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name: test-coverage-improver
description: 'Improve test coverage in the OpenAI Agents Python repository: run

test-coverage-improver
by openai
test-coverage-improverは、機械学習とAI開発のためのスキルです。モデル構築から運用まで、包括的で効率的なAI開発ワークフローをサポートします。
⭐ 18,468🍴 3,107📅 2026年1月23日
SKILL.md
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.
Quick Start
- From the repo root run
make coverageto regenerate.coveragedata andcoverage.xml. - Collect artifacts:
.coverageandcoverage.xml, plus the console output fromcoverage report -mfor drill-downs. - Summarize coverage: total percentages, lowest files, and uncovered lines/paths.
- Draft test ideas per file: scenario, behavior under test, expected outcome, and likely coverage gain.
- Ask the user for approval to implement the proposed tests; pause until they agree.
- After approval, write the tests in
tests/, rerunmake coverage, and then run$code-change-verificationbefore marking work complete.
Workflow Details
- Run coverage: Execute
make coverageat repo root. Avoid watch flags and keep prior coverage artifacts only if comparing trends. - Parse summaries efficiently:
- Prefer the console output from
coverage report -mfor file-level totals; fallback tocoverage.xmlfor tooling or spreadsheets. - Use
uv run coverage htmlto generatehtmlcov/index.htmlif you need an interactive drill-down.
- Prefer the console output from
- 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).
- Public APIs or shared utilities in
- 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/, orassets/unless needed later. - If coverage artifacts are missing or stale, rerun
pnpm test:coverageinstead of guessing.
スコア
総合スコア
80/100
リポジトリの品質指標に基づく評価
✓SKILL.md
SKILL.mdファイルが含まれている
+20
✓LICENSE
ライセンスが設定されている
+10
○説明文
100文字以上の説明がある
0/10
✓人気
GitHub Stars 1000以上
+15
✓最近の活動
1ヶ月以内に更新
+10
✓フォーク
10回以上フォークされている
+5
○Issue管理
オープンIssueが50未満
0/5
✓言語
プログラミング言語が設定されている
+5
✓タグ
1つ以上のタグが設定されている
+5
レビュー
💬
レビュー機能は近日公開予定です
