「screen」の検索結果

Inspects PostHog Visual Review (VR) runs that gate PR merges with screenshot regression checks. Use when the user mentions "visual review", "VR", "snapshot diff", "screenshot test", "storybook regression", "playwright snapshot", asks why a PR is blocked or what changed visually, wants to triage the VR backlog, decide whether a snapshot diff is real vs flaky, or check whether a story has been changing across runs. Also invoke when a PR has a failing `visual-review` status check, when a PR comment mentions "Visual review", or when the user is on a branch with an open VR run.

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 visual changelog or PR description by taking before/after screenshots of UI changes using Cursor's built-in browser. Use when preparing a PR with visual changes.

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。

End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping with NovaSeq 10x MiSeq caveat (Schnell 2015), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition with MANDATORY PERMANOVA + PERMDISP pair (Anderson & Walsh 2013), constrained ordination, and the read-counts-not-abundance critique (Lamb 2019). Use when processing eDNA samples for biodiversity assessment, deciding ASV vs OTU, configuring OBITools3 v3, interpreting decontam screening, or reporting community comparisons with the dispersion confound check.

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。

Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.

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。

Simulate the SCF team's manual prescreen on a Build Award submission — a completeness and eligibility check applied to every full submission before panel review. Checks completeness, Stellar integration, eligibility, budget issues, and common disqualifiers. Use when reviewing a draft Build Award submission for readiness before it goes to the SCF round.

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。

Comprehensive WCAG compliance and accessibility testing covering ARIA, keyboard navigation, screen readers, color contrast, and automated a11y 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。

This skill enables Claude to perform comprehensive accessibility audits. It uses the accessibility-test-scanner plugin to identify WCAG 2.1/2.2 compliance issues, validate ARIA attributes, check keyboard navigation, and assess screen reader compatibility. Use this skill when the user requests an accessibility scan, audit, or compliance check, or when terms like "WCAG", "ARIA", "screen reader", "accessibility testing", or "a11y" are mentioned. It provides actionable insights for improving web application accessibility.

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。

Automated WCAG 2.1 AA accessibility testing with axe-core and Lighthouse CI. Auto-detects frontend framework (React, Next.js, Vue, Angular, Svelte, Astro, Flutter, React Native), discovers all routes and interactive components, installs Playwright + axe-core for page-level scanning and jest-axe/vitest-axe for component-level testing. Generates tests for color contrast (4.5:1), alt text, form labels, ARIA attributes, heading order, landmark regions, focus visibility, keyboard navigation (tab order, focus traps, modal focus management, skip-to-content), screen reader compatibility (aria-live regions, error announcements, toast notifications), and Flutter Semantics validation (48dp touch targets, semanticLabel). Reports violations by severity (critical, serious, moderate, minor) with WCAG criterion references. Use when adding a11y testing, auditing accessibility compliance, fixing contrast issues, or validating keyboard and screen reader 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。

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。

Apple SwiftUI 宣言的 UI フレームワークリファレンス。 View, VStack, HStack, ZStack, List, NavigationStack, TabView, State, Binding, Bindable, Environment, EnvironmentObject, sheet, fullScreenCover, modifier, animation, withAnimation, Animation, Transition, matchedGeometryEffect, PhaseAnimator, KeyframeAnimator。

pen.dev CLI（`pencil` / `pen`コマンド）だけを使って.penファイル（pen.devで作成されたデザインファイル）をAIプロンプトまたは決定論的な編集操作で修正・更新・新規作成するスキル。.penファイルの編集、ボタン追加、レイアウト変更、UIデザインの調整、pen.devデザインの更新、新しい.penデザインの作成などの依頼で必ず使用する。AI任せの編集・新規作成はエージェントモード（`pencil --in --out --prompt`）で行い、既存ファイルは同一パスを指定して上書き、新規作成は`--in`を省略して`--out`に新しいパスを指定する。決定論的な編集はインタラクティブモード（`pencil interactive`）の`execute`（`Insert` / `Update` / `Delete` など）で行い、最後に`save()`する。編集・作成後は「**編集・作成したコンポーネントのNodeだけ**」を`execute`の`Export` / `TakeScreenshot`でPNG出力し、`.pen`と同階層の`snapshots/`ディレクトリに保存する。pen.dev MCPには依存せず`pencil`コマンドのみで完結する。.penファイルのgitコンフリクト解消・破損復旧は本スキルではなく`resolve-pencil-conflict`スキルの担当。

