage.

Does this skill write tests automatically?

No, the skill follows a safety-first workflow where it proposes a list of test ideas and coverage gains, then pauses for explicit user approval before writing any code to the repository.

Which Python testing tools are supported?

The skill is optimized for coverage.py and standard Python testing workflows, specifically designed to integrate with tools like uv and make-based build systems.

Can it help with complex logic like async or error handling?

Yes, it specifically targets uncovered paths such as error cases, boundary inputs, cancellation, and timeouts to ensure robust combinational logic coverage.

Python Test Coverage Improver

Name: Python Test Coverage Improver
Author: openai

byopenai

•

18,501

Security & Testing

Analyzes code coverage gaps and automates the creation of high-impact unit tests for Python projects.

About

The Test Coverage Improver skill streamlines the quality assurance process by executing coverage suites, identifying untested logic, and proposing targeted test cases. It focuses on high-impact areas such as public APIs, error handling, and complex logic paths within the OpenAI Agents framework. By automating the analysis of coverage artifacts and requiring user confirmation before implementation, it ensures that your codebase reaches its testing thresholds efficiently and reliably.

Key Features

Detailed analysis of coverage.xml and console reports to find gaps
Automated execution of coverage suites via make and coverage.py
Post-implementation verification to confirm coverage gains
Structured test proposals with scenarios and expected outcomes
18,501 GitHub stars
Prioritization of public APIs and critical error-handling paths

Use Cases

Generating edge-case tests for complex error handling and timeouts
Identifying and fixing coverage regressions after a large refactor
Increasing test density for critical public-facing API modules

About

Key Features

Detailed analysis of coverage.xml and console reports to find gaps
Automated execution of coverage suites via make and coverage.py
Post-implementation verification to confirm coverage gains
Structured test proposals with scenarios and expected outcomes
18,501 GitHub stars
Prioritization of public APIs and critical error-handling paths

Use Cases

Generating edge-case tests for complex error handling and timeouts
Identifying and fixing coverage regressions after a large refactor
Increasing test density for critical public-facing API modules