「eas」の検索結果

This skill should be considered when you need to normalize testing engineers' written defect reasons following the provided product codebooks. This skill will correct the typos, misused abbreviations, ambiguous descriptions, mixed Chinese-English text or misleading text and provide explanations. This skill will do segmentation, semantic matching, confidence calibration and station validation.

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。

VERIFY your changes work. Measure performance, CLS. Use BEFORE and AFTER making changes to confirm fixes. Includes ready-to-run scripts: measure-cls.ts, detect-flicker.ts

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。

VERIFY your changes work. Measure CLS, detect theme flicker, test visual stability, check performance. Use BEFORE and AFTER making changes to confirm fixes. Includes ready-to-run scripts: measure-cls.ts, detect-flicker.ts

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。

Detect long-term trends in time series data using parametric and non-parametric methods. Use when determining if a variable shows statistically significant increase or decrease over time.

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 general knowledge reasoning across diverse domains — trivia, commonsense inference, analogy, and factual question answering.

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。

Perform closed-book frontier reasoning — complex problem solving from internalized knowledge without external retrieval or tool use.

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 ideas in one shot using creative sampling

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。

Für Leasing AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.

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Writing a Succession-style ensemble series (继承之战群像剧写法) — distilled from Jesse Armstrong's complete shooting scripts for all four seasons, 39 episodes (Faber 2023), with his introductions and footnotes, plus Frank Rich's and Lucy Prebble's essays on the writers' room. Covers the invisible-act three-test for scripts with no ACT marks, container episodes built on a ceremony's running order, pressure chambers, one core question per episode, finales with fewer and longer scenes, the absent protagonist at the climax, humiliation passed downward, stage directions carrying subtext and "maybe", reversals turned on one word, writing long, alts, the mega-chart, and engineering an ending once the plot engine loses pressure. Use when the user wants a dynastic family drama, a media-power ensemble, a wedding/funeral/summit set piece, a streaming hour with no act breaks, a finale that redraws alliances, or asks how to "Succession-ize" a script or kill a lead mid-season.

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Pre-screen analysis outputs (tables, figures, logs) built on restricted or confidential data for statistical-disclosure-limitation problems before any release. Scans for small cell counts, complementary-suppression gaps, dominance (p-percent / (n,k)), re-identifiable exact counts, PII leakage, and unrounded sensitive statistics; classifies each finding CRITICAL / WARNING / OK and gates on any CRITICAL. Use before depositing or sharing restricted-data results, or when the user says "disclosure check", "SDL scan", "is this output safe to release", "check for small cells", "disclosure avoidance", "pre-screen for the RDC", or "can I export this from the enclave".

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 the full R package release gate — regenerate docs, run the test suite, run R CMD check --as-cran, and triage every ERROR / WARNING / NOTE against CRAN policy before a release or submission. Use when the user says "check my R package", "R CMD check", "is this package CRAN-ready", "run devtools::check", "prepare for CRAN submission", or points at a directory containing a DESCRIPTION file. Produces a check report + CRAN-submission checklist in `quality_reports/`.

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Stress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.

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Research any company, product, or service from a URL plus description and build a comprehensive, evidence-bound AEO/GEO/AI-visibility prompt panel across buyer jobs, information acts, journey states, B0-B5 proximity, aided status, roles, locales, variants, partitions, surfaces, and measurement lanes. Use when a user wants prompts to track in ChatGPT, Claude, Gemini, Perplexity, AI search, or answer engines; wants an AI-visibility measurement design; or needs a versioned panel rather than an SEO keyword 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。

Turn an approved measurement charter, ICPs, and buyer jobs into a budget-aware prompt coverage blueprint across proximity bands, aided status, information acts, journey states, roles, locales, evidence grades, partitions, and measurement lanes. Use before prompt wording to define required, optional, and prohibited canonical intent cells.

