「tests」の検索結果

Implement the minimal code needed to make failing tests pass in the TDD green 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。

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

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。

Scaffold Python projects with uv, package structure, type checking, and tests. Use when starting applications, services, or libraries with a consistent development setup.

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。

Investigate production issues, isolate root causes, implement focused fixes, and verify recovery with regression tests. Use when resolving failures across application or infrastructure components.

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。

Scaffold TypeScript applications with project structure, build tools, type checking, and tests. Use when starting a Node.js or frontend project or standardizing its development setup.

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。

Scaffold React and React Native components with TypeScript, accessibility, styles, and tests. Use when building reusable UI components or establishing component structure.

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。

Validate application configuration with schemas, environment checks, and runtime tests. Use when preventing configuration errors, checking deployment settings, or detecting configuration drift.

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 data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

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。

Master Bash Automated Testing System (Bats) for comprehensive shell script testing. Use when writing tests for shell scripts, CI/CD pipelines, or requiring test-driven development of shell utilities.

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。

Build and test the Morphia project using Maven. Use when compiling, running tests, or building artifacts.

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。

Premise, theme, controlling idea, subject selection and logline for scripts (前提/主题/立意/选材/戏核) — merged from Lajos Egri's premise-as-tyrant and dialectic, Robert McKee's controlling idea (value + cause) and idea/counter-idea, Neill Hicks' five premise questions, Lisa Cron's "third rail" (desire vs misbelief, origin scene, what-if), Julian Hoxter's logline and theme-attitude rule, Blake Snyder's logline/genre/title tests, and Lu Jun's Chinese craft chapters on 选材要严/开掘要深/视角要新/戏核要好/情节要奇. Use when turning a vague idea into a premise, finding what a story is really about, choosing a subject and angle, testing a logline, deciding the ending from the theme, or diagnosing a script that "has events but no story".

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。

Episode and season structure for TV series (剧集单集与季的结构) — teasers and cold opens, broadcast four/five/six-act grids with page anchors, three tests for finding the invisible acts in streaming scripts, act-out and cliffhanger taxonomies, Oberg's four information tools, A/B/C/runner weaving and scene-count ratios, the Calvisi pilot beat sheet, five-part scene structure, and season shape (tentpoles, bottle and container episodes, two-parters, finales). Merged from Calvisi, Douglas, Oberg, Landau, Goldberg & Rabkin, Miller, Rabkin and Blum, plus act lengths measured from published West Wing, Succession, Sopranos, Downton and Fleabag scripts. Use when breaking an episode into acts, deciding how many acts a script needs, placing act outs and cliffhangers, designing a teaser, weaving A/B/C stories and runners, beating out a one-hour pilot, auditing an episode that has no shape, reverse-engineering a show's structure, or planning a season's episode order (分幕、出幕、悬念结局、季弧、瓶子集).

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。

Live Borg debugging by exec-ing into the borg-web-ui Docker container. Use when debugging borg commands, writing tests against real borg output, developing borg 2.0 features, or verifying borg behavior before writing code.

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。

AI DevKit · Testing phase guidance for adding and validating feature test coverage. Use when the user wants to write tests, update testing docs, run coverage, close coverage gaps, or run dev-lifecycle phase 8.

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。

AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

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。

AI DevKit · Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.

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Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.

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 Gluten ANSI-mode test results (run dev/verify-ansi-expressions.sh, parse JSON tracker output, produce root-cause analysis and fix recommendations). Trigger on user requests like "analyze ANSI tests", "run ANSI matrix", "why is this ANSI test failing".

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。

Run GDScript test suites from the command line with `godot --headless`, using a SceneTree/MainLoop runner script that exits non-zero on failure so CI can gate merges. Use when a Godot project needs unit tests without opening the editor, when wiring a CI job (GitHub Actions or similar) that must fail the build on a failing `.gd` test, or when a `godot --headless` invocation hangs, opens a window, or exits 0 despite failed assertions.

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。

Performance, load, and reliability (SLO/SRE) planning for production readiness. Use when defining service level objectives, load characterization, capacity, latency budgets, stress/soak/spike test plans, false-positive baselines, and reliability targets. USE FOR: SLO/SLA definition, load testing plan, performance budget, capacity planning, reliability/SRE backlog, latency targets, error-budget policy. DO NOT USE FOR: executing load tests (use Azure Load Testing tooling), security threat modeling, RAI assessment, privacy/compliance planning, or authoring/restating PRD requirements (cite the PRD's existing NFR/FR ids instead).

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 LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.

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。

Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with unit tests per `write-code-eval`).

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Run /document `pr` | `changelog` | `release-note` | `postmortem` (or let it ask) to write the human facing prose about a change. Drafts from the real commits and diff, writing to the right place. Does not write code, tests, or specs.

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Detect and use Crabbox for repository tests and validation on remote runners. Use when crabbox.yaml or .crabbox.yaml exists, the crabbox CLI is available, or work needs remote compute, a clean or reusable environment, target-platform coverage, or auditable execution evidence.

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。