「frame」の検索結果

Guide to macOS 26 Tahoe APIs and features. Covers Apple Intelligence (FoundationModels), MLX framework, and Continuity. Use when implementing macOS 26 specific features.

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Expert guidance for hybrid AppKit-SwiftUI development. Covers NSViewRepresentable, hosting controllers, and state management between frameworks. Use when bridging AppKit and SwiftUI.

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On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.

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Foundation framework skills — AttributedString patterns for rich text formatting, alignment, selection, and SwiftUI integration. Use when working with styled text or AttributedString APIs.

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SwiftUI framework skills — data flow (identity, Observation, state ownership), layout & containers (Layout protocol, lazy-stack performance), AlarmKit, 3D charts, rich text editing, customizable toolbars, and WebKit embedding. Use for SwiftUI state/rendering bugs, custom layouts, scroll performance, or these feature areas.

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SwiftUI animation patterns including springs, transitions, PhaseAnimator, KeyframeAnimator, SF Symbol effects, scroll-driven effects, mesh gradients, text renderers, and shader effects. Use when implementing, reviewing, or fixing animation or visual-effect code on iOS/macOS.

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Interface copy that works — voice/tone, the PACE framework, alert anatomy, naming features, empty states, error messages, and the small-word edits that measurably improve UX. Distilled from Apple's writing sessions (2017–2026). Use when writing or reviewing any user-facing text — labels, alerts, onboarding, notifications, empty states, paywalls — or naming a feature.

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Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.

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treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; qua

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The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules fr

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SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.

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Intuitive framework for identifying spatially variable genes (SVGs) via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the

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Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate

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Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework.

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Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.

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Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/imp

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syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-bas

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Expert knowledge for Azure AI Vision development including decision making, limits & quotas, configuration, integrations & coding patterns, and deployment. Use when using Image Analysis, Read OCR containers, smart-crop thumbnails, background removal, or video frame analysis, and other Azure AI Vision related development tasks. Not for Azure AI Custom Vision (use azure-custom-vision), Azure AI Video Indexer (use azure-video-indexer), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Immersive Reader (use azure-immersive-reader).

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Expert guidance for designing Azure solutions using Azure Architecture. Covers reference architectures, solution ideas, design patterns, technology choices, architecture styles, best practices, anti-patterns, example workloads, and migration guides. Use when designing AKS/microservices, hybrid/Arc, SAP or mainframe migrations, IoT/Edge, or mission-critical BCDR, and other Azure Architecture related development tasks.

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Turn any skill in this instance into a publish-ready launch article - proof-stat headline, one contrarian thesis, mechanics, war stories from real run history, a mental-model reframe, and the full SKILL.md embedded verbatim so readers can steal it. Optional Higgsfield title banner with --banner.

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Pull framework updates from the upstream Aeon repo into this instance - 3-way merges canon's new commits into a PR, never clobbering operator config.

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Provides guidance for writing, optimizing, and benchmarking Triton kernels for Intel XPU GPUs (Battlemage/Arc Pro B50) using the Xe-Forge optimization framework. Includes an LLM-driven trial-loop workflow (analyze, validate, benchmark, profile, finalize), XPU-specific patterns (tensor descriptors, GRF mode, tile swizzling), KernelBench fused kernels, and Flash Attention.

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Amazon Buy Box strategy and optimization framework. Eligibility factor analysis, competitive pricing strategies, FBA vs FBM impact assessment, seller performance optimization, and Buy Box winning tactics. Use when the user asks about Amazon Buy Box, winning Buy Box, Buy Box eligibility, or Buy Box optimization.

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Amazon backend search term optimization and strategy. 250-byte limit management, keyword deduplication, prioritization frameworks, and search visibility optimization. Use when the user asks about backend keywords, search terms, Amazon SEO, or keyword optimization.

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