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Found 36 Skills
Next-generation test runner for Rust with parallel execution, advanced filtering, and CI integration. Use when running tests, configuring test execution, setting up CI pipelines, or optimizing test performance. Trigger terms: nextest, test runner, parallel tests, test filtering, test performance, flaky tests, CI testing.
Fixes flaky tests by replacing arbitrary timeouts with condition polling. Use when tests fail intermittently, have setTimeout delays, or involve async operations that need proper wait conditions.
CI/CD: GitHub Actions workflows, parallel sharding, flaky quarantine, junit XML/Allure, coverage gates
Use after creating PR - monitor CI pipeline, resolve failures cyclically until green or issue is identified as unresolvable
QCSD Verification phase swarm for CI/CD pipeline quality gates using regression analysis, flaky test detection, quality gate enforcement, and deployment readiness assessment. Consumes Development outputs (SHIP/CONDITIONAL/HOLD decisions, quality metrics) and produces signals for Production monitoring.
Test planning and coverage strategy workflow. Use when designing test suites, establishing coverage targets, planning TDD approaches, or assessing testing gaps in existing codebases.
Deterministic CI/CD interaction patterns. Push-and-wait discipline, failure triage, self-healing for lint/format/infra failures, structured output for pipeline consumption. Activate when interacting with CI/CD systems.
Fix failing or flaky Playwright tests. Use when user says "fix test", "flaky test", "test failing", "debug test", "test broken", "test passes sometimes", or "intermittent failure".
Compares a single test case's behavior across two branches, analyzing pass/fail status, duration, flakiness, and failure details. Useful for investigating test regressions introduced by a feature branch.
Playwright testing best practices for Next.js applications (formerly test-playwright). This skill should be used when writing, reviewing, or debugging E2E tests with Playwright. Triggers on tasks involving test selectors, flaky tests, authentication state, API mocking, hydration testing, parallel execution, CI configuration, or debugging test failures.
Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.
Identify and fix common testing mistakes across unit, integration, and E2E test suites. Use when tests are flaky, brittle, over-mocked, order-dependent, slow, poorly named, or providing false confidence. Use for "test smell", "fragile test", "flaky test", "over-mocking", "test anti-pattern", or "skipped tests". Do NOT use for writing new tests from scratch (use test-driven-development), refactoring architecture (use systematic-refactoring), or performance profiling without a specific test quality symptom.