Agent skill
test-coverage-improver
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.
Install this agent skill to your Project
npx add-skill https://github.com/openai/openai-agents-python/tree/main/.agents/skills/test-coverage-improver
SKILL.md
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.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
openai-knowledge
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.
final-release-review
Perform a release-readiness review by locating the previous release tag from remote tags and auditing the diff (e.g., v1.2.3...<commit>) for breaking changes, regressions, improvement opportunities, and risks before releasing openai-agents-python.
examples-auto-run
Run python examples in auto mode with logging, rerun helpers, and background control.
implementation-strategy
Decide how to implement runtime and API changes in openai-agents-python before editing code. Use when a task changes exported APIs, runtime behavior, serialized state, tests, or docs and you need to choose the compatibility boundary, whether shims or migrations are warranted, and when unreleased interfaces can be rewritten directly.
docs-sync
Analyze main branch implementation and configuration to find missing, incorrect, or outdated documentation in docs/. Use when asked to audit doc coverage, sync docs with code, or propose doc updates/structure changes. Only update English docs under docs/** and never touch translated docs under docs/ja, docs/ko, or docs/zh. Provide a report and ask for approval before editing docs.
runtime-behavior-probe
Plan and execute runtime-behavior investigations with temporary probe scripts, validation matrices, state controls, and findings-first reports. Use only when the user explicitly invokes this skill to verify actual runtime behavior beyond normal code-level checks, especially to uncover edge cases, undocumented behavior, or common failure modes in local or live integrations. A baseline smoke check is fine as an entry point, but do not stop at happy-path confirmation.
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