Analyze Python and JavaScript test suites for speed bottlenecks and DRY violations.
npx skills add https://github.com/bryonjacob/aug --skill test-optimization
Please help me install this Agent Skill. Skill: test-optimization Source: https://github.com/bryonjacob/aug/tree/main/aug-dev/skills/test-optimization Command: npx skills add https://github.com/bryonjacob/aug --skill test-optimization
This Skill analyzes test suites to identify speed bottlenecks, redundancy, and DRY violations, enabling faster feedback and maintainable tests.
Analyze a codebase's test suite to identify optimization opportunities and generate a prioritized plan for remediation.
High-intent search queries and answers about installing and using this skill.
To optimize a slow test suite, parse test results and source code to identify speed bottlenecks, proposing actionable changes like mocking and fixture reuse to accelerate execution.
Reduce redundancy and DRY violations in unit and integration tests by detecting overlapping or duplicate tests and highlighting opportunities to parameterize fixtures and helper functions. This suggests consolidating tests to maintain quality.
Yes, you can analyze both Python and JavaScript test suites to identify optimization opportunities across unit and integration tests. It parses test results, source code, and configurations to propose concrete changes for speed and maintainability.
The best way to propose parameterization and mocking for flaky tests is to parse test source code and configuration files to generate a prioritized remediation plan. This highlights fixture reuse and computation caching to clean up test suites.
Yes, test suite optimization works with vitest and pytest frameworks by analyzing test results and configurations to identify speed bottlenecks and DRY violations. It provides actionable suggestions for fixture reuse and parameterization across these environments.
Space-DinosaursMap source modules to tests and produce a ranked coverage gap report.
duanbiao2000Provide pytest testing patterns for Python projects with fixtures, mocking, and parameterization.
janewilkinRun pytest test suites and generate code coverage reports.
yangqiongAnalyze code execution metrics to identify and prioritize missing test cases.
