test-coverage-improver▌
openai/openai-agents-python · updated Apr 8, 2026
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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.
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.
How to use test-coverage-improver on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add test-coverage-improver
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches test-coverage-improver from GitHub repository openai/openai-agents-python and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate test-coverage-improver. Access the skill through slash commands (e.g., /test-coverage-improver) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★67 reviews- ★★★★★Kwame Singh· Dec 24, 2024
Registry listing for test-coverage-improver matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ganesh Mohane· Dec 16, 2024
Registry listing for test-coverage-improver matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mia Taylor· Dec 8, 2024
Keeps context tight: test-coverage-improver is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Hana Gonzalez· Dec 8, 2024
Solid pick for teams standardizing on skills: test-coverage-improver is focused, and the summary matches what you get after install.
- ★★★★★Kiara Garcia· Dec 8, 2024
test-coverage-improver fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chinedu Srinivasan· Nov 27, 2024
Registry listing for test-coverage-improver matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chinedu Singh· Nov 27, 2024
test-coverage-improver has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Lucas Sanchez· Nov 27, 2024
test-coverage-improver is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ishan Verma· Nov 15, 2024
Keeps context tight: test-coverage-improver is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sakshi Patil· Nov 7, 2024
Keeps context tight: test-coverage-improver is the kind of skill you can hand to a new teammate without a long onboarding doc.
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