「fact」の検索結果

Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.

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。

Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.

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。

Continue working on an OpenSpec change by creating the next artifact. Use when the user wants to progress their change, create the next artifact, or continue their workflow.

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。

Extract structured fields from ro e-factura documents.

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。

Extract structured fields from factura documents.

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。

Extract structured fields from facture documents.

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。

Imports an FBX vehicle into a CARLA source build as a spawnable vehicle.<make>.<model> — checks the FBX carries CARLA's 4-wheel bone rig, imports the mesh, then assembles the vehicle through CarlaTools' VehicleAuthoringLibrary (physics asset with kinematic wheel bodies, retargeted anim blueprint, four configured wheel blueprints, the vehicle blueprint), registers it in VehicleFactory through CARLA's own add_vehicle_to_vehicle_factory.py, and verifies it spawns, drives and steers. Use when the user asks to "add a car/truck/van to CARLA", "import a vehicle FBX", or needs a custom drivable vehicle spawnable from the Python API.

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。

Imports an FBX pedestrian into a CARLA source build as a spawnable walker.pedestrian.NNNN — checks the FBX is skinned to CARLA's GEN3 rig, imports the mesh, physics asset and materials, builds the walker blueprint from a GEN3 donor, registers it in WalkerFactory through CARLA's own add_walker_to_walker_factory.py, and verifies it spawns, stands on the ground and walks. Use when the user asks to "add a pedestrian to CARLA", "import a walker/character FBX", or needs a custom human spawnable from the Python API.

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 12-Factor Agents compliance analysis on any codebase. Use when evaluating agent architecture, reviewing LLM-powered systems, or auditing agentic applications against the 12-Factor methodology.

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When the user wants to optimize aerospace and defense supply chains, manage long lead-time components, handle MRO operations, or ensure AS9100 compliance. Also use when the user mentions "aerospace supply chain," "aviation manufacturing," "MRO supply chain," "AS9100," "aircraft parts," "defense procurement," "AOG support," "airworthiness," "ITAR compliance," "aerospace certification," or "aviation aftermarket." For general manufacturing, see production-scheduling. For quality, see quality-management.

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[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.

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Use when you need to write or improve integration tests for Quarkus — including @QuarkusTest, Dev Services for automatic container provisioning, Testcontainers via QuarkusTestResourceLifecycleManager, WireMock for external HTTP stubs, @QuarkusIntegrationTest for black-box testing against packaged artifacts, REST Assured, data isolation strategies (@TestTransaction vs @BeforeEach cleanup), and Maven Surefire/Failsafe three-tier split (*Test, *IT, *AT). This should trigger for requests such as Add or improve integration tests in a Quarkus project; Configure Testcontainers or Dev Services for Quarkus tests; Add WireMock stubs for external HTTP dependencies in Quarkus integration tests; Set up @QuarkusIntegrationTest for packaged artifact or native binary testing; Fix test data isolation or configure Maven Surefire/Failsafe split. Part of cursor-rules-java project

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 a diff-scoped, behavior-neutral cleanup of AI-generated slop on the current branch before code review. Use for removing trivial comment, defensive-check, type-laundering, redundant-intermediate, compatibility-shim, or local style drift from the branch diff. Do not use for full correctness or safety review, documentation work handled by technical-documentation or docs-refactor, or repo-wide refactors.

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。

Windows アプリ開発 (MSIX / Microsoft Store) のパッケージング・配置・公開リファレンス。 MSIX, AppxManifest, MakeAppx, MSIX Packaging Tool, Package Bundles, App Attach, Package Support Framework, Package Identity, PackageFamilyName, PackageManager, スパースパッケージ, App Installer, サイドローディング, Windows App SDK Bootstrapper, 自己完結型デプロイ, SignTool, Azure Trusted Signing (Azure Artifact Signing), 証明書, CI/CD 署名, Partner Center, アプリ送信, 年齢レーティング, ストアリスティング, 段階的ロールアウト, StoreContext, msstore CLI, 認定プロセス, 分析レポート。

