815 件 ・ 関連度順
概要と使いどころ
Drafts U.S. bulk sales compliance packages for asset purchase transactions outside the ordinary course, including UCC Article 6 jurisdictional analysis, creditor schedules, notice of intended bulk sale, seller affidavit, escrow framework, and closing checklist. Use when handling bulk sale, bulk transfer, inventory sale, asset purchase compliance, creditor notice, or successor liability avoidance.
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。
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
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。
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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。
Clinical copy-editor that reviews text for communication issues. Use when user says review for prose or improve the prose
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。
Jevを使い、日本語中心・英語対応で文書、メール、メッセージ、提案書、技術資料、スライドの抽出テキストを多角的にレビューする。AIっぽさ、定型的な文体、冗長さ、直訳調、過剰敬語などを評価し、根拠引用と具体的な改善案を作成担当者へ返して改善ループを回すときに使う。Japanese-first bilingual editorial style review with Jev scores, source-grounded feedback and bounded revision loops; not an AI-authorship detector.
Use when writing, running, or debugging Playwright e2e tests in this Hydrolix Grafana datasource plugin. Covers the docker-compose `playwright` service invocation, locator patterns for Grafana's react-select / Monaco editor / panel chrome, state-dependent interactions in QueryEditor, and timeout/retry expectations. Apply whenever a task touches `tests/*.spec.ts`, `tests/helpers.ts`, the `playwright` service in `docker-compose.yaml`, or the test results under `test-results/`.
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。
Expert en édition de contenu (style guides, grammaire, fact-checking, SEO, calendriers éditoriaux)
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。
Content strategist v3 — content pillars, editorial calendar, distribution, repurposing
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。
Unity プロジェクト向けにプロシージャル音声（SE・BGM）を C# Editor スクリプトで生成する。「音声を生成」「SEを作って」「BGMを作って」「サウンドアセット」「AudioGenerator」「効果音」「プロシージャルオーディオ」といったリクエスト時に使用。WAV ファイル（44100Hz/16bit/Mono）を Assets フォルダに直接出力し、外部ツール不要で完結する。
Unity プロジェクトの WebGL ビルドとローカルサーバー起動を一貫して行う。「WebGLビルド」「ブラウザビルド」「WebGL」「ローカルサーバー」「ビルドして」「デプロイ」「Build WebGL」といったリクエスト時に使用。Editor スクリプト作成 → プラットフォーム切り替え → ビルド実行 → ローカル HTTP サーバー起動までを自動化する。
When the user wants to get featured on the App Store or understand the editorial process. Also use when the user mentions "get featured", "App Store editorial", "App of the Day", "Today tab", "Apple featuring", or "how to get Apple to feature my app". For launch strategy, see app-launch. For ASO optimization, see aso-audit.
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 operate a WeChat editorial content library in Busabase. Use when a team wants reusable source material, linked article drafts, an editorial kanban board, a publishing calendar, or a source-check and review queue. Keeps source documents and article scheduling in two linked Bases; does not publish to WeChat or approve an agent's own proposals.
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 a GitHub social-preview card (og:image, 1280x640) for a repository. Use when the user asks for a social preview, repo cover, og image, link card, opengraph image, or README hero. Five editorial moods (editorial, poster, blueprint, gallery, terminal), CJK-first typography, one self-contained HTML file, deterministic checks, crisp PNG export. No image model needed.
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 after changing prompts, skills, the compiler, or editorial policies — or when the user asks to measure edit quality, run regression evals, check agreement with approved edits, or says '評価して', '一致率を測って', 'eval を回して'. Compares agent output against human-approved golden projects.
Starts a CARLA UE 5.8 server from a ue58-dev tree — the editor binary in -game mode (the working headless server), the packaged shipping build, or the full editor — with offscreen/nullrhi rendering, custom RPC and streaming ports, and native ROS 2 via the single-dash -ros2 flag. Probes a running server, and stops one cleanly despite the 15-character process-name truncation. Use when the user asks to "run/start CARLA UE5", "launch a ue58 server", "run headless", "publish ROS 2 topics from UE5", or a client cannot reach the simulator.
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 cytosine (CBE, C-to-T) and adenine (ABE, A-to-G) base-editor guides by positioning the target base at the activity-peak of the editing window (protospacer positions ~5-7, PAM-distal numbering), minimizing bystander edits for product purity, reading dinucleotide context (APOBEC1 TC favored / GC disfavored), and selecting the editor variant (BE4max, ABEmax, ABE8e, YE1/SECURE, TadCBE, CGBE, SpG/SpRY-BE). Covers knockout by premature stop (CRISPR-STOP/iSTOP) and splice-site disruption, the three off-target classes (Cas-dependent, Cas-independent DNA, RNA), outcome prediction (BE-Hive/DeepBE), and the base-vs-prime-vs-HDR decision. Use when installing a transition mutation without a double-strand break, knocking out a gene without indels, or choosing CBE vs ABE. Generic guide scoring, prime editing, and HDR donors 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。
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
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。
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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。
A serif-led magazine aesthetic: terracotta accent on warm off-white paper. Good for long-form, editorial, and brand-led marketing pages. "A New Yorker interview column online.
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。
Warm editorial, print-leaning document design system — parchment background, single ink-blue accent, serif-led hierarchy (LXGW WenKai for Chinese, Charter for English). For résumés, reports, white papers, letters, and editorial reading pages that should feel authored and published. Not a product-UI / component system.
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。
Magazine-inspired editorial layout with refined serif typography, structured grids, and elegant reading experiences.
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。
BROWSER REVIEW QA SKILL — Use the built-in browser/headless browser (puppeteer-core) to open a browser-runnable Cocos preview, switch preview targets, capture screenshots, and compare them with reference images. USE FOR: screen-driven QA only when the user has already prepared a Browser Review environment that can run in the browser (`http://localhost:7456`, LoadingScene/preview host/preview target wiring ready). DO NOT USE FOR: current Cocos Editor window screenshots, Editor Preview capture, compile errors, or pure log analysis. If the Browser Review environment is not ready, stop and remind the user to prepare it first.
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。