「tests」の検索結果

Refactor with tests first, one change at a time, never mix refactoring with bug fixes or new features

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。

Replace arbitrary timeouts with condition polling for reliable async 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。

Render module-scoped human-readable traceability for a single module — REQs, DESs, TESTs, code locations with anchor links, plus the module-dependency graph as a markdown edge-list.

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。

Generate a GitHub Actions workflow for Android — JDK + Gradle caching, build, unit + instrumented tests, Android Lint, run the Android pre-flight checks, and (optionally) sign + upload a signed app bundle to a Play track. Use when setting up CI for an Android app.

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。

Audit a repo's test, lint, type-check, static analysis, build, and debug infrastructure for AI coding agents. Generate scored reports and optimized configs for the lint-on-write hook. Triggers on audit tests, test harness, lint setup, check test infrastructure, entering a new repo.

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。

Coordinate SEO, LLM-readable site architecture, UX, content, monitoring, security, and external authority work as a task-plan-driven sequence. Use this skill when the user asks to plan, execute, validate, or route multi-skill website growth work; asks what to do next across semantic core, URL structure, internal linking, schema, llms.txt, UX journeys, analytics, citation monitoring, or white-hat link placement; or wants a central architect that preserves evidence, alarms, handoffs, and 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。

Builds, validates, packages, tests, and optimizes deployment-ready Skill files (SKILL.md + references/ + scripts/ + agents/) from design specifications. Three pre-build gates + 9-dimension quality gate (with D9a/b/c empirical evidence tiers) + behavioral iteration loop + automated description optimizer + blind version comparator. Use for building, reviewing, revising, testing, or optimizing Skills. Trigger on: "build this Skill," "review this Skill," "test my Skill," "optimize the description," "compare these Skill versions," "should this be a hook instead," "build a template first." Also: "my Skill doesn't activate," "my description truncates," "my SKILL.md is too long," "my Skill is overtriggering." Do NOT use when designing Skill methodology — that's design work, not packaging. Do NOT use for prompt compilation, project auditing, or prompt scoring (use rootnode-prompt-compilation, rootnode-project-audit, or rootnode-prompt-validation if available).

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。

Audit RuField tests, trust boundaries, privacy projections, and evidence promotion. Use for conformance review, security regression review, release validation, or checking whether field accuracy claims have support.

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。

Apply WIT (Writing Is Thinking) as a human–LLM collaborative scientific reasoning skill for scientific question formulation, finding-driven research planning, next-experiment selection, Results or Discussion review, claim–evidence and reviewer stress tests, manuscript logic audits, deadline closure, and researcher growth. Use when the user invokes WIT or asks for these question-driven scientific reasoning workflows; do not use for generic copyediting, summarization, or literature search alone.

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。

Post-implementation self-review. Use AFTER finishing ANY code implementation or change — before declaring it done, committing, or opening a PR — to critique your own work: re-read the diff adversarially, confirm unit tests cover every branch/edge, kill duplication, follow clean-code/SOLID and the repo's own docs & invariants, decide if something should become documentation, update/prune memory, and strip outdated comments. Trigger it whenever you've just written or modified code and are about to wrap up, even if the user didn't explicitly ask for a review.

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。

Curate tests then decompose dependencies

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。

Testing conventions and patterns. Use when writing or modifying tests, creating mocks, or using object mothers.

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。

Use when the user needs to design, run, or audit product or GTM analytics — metric hierarchies and north stars, funnels, cohort retention, churn, pipeline velocity, forecasting, lead scoring, NRR, A/B tests, or analytical SQL — or uploads a dataset to analyze. Triggers on questions like "why did activation drop", "is this metric meaningful", "review my dashboard", or "what should I measure". For writing the spec that defines a feature's success metrics, use product-spec.

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。

Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.

