2천건 · 관련도순
Use when building UI, components, motion icons, loaders, or visual design. In BDB projects, routes to the BDB component and motion-icon library (npm @hybridlabor-api/ui-motion-library, shadcn namespace @bdb) as the first source before third-party registries or building from scratch. Outside BDB projects, the same lookup order applies with the original sources listed in the bdb-ui-library repository docs.
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Bootstrap a new Gradle module in any Kotlin Android project. Walks the user through choosing one of three module types — library module (`com.android.library`), feature module (regular Android library that hosts a feature/UI, install-time only), or dynamic feature module (`com.android.dynamic-feature`, on-demand/conditional/instant delivery). Use whenever the user wants to add a module, modularize code, extract a util/data/ui layer, create a new feature, set up Play Feature Delivery, or asks "should this be a library or feature module?". Discovers the project's existing conventions (plugin aliases, namespaces, sibling modules, version catalog) instead of hardcoding any. Pushes back on bad combos (e.g. utility code in a dynamic feature). Stops at scaffolding; app-level wiring is out of scope.
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新しいライブラリ/フレームワークの参照スキルを skills/<library>/ に新規作成するワークフロー。 公式ドキュメント調査から SKILL.md 作成・検証・CLAUDE.md 反映までを Agent 委譲で進める。 「スキルを追加して」「<library> のリファレンスを作って」などで使用。
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Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.
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Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
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Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
사용자 조작과 화면 결과를 중심으로 React 컴포넌트와 훅을 테스트합니다. 통신 응답 모의와 접근성 점검을 지원하고 실제 브라우저에서 검증할 범위도 정리합니다.
Flutter/Dart 코드를 상태 관리 라이브러리의 관례에 맞춰 검토합니다. 화면 구성과 비동기 처리부터 성능, 테스트, 접근성, 보안까지 점검합니다.
Context7 MCP로 라이브러리와 프레임워크의 설정, API, 코드 예시를 조사합니다. 가져온 최신 문서를 바탕으로 답변하며, 자료가 있으면 지정 버전을 우선합니다.
Collect competitors’ ads from libraries such as Facebook Ad Library and LinkedIn, save screenshots, and compare messages, audiences, and visual patterns. Use the findings to develop original campaign ideas and advertising copy.
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Design reusable pose data for 2D characters, covering emotions, actions, mouth shapes, and repeated movements. Organize changes to individual body parts, pose durations, and transitions so animation sequences have clearly defined stages.
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Look up current library and framework documentation with Context7 to explain setup, configuration, and APIs. Retrieve relevant code examples, prefer official packages, and use documentation for a specified version when available.
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Analyze pooled CRISPR screens from FASTQ reads or guide-count tables using MAGeCK. Validate inputs, assess library and replicate quality, and rank enriched or depleted genes with effect sizes and false discovery rates, which estimate false-positive proportions.
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Prepare a library release on GitHub by reviewing code changes, proposing a version number, drafting a changelog, and opening a release pull request. Version and changelog reviews let you confirm the details before proceeding.
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Build Blazor interfaces with Microsoft Fluent UI components. The skill guides library setup, forms, selection controls, dialogs, notifications, and themes, and helps troubleshoot missing interface elements caused by provider configuration or JavaScript integration issues.
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Collect competitors’ ads from libraries such as Facebook Ad Library and LinkedIn, save screenshots, and compare messages, audiences, and visual patterns. Use the findings to develop original campaign ideas and advertising copy.
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Creates test starters for React and Next.js applications and analyzes coverage reports to find untested code. Supports unit, integration, and browser-based tests, simulated API responses, reusable test data, and test runner configuration.
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Write Python for n8n Code nodes to transform, filter, aggregate, and validate workflow data. Get guidance on reading input items, returning the required JSON structure, using standard library modules, and avoiding common errors within n8n's Python execution modes.
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Prepare a local development environment for the Data Manager API with authentication and library installation guidance. It covers five programming languages and companion utilities for formatting, hashing, and encrypting user identifiers before ingestion.
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Collect competitors’ ads from libraries such as Facebook Ad Library and LinkedIn, save screenshots, and compare messages, audiences, and visual patterns. Use the findings to develop original campaign ideas and advertising copy.
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Import FBX 3D models into CARLA on UE 5.8 and register them as props that can be placed in a simulation. Check measured dimensions, adjust scale, verify placement on a running server, and undo imports when needed.
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Build React components, forms, and custom hooks with TypeScript. Organize state and data fetching, debug rendering problems, migrate class components, and improve performance, with type checks and component tests to validate the implementation.
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Review licenses in dependency lists, individual libraries, or code planned for public release. Get a memo covering obligations for the deployment model, license conflicts, unresolved questions, and recommended actions for legal review before shipping.
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