Loading...
Loading...
Found 17 Skills
Fixes flaky tests by analyzing failure patterns from Tuist test insights, identifying root causes, and applying targeted corrections. Can be invoked with a specific test case URL (e.g. `https://tuist.dev/{account}/{project}/tests/test-cases/{id}`) or without arguments to discover and fix all flaky tests in the project.
Diagnose and fix tests that pass in isolation but fail when run concurrently. Covers shared state isolation and resource conflicts. References condition-based-waiting for timing issues.
Fixes a specific flaky test by analyzing its failure patterns from Tuist, identifying the root cause, and applying a targeted correction. Typically invoked with a Tuist test case URL in the format such as `https://tuist.dev/{account}/{project}/tests/test-cases/{id}`.
Diagnoses and fixes flaky tests by identifying root causes (timing issues, shared state, randomness, network dependencies) and provides stabilization strategies. Use for "flaky tests", "test stability", "intermittent failures", or "test debugging".
Review Argos visual regression builds as one input to a pull request review. Use when a PR has an Argos build link, an Argos status check, or a bot comment pointing to an Argos build, and you need to decide whether the visual diffs match the developer's intent before approving.
Use this skill when deciding what to test, choosing between test types, designing a testing strategy, or balancing test coverage. Triggers on test pyramid, unit vs integration vs e2e, contract testing, test coverage strategy, TDD, BDD, testing ROI, and any task requiring testing architecture decisions.
World-class QA engineering - systematic testing, automation, and the mindset that finds bugs before users doUse when "QA, quality assurance, testing, test automation, e2e tests, integration tests, regression testing, test coverage, playwright, cypress, selenium, test suite, bug report, test strategy, flaky tests, testing, QA, automation, e2e, integration, regression, quality" mentioned.
Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.
Detect test smells, overmocking, flaky tests, and coverage issues. Analyze test effectiveness, maintainability, and reliability. Use when reviewing tests or improving test quality.
Use when tests have race conditions, timing dependencies, or inconsistent pass/fail behavior - replaces arbitrary timeouts with condition polling to wait for actual state changes, eliminating flaky tests from timing guesses
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.
Use after creating PR - monitor CI pipeline, resolve failures cyclically until green or issue is identified as unresolvable