pen.dev CLI（`pencil` / `pen`コマンド）だけを使って、.penファイル（pen.devで作成されたデザインファイル）の中のNodeのデザインデータ（属性・構造）とスクリーンショット画像を読み取り専用で取得するスキル。Node ID指定に加え、名前の正規表現（例: 「ヘッダー」「.*Button」）、Nodeタイプ（frame / text / image など）、再利用可能コンポーネント、特定Node配下、ドキュメント全体のトップレベルなど、**ID以外の指定方法**にも対応する。ユーザーが「.penのこのNodeの中身を見せて」「特定コンポーネントのデザインデータを取り出して」「Nodeのスクリーンショットだけ欲しい」「ヘッダーの構造を確認したい」「ボタンのスタイルをコピーしたい」「再利用可能コンポーネント一覧を見せて」「ドキュメント全体の構造を覗きたい」「全てのテキストNodeを取得して」のように.pen内の要素の調査・参照・確認・抜き出しを依頼した場合に必ずこのスキルを使う。インタラクティブモード（`pencil interactive`）で `execute` の `Get` / `Print`（読み取り専用関数のみ）を使ってNode属性を取得し、同じ `execute` の `Export` / `TakeScreenshot` で画像を`.pen`と同階層の`snapshots/`にPNG出力する。編集はしない（`save()` も変更系関数も呼ばない）ため、対象ファイルは絶対に書き換わらない。pen.dev MCPには依存せず`pencil`コマンドのみで完結。

Detects and removes contamination in genome assemblies via two disjoint workflows - foreign-sequence screening of a single-organism (eukaryote/isolate) assembly with NCBI FCS-GX (GenBank-submission-mandatory), FCS-adaptor, and BlobToolKit blob plots; and MAG/bin quality assessment with CheckM2 plus GUNC (chimerism) plus GTDB-Tk taxonomy, judged against MIMAG. Covers why CheckM2 alone is blind to disjoint-marker chimeras, the FCS-GX RAM wall, organelle/NUMT triage, strain heterogeneity, and the HGT-vs-contamination (tardigrade) trap. Use when screening an assembly for foreign contamination before GenBank submission, assessing MAG completeness/contamination/chimerism, deciding which contigs to remove, or distinguishing real HGT from contaminant contigs.

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。

Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.

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。

Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.

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。

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

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。

Read this skill before the first step of any request to do something in an app on the person's own computer (Notes, Finder, System Settings, any desktop app), to look at their screen, or for "computer use". Computer use (desktop control) lets Claude take screenshots of the person's desktop and control it with clicks, typing and scrolling through the Claude desktop app; its tools are named mcp__computer-use__* when the session runs in the desktop app and mcp__remote-devices__computer_* when a cloud session is linked to the person's computer; before computer use is turned on for a conversation there may be no such tools, only an enable__mcp__remote-devices__computer tool, which turns it on. It covers turning it on, picking the right tool, the access flow, and the safety rules for tiered apps, links and financial actions. It is not for websites, which go through Claude in Chrome or the built-in browser and their own skills.

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。

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

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。

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

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 accessible web applications following WCAG 2.1/2.2 guidelines with proper semantic HTML, ARIA attributes, keyboard navigation, screen reader support, and inclusive design. Use when implementing ARIA labels and roles, ensuring keyboard navigation, supporting screen readers, providing text alternatives for images, managing focus, creating accessible forms, building inclusive UI components, testing with accessibility tools, meeting WCAG compliance levels, or designing for users with disabilities.

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 headless browser for QA testing and site dogfooding. Navigate any URL, interact with elements, verify page state, diff before/after actions, take annotated screenshots, check responsive layouts, test forms and uploads, handle dialogs, and assert element states. ~100ms per command. Use when you need to test a feature, verify a deployment, dogfood a user flow, or file a bug with evidence. Use when asked to "open in browser", "test the site", "take a screenshot", or "dogfood this". (gstack)

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 headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)

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。

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

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。

Score an unbuilt business idea on desirability, viability, feasibility, distribution wedge, why-now, founder-market-fit, market size, alternatives, defensibility, capital intensity, and regulatory/ethical risk — and return a GO / ITERATE / KILL verdict with kill criteria and a next kill test. Load when the user asks to evaluate a business idea, score a startup idea, screen an idea, decide whether to pursue this venture, do an idea review, or says "is this a good business idea", "should I build this", "evaluate this startup", "screen this idea", "go/no-go on this idea", "kill or pursue". Sub-skill of `venture-exploration`. Calls `fermi` for sizing, `assumption-mapping` for hidden beliefs, optional `pre-mortem` / `adversarial-hat` for high-stakes ideas. Does NOT evaluate built products — for that use `reality-check`.

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。