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。

Design and execute chaos engineering experiments using Litmus or Chaos Mesh. Test system resilience through controlled fault injection, validate hypothesis-driven tests, and improve failure recovery. Use before major product launches, after architecture changes to validate resilience, during GameDays or disaster recovery drills, to validate assumptions about failure modes, or as part of an SRE maturity program.

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。

Field identification of minerals and ores using hardness, streak, luster, cleavage, crystal habit, and simple chemical tests. Covers the systematic elimination methodology, Mohs scale application, and common ore indicators for precious metals, gemstones, and industrial minerals. Use when encountering an unknown rock or mineral specimen, when prospecting and assessing whether a site shows valuable mineral indicators, when distinguishing ore-bearing rock from barren rock in the field, or when building geological literacy through systematic observation.

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。

Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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。

Identify gemstones using optical properties, physical tests, and inclusion analysis. Covers refractive index, specific gravity, pleochroism, spectroscopy indicators, and common simulant detection. Use when identifying an unknown gemstone, verifying a seller's claim about species identity, distinguishing natural stones from simulants or synthetics, building gemological literacy through structured observation, or identifying rough material before cutting to ensure safe handling.

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。

Form a coherent gestalt — the whole that is more than the sum of its parts — from the panoramic perception produced by expand-awareness. Maps tensions and resonances between domains, identifies the emergent figure from the ground of multiple perspectives, tests the candidate whole for premature closure, and articulates the insight in a single sentence no single domain could have produced. Use after expand-awareness has surfaced raw multi-domain perception and before express-insight communicates the result.

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。

Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.

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。

Analyze prime numbers using primality tests, factorization algorithms, prime distribution analysis, and sieve methods. Covers trial division, Miller-Rabin, Sieve of Eratosthenes, and the Prime Number Theorem. Use when determining whether an integer is prime or composite, finding prime factorizations, counting or listing primes up to a bound, or investigating prime properties within a number-theoretic proof or computation.

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。

Testing Vue 3 SPAs: Vitest + @vue/test-utils, RTL-style alternatives, Pinia testing via createTestingPinia, composable testing, msw for network mocks, Cypress component testing, Playwright e2e. Use this skill to: - Set up Vitest for Vue 3 with jsdom + @vue/test-utils. - Pick mount vs shallowMount. - Test components by props/emits/slots contract. - Mock Pinia stores in tests. - Test composables in isolation. - Set up Playwright or Cypress for end-to-end. Do NOT use this skill for: - General Vue conventions (see vue-conventions). - State patterns (see vue-state-management). - Form-specific patterns (see vue-forms).

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。

Testing React SPAs: React Testing Library + Vitest/Jest for components and hooks, msw for network mocks, Playwright/Cypress for e2e. Query priority, user events, async assertions, mocking patterns. Use this skill to: - Pick the right runner (Vitest vs Jest) and setup. - Write component tests with RTL using accessible queries. - Test custom hooks via renderHook. - Mock network with msw at the boundary. - Set up Playwright or Cypress for end-to-end coverage. Do NOT use this skill for: - General React conventions (see react-conventions). - State management testing patterns specific to a store lib (see react-state-management). - Form-specific testing (apply react-forms patterns inside test).

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。

PHPUnit and Pest patterns for any PHP project: test structure, naming, data providers, test doubles (mocks/stubs/fakes), fixtures, and coverage targets. Stack-agnostic — referenced by every PHP plugin in the marketplace. Use this skill to: - Structure unit and integration tests with PHPUnit or Pest consistently. - Drive cases with data providers / datasets instead of copy-pasted test bodies. - Use test doubles with discipline — mock collaborators, not the system under test. - Set up and tear down fixtures cleanly and aim for a meaningful coverage target. Do NOT use this skill for: - PHP language idioms — see php-foundation:php-conventions. - Composer / autoloading — see php-foundation:composer-tooling. - Framework-specific test helpers (Laravel RefreshDatabase/actingAs, Symfony WebTestCase/KernelTestCase) — those are injected by the framework plugin's QA phase.

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。