「test」の検索結果

Provides patterns for unit testing `@ConfigurationProperties` classes with `@ConfigurationPropertiesTest`. Validates property binding, tests validation constraints, verifies default values, checks type conversions, and mocks property sources for Spring Boot configuration properties. Use when testing application configuration binding, validating YAML or application.properties files, verifying environment-specific settings, or testing nested property structures.

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Find consequential behavior that a passing test suite does not establish, using focused exploratory checks. Use when the user asks what green tests miss or wants confidence in test coverage for a specific change. Not a general code review, routine test run, or debugging workflow for an already failing test.

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Analyze test coverage gaps and generate tests to improve coverage. Use when improving test coverage, finding untested code, or writing missing tests.

日本語の概要は準備中です。原文の説明を表示しています。

Updates, generates, and validates tests using git-workspace context and TDD/BDD methodology. Use when code changes require new or updated test coverage.

日本語の概要は準備中です。原文の説明を表示しています。

An expert at creating and refining automated tests using TestDriver.ai

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Use AI coding agents and exploration mode to generate TestDriver tests

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An expert at creating and refining automated tests using TestDriver.ai

日本語の概要は準備中です。原文の説明を表示しています。

An expert at creating and refining automated tests using TestDriver.ai

日本語の概要は準備中です。原文の説明を表示しています。

The fastest way to get started with TestDriver. Just set your API key and start testing.

日本語の概要は準備中です。原文の説明を表示しています。

The fastest way to get started with TestDriver. Just set your API key and start testing.

日本語の概要は準備中です。原文の説明を表示しています。

Main app unit testing patterns (BaseUnitTest, BDD naming, mockito-kotlin, AssertJ, StateFlow/LiveData capture, coroutine dispatchers). Use when writing, editing, exploring, debugging, or reviewing unit tests in the store management app. For POS (WooPos*) tests, use the `pos-tests` skill instead.

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Guides audit follow-up and verification processes including follow-up scheduling, remediation effectiveness testing, finding closure criteria, re-testing procedures, status reporting, and escalation of unremediated findings. Implements IIA Standard 2500 monitoring requirements and ISO 19011 follow-up guidance for privacy audit engagements. Keywords: audit follow-up, verification testing, finding closure, remediation effectiveness, re-testing, follow-up audit.

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Use when writing tests of any kind — unit, slice, or integration. Covers test structure, naming conventions, Mockito patterns, @WebMvcTest, @DataJpaTest, and Testcontainers setup.

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Analyze failing tests to detect functional bugs in code. Takes repository and failing test output as input, analyzes execution behavior, assertions, and stack traces to identify suspicious code regions and root causes. Use when debugging test failures, investigating regression bugs, or understanding why tests fail. Explains the bug mechanism, identifies affected code, and suggests fixes based on test expectations vs actual behavior.

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Explains test failures and provides actionable debugging guidance. Use when tests fail (unit, integration, E2E), builds fail, or code throws errors. Analyzes error messages, stack traces, and test output to identify root causes and suggest concrete fixes. Handles pytest, jest, junit, mocha, vitest, selenium, cypress, playwright, and other testing frameworks across Python, JavaScript/TypeScript, Java, Go, and other languages.

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Generate targeted test inputs to reach specific code paths and hard-to-reach behaviors in Python code. Use when: (1) Targeting uncovered branches or specific execution paths, (2) Need coverage-guided test generation, (3) Want to leverage LLM understanding of code semantics for meaningful test inputs, (4) Testing boundary conditions and edge cases systematically, (5) Combining symbolic reasoning with fuzzing. Provides path analysis, constraint solving, coverage-guided strategies, and LLM-driven semantic generation for comprehensive test input creation.

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Detect test smells, overmocking, flaky tests, and coverage issues. Analyze test effectiveness, maintainability, and reliability. Use when reviewing tests or improving test quality.

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Modern TypeScript/JavaScript testing with Vitest. Fast unit and integration tests, native ESM support, Vite-powered HMR, and comprehensive mocking. Use for testing TS/JS projects.

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Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests, reproduce a user-visible regression, or convert a narrow workflow specification into stable web, API, CLI, desktop, or mobile interaction coverage. Do not use for broad exploratory journeys or agent-judged smoke tests.

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Flaky test fixes: detect nondeterministic tests empirically (repeat/shuffle/parallel runs), diagnose the root cause, fix it — never retry/skip/sleep — and verify across many randomized runs. Triggers on "absolute deflake", "fix flaky tests", "CI is flaky", "this test fails randomly/intermittently".

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Scheduled probes that run CONTINUOUSLY after release. Covers probe design for critical user journeys, alerting integration, SLA validation, multi-region monitoring, and the boundary between QA and SRE. Use when: "synthetic monitoring," "uptime testing," "scheduled probes," "SLA validation," "availability monitoring," "post-deploy checks." Not for: safe-release techniques during rollout — use testing-in-production. Not for: designing tests from prod telemetry — use observability-driven-testing. Not for: a one-shot post-deploy smoke gate tied to a release — use release-readiness. Related: testing-in-production, release-readiness, performance-testing, qa-metrics.

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Safe-release techniques DURING rollout: feature flags, progressive rollouts, canary analysis, guardrail metrics, production smoke tests, and synthetic users. Bridges QA and SRE practices. Use when: "feature flag testing," "canary deploy," "progressive rollout," "guardrail metrics," "dark launch," "safe rollout." Not for: scheduled probes that run continuously after release — use `synthetic-monitoring`. Not for: designing tests from prod telemetry — use `observability-driven-testing`. Related: release-readiness, synthetic-monitoring, observability-driven-testing, qa-metrics.

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Onboard a new QA engineer to an existing codebase, or audit an existing test architecture. Produces a 30-day ramp plan: codebase orientation, framework walkthrough, test architecture audit, mentorship pairing, and first-test guidance. Use when: "QA onboarding," "new tester," "ramp up," "test architecture audit," "first 30 days," "QA mentorship," "joining QA team." Not for: setting up QA on a brand-new project from scratch — use `qa-start`. Related: qa-start, qa-project-context, shift-left-testing, ai-qa-review.

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Systematically harvest short testimonials from customers via NPS, milestones, post-purchase, and email during PRD v1.0 Market Adoption. Triggers on requests to collect testimonials, set up social proof harvest, gather customer quotes, or when user asks "how do we get testimonials?", "social proof harvest", "NPS responses", "testimonial wall", "customer quotes", "post-purchase survey", "milestone follow-up". Outputs CFD-TST-* testimonial entries and GTM-TST-* placement assets.

日本語の概要は準備中です。原文の説明を表示しています。