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Found 20 Skills
Review EXISTING test code for quality, smells, and testability issues. Detects test smells across six dimensions — readability, reliability, diagnostic value, design, AI-generated, and coverage — analyzes testability of application code, and backs the qualitative smells with mutation testing. Use when: "review my tests," "test quality audit," "test smells," "testability analysis," "are these tests any good." Not for: generating new tests — use `ai-test-generation`. Not for: testing AI features in your product — use `ai-system-testing`. Related: unit-testing, shift-left-testing, coverage-analysis, ai-test-generation.
Define, track, and act on QA metrics: test coverage percentage, flakiness rate, defect escape rate, MTTR, test execution time trends, automation ROI, quality gates, and SLAs for test suites. Includes metric formulas, realistic targets by company stage, and the action to take when each metric goes red. Use when: "QA metrics," "test metrics," "quality KPIs," "test health," "flakiness rate," "defect escape rate." Not for: building the dashboard UI (Allure/Grafana) — use qa-dashboard; measuring coverage gaps and mutation score — use coverage-analysis. Related: qa-dashboard, coverage-analysis, ci-cd-integration, release-readiness, quality-postmortem.
Write effective unit tests with Jest, Vitest, or pytest. Covers the test-doubles taxonomy (stub/spy/mock/fake), Arrange-Act-Assert, coverage threshold configuration and CI gating, snapshot testing, fake timers, and mutation testing with Stryker/mutmut. Use when: "unit test," "Jest," "Vitest," "pytest," "mock," "coverage threshold," "test doubles," "mutation testing," "fake timers," "snapshot test." Not for: interpreting coverage reports or finding coverage gaps — use coverage-analysis; AI generating the test code for you — use ai-test-generation; auditing existing tests for smells — use ai-qa-review; browser/component rendering assertions — use cypress-automation or visual-testing. Related: coverage-analysis, ci-cd-integration, ai-test-generation, shift-left-testing.
Audit a whole regression suite and prune/restructure it with evidence: per-test coverage fingerprinting, AST near-duplicate clustering, CI-history mining for never-failing and flaky tests, prune decision rules (redundant/obsolete/low-value/keep), smoke/core/extended tiering by risk and defect-detection history, and a defensible "what we deleted and why" record. Deletion is destructive — quarantine and human sign-off are mandatory. Use when: "audit the test suite," "prune redundant tests," "find duplicate tests," "which tests can we delete," "restructure into smoke/core/extended," "is this test pulling its weight," "shrink the regression suite." Not for: Judging whether an individual test is WELL-WRITTEN (smells, assertions) — that is ai-qa-review. Healing one flaky test at runtime — that is test-reliability. Bulk selector regeneration after a UI refactor — that is selector-drift-recovery. Related: ai-qa-review, coverage-analysis, test-reliability, risk-based-testing, qa-project-context.
Design CI/CD pipelines that run test suites. Covers GitHub Actions and GitLab CI templates, parallelism and sharding, artifact management, flaky-test quarantine, test-result publishing, coverage quality gates, OIDC keyless deploy, and copy-paste workflows for Playwright, Jest, and multi-stage pipelines. Use when: "CI/CD," "GitHub Actions," "pipeline," "test in CI," "GitLab CI," "continuous integration," "test automation pipeline," "shard tests in CI." Not for: per-test flaky healing at runtime — use test-reliability; go/no-go release decisions and smoke-test checklists — use release-readiness; test-result dashboards and trend reporting — use qa-metrics. Related: playwright-automation, qa-metrics, test-reliability, coverage-analysis, release-readiness.
Use when validating golden dataset quality. Runs schema checks, duplicate detection, and coverage analysis to ensure dataset integrity for AI evaluation.
cargo-fuzz is the de facto fuzzing tool for Rust projects using Cargo. Use for fuzzing Rust code with libFuzzer backend.
Use when reviewing or scoring AI-generated unit tests/UT code, especially when coverage, assertion effectiveness, or test quality is in question and a numeric score, risk level, or must-fix checklist is needed
Generate comprehensive, maintainable unit tests across languages with strong coverage and edge case focus.
Comprehensive QA testing orchestrator. Use when user says 'test', 'qa', 'check site', 'find bugs', 'helpmetest', provides a URL to test, or wants complete testing coverage from discovery through bug reporting. Discovers ALL pages, enumerates ALL features, tests comprehensively, reports exact metrics.
Testing expert with comprehensive knowledge of test structure, mocking strategies, async testing, coverage analysis, and cross-framework debugging. Use PROACTIVELY for test reliability, flaky test debugging, framework migration, and testing architecture decisions. Covers Jest, Vitest, Playwright, and Testing Library.
OSS-Fuzz provides free continuous fuzzing for open source projects. Use when setting up continuous fuzzing infrastructure or enrolling projects.