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概要と使いどころ
Generates App Clip targets with invocation URL handling, lightweight experiences, and full app upgrade prompts. Use when user wants NFC/QR/Safari banner invocation, instant app experiences, or App Clip Card setup.
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Generate sample data and a multi-variant
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How App Store ratings actually behave — per-storefront isolation (your US stars show nowhere else), the never-reset rule, phased release + manual release as rating protection, and where prompting/replying fit. Use when planning ratings strategy for new markets, considering a ratings reset, setting release options, or diagnosing "why is my rating missing in country X."
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Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.
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Generates protocol-based analytics infrastructure with swappable providers (TelemetryDeck, Firebase, Mixpanel). Use when user wants to add analytics, track events, or set up telemetry.
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MapKit and GeoToolbox skills — PlaceDescriptor patterns, geocoding, and cross-service place identifiers with MapKit integration. Use when working with maps, place data, or geocoding.
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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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Generates an Apple-compliant account deletion flow with multi-step confirmation UI, optional data export, configurable grace period, Keychain cleanup, and server-side deletion request. Use when user needs account deletion, right-to-delete, or Apple App Review compliance for account removal.
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When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' or 'data hygiene.' For cold outreach emails, see cold-email. For email drip campaigns, see email-sequence. For pricing decisions, see pricing-strategy.
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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.
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When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.
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 basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.
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SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontamin
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Pathview is a stand-alone software package for pathway based data integration and visualization.
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Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.
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'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated
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Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).
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Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime infere
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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
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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.
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SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.
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The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly fou
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A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.
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The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.
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。