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Audit, question, suggest, or fact-preservingly revise a press release, blog post, contributed article, or expert explainer so AI answer systems can more easily retrieve, understand, quote, and cite its useful information. Use when someone asks for AI visibility, AI search, answer-engine, AEO, GEO, AI Overview, or ChatGPT citation optimization of supplied writing; when they want an evidence-aware pre-publication audit; or when another Newsjack workflow needs a prose-level AI-discoverability pass. Do not use as a technical SEO audit, rank tracker, publishing system, or promise of rankings, mentions, citations, traffic, or coverage.

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Publish or update a game on Steam with Steamworks and SteamPipe: configure depots and packages, upload builds with steamcmd, set a build live on a branch, and run the release checklists. Use for Steam publishing, app_build.vdf/steamcmd uploads, depots, beta branches, or a store page release.

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Add "juice" and game feel that makes actions satisfying — screen shake, hit-stop/freeze frames, tweened/eased motion, squash & stretch, knockback, and layered audio-visual feedback — as engine-neutral techniques that pair with the detected engine's tween, particle, and camera APIs. Use when the user mentions game feel, juice, "make it feel good/punchy", screen shake, hit stop, screen freeze, easing, squash and stretch, impact frames, or feedback/polish on hits, jumps, pickups, and deaths.

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。

Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.

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。

Find and fix game performance problems methodically — measure with the engine profiler first, reason about the frame-time budget, locate the CPU-vs-GPU bottleneck, then apply the right fix: object pooling, draw-call batching, fewer allocations/GC spikes, and asset budgets. Engine- neutral method that pairs with each engine's profiler. Use when the user mentions performance, optimize, low/dropping FPS, frame drops, stutter, lag, profiler, frame budget, draw calls, batching, garbage collection/GC spikes, object pooling, or "the game runs slow".

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Create or assess an evidence-grounded, falsifiable outcome hypothesis: a testable prediction of what measurable business result will change, for whom, by when, and how leading and lagging indicators will prove or disprove it. Use when framing measurable outcomes, turning an MVP, POC, feature, or technical initiative into a beneficiary result, defining targets and indicators, or judging whether evidence is strong enough to invest. Also applies to business outcome hypotheses, value hypotheses, and outcome statements.

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Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Use when requirements lack triggers or measurable outcomes, or the user asks to "optimize my prompt" / "improve this requirement" / "make this more specific".

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 asked 等保怎么做, 等保 2.0 定级, 等保备案和测评流程, 等保三级要求, 我们的系统需要过等保吗, or prepare an information system for China's Multi-Level Protection Scheme (MLPS 2.0, 网络安全等级保护). Produces a grading recommendation (定级) with the reasoning for the expert review, the filing (备案) and assessment (测评) steps and their usual timing, a gap checklist across the ten control areas of GB/T 22239-2019, and a remediation plan, marked as preparation for a licensed assessor.

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。

Plan a Chinese campus recruitment season (校招: 秋招 and 春招), including internships that convert to offers (实习转正): the timeline, a target company list, a one-page campus CV, online application answers, written tests and interview rounds, and choosing between offers and signing the tripartite agreement (三方协议). Use when asked 秋招怎么准备, 帮我做校招计划, 校招简历, 网申问题怎么写, 群面, 三方协议, 怎么选 offer, or plan my campus recruitment in China. Produces a season timeline to confirm with each company, a tiered company list, a campus CV, application answer drafts, a test and interview preparation plan, and an offer comparison.

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。

Plan a Chinese company's entry into an overseas market (出海): choose the market with evidence, then plan entity, payments, localisation, compliance, channels and team for the first twelve months. Use when asked 出海计划, 我们要进入东南亚 / 中东 / 欧洲 / 美国市场, how should a Chinese company expand overseas, or a go-global plan. Produces a market scorecard, a twelve-month entry plan by workstream, the compliance and data checklist for the chosen market, and the top risks with mitigations. Bilingual output on request.

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。