「detection」の検索結果

Expert blueprint for fighting games including frame data (startup/active/recovery frames, advantage on hit/block), hitbox/hurtbox systems, input buffering (5-10 frames), motion input detection (QCF, DP), combo systems (damage scaling, cancel hierarchy), character states (idle/attacking/hitstun/blockstun), and rollback netcode. Based on FGC competitive design. Trigger keywords: fighting_game, frame_data, hitbox_hurtbox, input_buffer, motion_inputs, combo_system, rollback_netcode, cancel_system, advantage_frames.

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 rhythm games including audio synchronization (BPM conductor, latency compensation with AudioServer.get_time_since_last_mix), note highways (scroll speed, timing windows), judgment systems (Perfect/Great/Good/Bad/Miss), scoring with combo multipliers, input processing (lane-based, hold note detection), and chart/beatmap loading. Based on DDR/osu!/Beat Saber research. Trigger keywords: rhythm_game, audio_sync, timing_judgment, note_highway, combo_system, BPM_conductor, latency_compensation.

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 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.

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 porting desktop games to mobile including touch control schemes (virtual joystick, gesture detection), UI scaling for small screens, performance optimization for mobile GPUs, battery life management, and platform-specific features. Use when creating mobile ports or cross-platform mobile builds. Trigger keywords: TouchScreenButton, virtual_joystick, gesture_detector, InputEventScreenTouch, InputEventScreenDrag, mobile_optimization, battery_saving, adaptive_performance, MOBILE_ENABLED.

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 physics queries using RayCast, ShapeCast, and DirectSpaceState. Covers hit detection, volume overlap, mouse picking, and high-performance server-side intersection queries. Use when implementing projectiles, LOS, terrain grounding, or AI sensors. Keywords raycast, shapecast, direct_space_state, intersect_ray, intersect_shape, PhysicsRayQueryParameters, collision mask, mouse picking.

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 fighting games including frame data (startup/active/recovery frames, advantage on hit/block), hitbox/hurtbox systems, input buffering (5-10 frames), motion input detection (QCF, DP), combo systems (damage scaling, cancel hierarchy), character states (idle/attacking/hitstun/blockstun), and rollback netcode. Based on FGC competitive design. Trigger keywords: fighting_game, frame_data, hitbox_hurtbox, input_buffer, motion_inputs, combo_system, rollback_netcode, cancel_system, advantage_frames.

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 rhythm games including audio synchronization (BPM conductor, latency compensation with AudioServer.get_time_since_last_mix), note highways (scroll speed, timing windows), judgment systems (Perfect/Great/Good/Bad/Miss), scoring with combo multipliers, input processing (lane-based, hold note detection), and chart/beatmap loading. Based on DDR/osu!/Beat Saber research. Trigger keywords: rhythm_game, audio_sync, timing_judgment, note_highway, combo_system, BPM_conductor, latency_compensation.

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 porting desktop games to mobile including touch control schemes (virtual joystick, gesture detection), UI scaling for small screens, performance optimization for mobile GPUs, battery life management, and platform-specific features. Use when creating mobile ports or cross-platform mobile builds. Trigger keywords: TouchScreenButton, virtual_joystick, gesture_detector, InputEventScreenTouch, InputEventScreenDrag, mobile_optimization, battery_saving, adaptive_performance, MOBILE_ENABLED.

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 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.

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。

Deploy applications to the Temps platform with automatic framework detection, Dockerfile generation, and container orchestration. Supports Next.js, Vite, React, Node.js, Python, Go, Rust, Java, and C# applications. Use when the user wants to: (1) Deploy their app to Temps, (2) Set up CI/CD with Temps, (3) Configure deployment settings, (4) Create a Dockerfile for Temps, (5) Deploy a containerized application, (6) Set up automatic deployments from Git. Triggers: "deploy to temps", "temps deployment", "push to temps", "containerize for temps", "temps ci/cd".

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。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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。

Backlink profile analysis: referring domains, anchor text distribution, toxic link detection, competitor gap analysis. Works with free APIs (Moz, Bing Webmaster, Common Crawl) and DataForSEO extension. Use when user says backlinks, link profile, referring domains, anchor text, toxic links, link gap, link building, disavow, or backlink audit.

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。

Comprehensive SEO analysis for any website, page, business, or topic. Full site audits, SEO checks, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, GEO for AI Overviews/ChatGPT/Perplexity, and SEO best-practice planning. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, full SEO check, SEO best practices, optimize my site, ranking, organic search, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured 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。

Assistive Access implementation for cognitive accessibility including simplified scenes, navigation icons, runtime detection, and design principles. Use when optimizing apps for Assistive Access 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。

Generates lapsed user detection and re-engagement screens with personalized return experiences, win-back offers, and inactivity tracking. Use when user wants to re-engage inactive users, detect lapsed users, or build return flows.

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 smart App Store review prompt infrastructure with configurable conditions and platform detection. Use when user wants to add review prompts, request ratings, or implement StoreKit reviews.

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。

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.

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 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

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。

Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages.

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。

A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.

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。

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz

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。

cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat

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。