name: test-coverage-improver description: 'Improve test coverage in the OpenAI Agents Python repository: run make coverage, inspect coverage artifacts, identify low-coverage files, propose high-impact tests, and confirm with the user before writing tests.'

Test Coverage Improver

Overview

Use this skill whenever coverage needs assessment or improvement (coverage regressions, failing thresholds, or user requests for stronger tests). It runs the coverage suite, analyzes results, highlights the biggest gaps, and prepares test additions while confirming with the user before changing code.

Workflow Details

• Run coverage: Execute make coverage at repo root. Avoid watch flags and keep prior coverage artifacts only if comparing trends.
• Parse summaries efficiently:
  • Prefer the console output from coverage report -m for file-level totals; fallback to coverage.xml for tooling or spreadsheets.
  • Use uv run coverage html to generate htmlcov/index.html if you need an interactive drill-down.
• Prioritize targets:
  • Public APIs or shared utilities in src/agents/ before examples or docs.
  • Files with low statement coverage or newly added code at 0%.
  • Recent bug fixes or risky code paths (error handling, retries, timeouts, concurrency).
• Design impactful tests:
  • Hit uncovered paths: error cases, boundary inputs, optional flags, and cancellation/timeouts.
  • Cover combinational logic rather than trivial happy paths.
  • Place tests under tests/ and avoid flaky async timing.
• Coordinate with the user: Present a numbered, concise list of proposed test additions and expected coverage gains. Ask explicitly before editing code or fixtures.
• After implementation: Rerun coverage, report the updated summary, and note any remaining low-coverage areas.

Notes

• Keep any added comments or code in English.
• Do not create scripts/, references/, or assets/ unless needed later.
• If coverage artifacts are missing or stale, rerun pnpm test:coverage instead of guessing.