5,137 件 ・ 関連度順
Security category index for Apple platforms. Routes to the privacy-manifests skill. General secure-storage/biometrics/ATS guidance was retired in the 2026 blind-test audit — current models cover it natively.
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。
Growth skills for indie Apple developers — user acquisition, analytics interpretation, press/media outreach, community building, and indie business operations. Also covers the stage-by-stage store-growth audit and the post-launch store-signals loop. Use when user asks about growing their app, auditing growth levers, understanding metrics, getting press coverage, or running an indie dev business.
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。
TDD and testing skills for iOS/macOS apps. Covers characterization tests, TDD workflows, test contracts, snapshot tests, test infrastructure, and deterministic quality gates (fitness functions, coverage ratchet, mutation testing). Use for test-driven development, adding tests to existing code, or building test infrastructure.
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。
Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.
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。
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.
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。
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.
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。
The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across
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。
Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordina
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。
Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.
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。
The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provi
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。
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to
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。
phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.
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。
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.
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。
Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working wit
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 knowledge for Azure Quotas development including limits & quotas. Use when checking storage account capacity, IOPS, throughput, portal views, or CLI-based quota increase requests, and other Azure Quotas related development tasks. Not for Azure Monitor (use azure-monitor), Azure Resource Manager (use azure-resource-manager), Azure Policy (use azure-policy).
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 knowledge for Azure Service Connector development including troubleshooting, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when wiring Azure compute to Redis, databases, storage, messaging, AI services, or third‑party endpoints, and other Azure Service Connector related development tasks. Not for Azure API Management (use azure-api-management), Azure Connector Namespace (use azure-connector-namespace), Azure Logic Apps (use azure-logic-apps), Azure Service Bus (use azure-service-bus).
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 knowledge for SQL Server on Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning SQL VM vs Managed Instance, Always On/FCI HADR, Azure storage/backups, Key Vault/Entra auth, or KQL telemetry, and other SQL Server on Azure Virtual Machines related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Virtual Machines (use azure-virtual-machines), Azure Data Science Virtual Machines (use azure-data-science-vm).
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 knowledge for Azure Large Instances development including troubleshooting, limits & quotas, and integrations & coding patterns. Use when configuring Epic SKUs, sizing volume groups, tuning EHR storage, or resolving Epic–ALI connectivity/perf issues, and other Azure Large Instances related development tasks. Not for Azure Baremetal Infrastructure (use azure-baremetal-infrastructure), Azure Virtual Machines (use azure-virtual-machines), Azure Virtual Machine Scale Sets (use azure-vm-scalesets), Azure HPC Cache (use azure-hpc-cache).
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 knowledge for Azure Managed Applications development including limits & quotas, security, configuration, and deployment. Use when designing createUiDefinition UIs, JIT access, managed identities, StorageAccountSelector, or Bicep-based catalog publishing, and other Azure Managed Applications related development tasks. Not for Azure Blueprints (use azure-blueprints), Azure Resource Manager (use azure-resource-manager), Azure Lighthouse (use azure-lighthouse), Azure Partner Solutions (use azure-partner-solutions).
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 knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AutoML, Prompt Flow, online/batch endpoints, vector stores/RAG, or Model Catalog in Azure ML, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Science Virtual Machines (use azure-data-science-vm), Azure HDInsight (use azure-hdinsight).
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 knowledge for Azure Operator Nexus development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and deployment. Use when managing Nexus clusters, fabric ACLs/BGP, isolation domains, near‑edge storage, or upgrade workflows, and other Azure Operator Nexus related development tasks. Not for Azure Operator Insights (use azure-operator-insights), Azure Operator Service Manager (use azure-operator-service-manager), Azure Networking (use azure-networking), Azure Virtual Network Manager (use azure-virtual-network-manager).
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 knowledge for Azure Defender For Cloud development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing VMs/containers/storage/SQL, enabling plans, tuning alerts, exporting via APIs/ARG, or integrating SIEM/XDR, and other Azure Defender For Cloud related development tasks. Not for Azure Security (use azure-security), Azure Sentinel (use azure-sentinel), Azure DDoS Protection (use azure-ddos-protection), Azure External Attack Surface Management (use azure-external-attack-surface-management).
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 knowledge for Azure Durable Task development including best practices, decision making, architecture & design patterns, limits & quotas, configuration, integrations & coding patterns, and deployment. Use when building Durable orchestrations with Azure Storage/SQL/Netherite, versioning deployments, or using instance APIs, and other Azure Durable Task related development tasks. Not for Azure Functions (use azure-functions), Azure Logic Apps (use azure-logic-apps), Azure App Service (use azure-app-service), Azure Service Fabric (use azure-service-fabric).
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 knowledge for Azure HDInsight development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with HDInsight Spark/Hive/Kafka/HBase clusters, Ambari/Oozie pipelines, or Azure-integrated storage/BI, and other Azure HDInsight related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics).
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。