2,950 件 ・ 関連度順
概要と使いどころ
Expert blueprint for UI themes using Theme resources, StyleBoxes, custom fonts, and theme overrides for consistent visual styling. Covers StyleBoxFlat/Texture, theme inheritance, dynamic theme switching, and font variations. Use when implementing consistent UI styling OR supporting multiple themes. Keywords Theme, StyleBox, StyleBoxFlat, add_theme_override, font, theme inheritance, dark mode.
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 patterns for CharacterBody2D including platformer movement (coyote time, jump buffering, variable jump height), top-down movement (8-way, tank controls), collision handling, one-way platforms, and state machines. Use for player characters, NPCs, or enemies. Trigger keywords: CharacterBody2D, move_and_slide, is_on_floor, coyote_time, jump_buffer, velocity, get_slide_collision, one_way_platforms, state_machine.
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 blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.
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 blueprint for sandbox games (Minecraft, Terraria, Garry's Mod) with physics-based interactions, cellular automata, emergent gameplay, and creative tools. Use when building open-world creation games with voxels, element systems, player-created structures, or procedural worlds. Keywords voxel, sandbox, cellular automata, MultiMesh, chunk management, emergent behavior, creative mode.
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 patterns for Godot audio including AudioStreamPlayer variants (2D positional, 3D spatial), AudioBus mixing architecture, dynamic effects (reverb, EQ,compression), audio pooling for performance, music transitions (crossfade, bpm-sync), and procedural audio generation. Use for music systems, sound effects, spatial audio, or audio-reactive gameplay. Trigger keywords: AudioStreamPlayer, AudioStreamPlayer2D, AudioStreamPlayer3D, AudioBus, AudioServer, AudioEffect, music_crossfade, audio_pool, positional_audio, reverb, bus_volume.
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。
Post-earnings quarterly update reports for A-share companies under coverage. Analyzes 业绩快报/正式财报, generates variance tables (actual vs 一致预期 vs prior), flags key drivers, and drafts structured earnings notes in Chinese sell-side format. Use instead of the original earnings-analysis skill for Chinese equities. Triggers on "A股财报分析", "季度业绩点评", "年报/中报点评", "earnings review", or "[company] earnings".
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 Qwen Image 2512 text-to-image workflows with QwenImageIntegratedKSampler, separate component loading, lightning LoRAs, and fine-tuned model 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。
Turn the current conversation context into a PRD and publish it as a GitHub issue that is ready to receive sub-issues from /to-issues-project. Project-local variant of /to-prd, adapted for this repo's PRD-as-parent-issue + sub-issues + agent:implement workflow.
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。
Optimize slow React Router loaders by eliminating redundant DB queries, creating slim query variants, and parallelizing independent fetches. Use proactively when writing or reviewing loader code that calls domain operations services (e.g. CourseOperationsService, VideoOperationsService), or when triaging a slow page load.
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。
Break a PRD into native GitHub sub-issues attached to the parent PRD. Project-local variant of /to-issues, adapted for this repo's PRD-as-parent + native sub-issues + agent:implement multi-session workflow. Argument is the parent PRD issue number.
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。
Generates Custom Product Page metadata templates with screenshot strategies, keyword targeting, and audience-specific messaging for up to 35 page variants. Use when creating targeted App Store pages for different audiences, ad campaigns, or traffic sources.
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。
Generates an app icon for macOS or iOS — a fast CoreGraphics placeholder and/or flat layered source art to finish in Icon Composer (the Liquid Glass / iOS 26+ standard). Produces appearance variants (light/dark/tinted) and installs into the asset catalog. Use when the user wants to create, generate, iterate, or update an app icon.
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。
Generate sample data and a multi-variant
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。
SF Symbols end-to-end — choosing and configuring the 7,000+ system symbols (rendering modes, variable color/draw, gradients), authoring custom symbols that interpolate across weights, and the animation preset vocabulary. Use when picking iconography, building custom symbols, animating symbol state changes, or rendering values as symbols.
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。
Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream().
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。
mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.
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。
SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.
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。
Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts. Scales linearly with the number of spots and is designed to be used with 'SpatialExperiment' objects.
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 package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of th
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。
Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.
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。
Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and
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。
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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。
Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such
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。
Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression
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。