test-coverage-improver

To identify coverage gaps in a Python project, this workflow analyzes coverage artifacts like .coverage or coverage.xml files to surface under-tested public APIs and core utilities. It prioritizes high-leverage areas to maximize coverage improvements.

To propose high-impact tests for untested Python code paths, the workflow drafts concrete test ideas with scenarios, expectations, and impact estimates. It requires user approval before any coding changes are implemented.

Yes, you need existing coverage artifacts to generate test ideas. The Skill requires running make coverage to regenerate reports and collect artifacts before proposing tests for public APIs and core utilities.

Yes, you can integrate this into a CI pipeline to promote incremental quality improvements. The workflow provides a repeatable process to surface coverage gaps and propose tests, fitting naturally into automated quality checks.

The workflow for improving Python test coverage involves running make coverage, collecting artifacts, and drafting targeted test ideas. It awaits explicit user approval before writing any new tests to ensure guided implementation.