Hono (軽量・マルチランタイム対応 Web フレームワーク) リファレンス。 Hono App, Context (c.req / c.json / c.text / c.html), HonoRequest, Routing, HTTPException、 Built-in Middleware (JWT, CORS, CSRF, Cache, Logger, Compress, Basic / Bearer Auth)、 Helpers (Cookie, Streaming, WebSocket, Factory, RPC)、 Cloudflare Workers / Deno / Bun / Node.js / AWS Lambda 対応。

App Store Connect API (REST) リファレンス。JWT 認証、API キー作成・失効、 Apps, Builds, Bundle IDs, Certificates, Devices, Provisioning Profiles, Users。 TestFlight Beta Groups / Testers / App Review。In-App Purchases, Subscriptions, Subscription Groups, Promotional Offers, App Price Points, Sales Reports。アプリ配布。 Sandbox Testers, User Invitations, Merchant IDs, Pass Type IDs, Bundle ID Capabilities。 Webhooks / Webhook Events / Deliveries、Xcode Cloud (Workflows, Build Runs, Artifacts)、 Alternative Distribution (EU マーケットプレイス / Web Distribution)、 Game Center (Achievements, Leaderboards, Challenges)、Analytics / 診断レポート。

Claude Code (code.claude.com) の各サーフェス（Desktop / IDE / Web / モバイル / ターミナル UX）リファレンス。 Desktop app（launch.json, scheduled tasks, WSL, Linux, iOS simulator）、 VS Code / JetBrains / Chrome 拡張連携、claude.ai/code（Web）と mobile アプリ、 interactive mode, fullscreen, voice dictation, accessibility, computer use, artifacts。

日本語のUI文言（ボタン・見出し・ラベル・入力欄の説明・空状態・エラー・確認ダイアログ・完了通知・チャットbotの応答文）を書く・直すときの規則。画面やbotがユーザーに見せる日本語を新しく書く時、既存の文言を見直す・統一する時、英語のまま残った表示を日本語にする時、サーバーのエラーメッセージを画面に出す経路を作る時に使用。公開されている日本語UIガイドライン（SmartHR・デジタル庁・Microsoft・Apple・公用文）の一致点を規則にし、食い違う点は採否と理由を定めてある。境界: 人に宛てた文章（PR本文・メッセージ・ドキュメント）は ukwhatn-writing、画面に何を出すかと提示前の完了基準は context/ui-artifact-standards.md、ナビゲーション・重ね方・通知の型とレイアウトは designing-ui、コードの実装原則は writing-code。

画面の設計判断（ナビゲーション・重ね方・通知と空状態と読み込み・外枠とトークンと状態表現）の型を決定表で選び、アプリ内の全画面で揃える。画面・ページ・レイアウト・ナビゲーション・ダイアログ・コンポーネントを新しく作る・作り直す時、モックやダッシュボードを作る時、サイドバー・タブ・モーダル・シート・toast・バナー・空状態のどれを使うか決める時に使用。PJ に components.json があれば shadcn の部品とトークンへの対応も扱う。主要なデザインシステム（Material・HIG・Carbon・Primer・Atlassian・Fluent・GOV.UK・NN/g・WAI-ARIA APG・WCAG）の一致点を規則にし、食い違いの採否を定めてある。境界: 画面の文言は writing-ui-text、画面に何を出すかと提示前の完了基準は context/ui-artifact-standards.md、コードの実装原則は writing-code、shadcn 部品の API と組み立ては shadcn。

Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".

Load and preprocess imaging mass cytometry (IMC) and MIBI data from raw MCD/TXT through hot-pixel removal, spillover compensation, and variance-stabilizing transformation, covering readimc/steinbock ingestion, NNLS spillover compensation (CATALYST), IMC-Denoise, and the IMC arcsinh-cofactor question. Use when starting analysis from raw MCD files, building per-channel TIFF stacks, compensating channel spillover, choosing an arcsinh cofactor, or preparing single-cell intensities for phenotyping.

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。

Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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。