Which Python tools does this skill require?

It is designed to work with projects using 'make coverage' and the standard Python coverage library, though it can adapt to other workflows using 'uv' or 'pytest-cov' outputs.

How does this skill identify which tests to write?

It analyzes coverage artifacts like .coverage and coverage.xml to locate specific uncovered lines and branches, prioritizing high-impact areas like public APIs and complex logic over trivial paths.

Can it help with async or complex code paths?

Yes, the skill specifically targets complex logic like error handling, retries, and cancellation, while aiming to avoid common pitfalls like flaky async timing.

Does it automatically modify my test suite without permission?

No, the skill follows a strict coordination workflow where it presents a numbered list of proposed test additions and waits for your explicit approval before editing any files.

Python Test Coverage Optimizer

Name: Python Test Coverage Optimizer
Author: wahmd

bywahmd

Security & Testing

Increases repository test coverage by identifying logic gaps, proposing high-impact test cases, and automating the verification workflow.

About

The Test Coverage Optimizer skill streamlines the process of auditing and enhancing code quality within Python projects. It automates the execution of coverage suites, parses complex reports to find untested logic branches, and prioritizes critical areas such as public APIs and error-handling paths. By generating structured test proposals and requiring user confirmation before implementation, it ensures that all new tests are meaningful, stable, and aligned with project standards, ultimately helping developers meet strict CI/CD coverage thresholds with minimal manual effort.

Key Features

Post-implementation verification to confirm coverage gains and prevent regressions
Automated coverage reporting and analysis using standard Python coverage tools
Prioritized test design focusing on public APIs and mission-critical utilities
0 GitHub stars
Interactive approval workflow to ensure proposed tests meet developer requirements
Intelligent identification of uncovered code paths, boundary cases, and logic gaps

Use Cases

Improving coverage for mission-critical components like retries, timeouts, and concurrency
Meeting mandatory coverage thresholds required for production deployment or CI/CD
Identifying and fixing coverage regressions after merging complex feature updates

About

Key Features

Post-implementation verification to confirm coverage gains and prevent regressions
Automated coverage reporting and analysis using standard Python coverage tools
Prioritized test design focusing on public APIs and mission-critical utilities
0 GitHub stars
Interactive approval workflow to ensure proposed tests meet developer requirements
Intelligent identification of uncovered code paths, boundary cases, and logic gaps

Use Cases

Improving coverage for mission-critical components like retries, timeouts, and concurrency
Meeting mandatory coverage thresholds required for production deployment or CI/CD
Identifying and fixing coverage regressions after merging complex feature updates