「condition」の検索結果

Expert patterns for AnimationTree including StateMachine transitions, BlendSpace2D for directional movement, BlendTree for layered animations, root motion, transition conditions, advance expressions, and state machine sub-states. Use for complex character animation systems with movement blending and state management. Trigger keywords: AnimationTree, AnimationNodeStateMachine, BlendSpace2D, BlendSpace1D, BlendTree, transition_request, blend_position, advance_expression, AnimationNodeAdd2, AnimationNodeBlend2, root_motion.

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Expert blueprint for puzzle games including undo systems (Command pattern for state reversal), grid-based logic (Sokoban-style mechanics), non-verbal tutorials (teach through level design), win condition checking, state management, and visual feedback (instant confirmation of valid moves). Use for logic puzzles, physics puzzles, or match-3 games. Trigger keywords: puzzle_game, undo_system, command_pattern, grid_logic, non_verbal_tutorial, state_management.

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Expert patterns for branching dialogue systems including dialogue graphs (Resource-based), character portraits, player choices, conditional dialogue (flags/quests), typewriter effects, localization support, and voice acting integration. Use for narrative games, RPGs, or visual novels. Trigger keywords: DialogueLine, DialogueChoice, DialogueGraph, dialogue_manager, typewriter_effect, branching_dialogue, dialogue_flags, localization, voice_acting.

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Expert patterns for AnimationTree including StateMachine transitions, BlendSpace2D for directional movement, BlendTree for layered animations, root motion, transition conditions, advance expressions, and state machine sub-states. Use for complex character animation systems with movement blending and state management. Trigger keywords: AnimationTree, AnimationNodeStateMachine, BlendSpace2D, BlendSpace1D, BlendTree, transition_request, blend_position, advance_expression, AnimationNodeAdd2, AnimationNodeBlend2, root_motion.

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Build Qwen Image Edit workflows covering model loading, conditioning, LoRAs, prompt patterns, and XY plot testing

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Extend / continue a video temporally with Pusa 2.2 in ComfyUI. Temporal flowmatching (the flowmatch_pusa scheduler + WanVideoAddPusaNoise) on the WanVideoWrapper stack with WAN 2.2 T2V A14B (HIGH/LOW) models and the Pusa V1 LoRAs, conditioning on the loaded clip via WanVideoEncode so the existing motion carries into the continuation. Covers the kijai wanvideo_2_2_14B_Pusa_extension graph, model/LoRA slots + downloads, noise/length/scheduler settings, chaining multiple extensions, VRAM tiers, gotchas, and the extend→upscale handoff.

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Use when implementing dialogue — data structures for branching dialogue, conditions, and UI presentation

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Use when using the Dialogue Manager addon — .dialogue files with titles, responses, conditions and mutations, runtime balloons, and C# support

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Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to up to 15 specialists (8 always + 7 conditional), generates health score. Use when user says audit, full SEO check, SEO best-practice review, analyze my site, website health check, or find SEO issues.

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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.

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Generates an HTTP caching layer with Cache-Control parsing, ETag/conditional requests, and offline fallback. Use when user wants to add response caching, offline support, or reduce API calls.

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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.

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This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.

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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.

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This tool fetches microarray data directly from GEO database, based on the GEOQuery R package. Given a GSE accession ID, it returns an Rdata object containing the data and a text file (.cond file) summarizing the conditions of the experimen

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This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.

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This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.

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Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con

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Expert knowledge for Azure Role-based access control development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when defining custom/built-in roles, ABAC conditions, deny assignments, PIM, or ARM/Bicep-based role automation, and other Azure Role-based access control related development tasks. Not for Azure Policy (use azure-policy), Azure Security (use azure-security), Azure Resource Manager (use azure-resource-manager), Azure Portal (use azure-portal).

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把模糊诉求收敛成另一个 AI 能自主执行且可验收的 goal contract（scope / non-goals / success criteria / verification / stop conditions）。本 skill 只写目标契约，**绝不替用户执行任务**。触发硬条件：这份 goal 是要交给别人跑的——subagent、Codex、另一个 AI 会话或另一个人。用于写 goal、优化 goal、改 handoff prompt，或把"今天做完、尽量优化、帮我研究并执行"这类请求变成执行方不会乱猜、不会越界的任务契约。不适用于：自己团队的需求管理和版本拆解（用 issue-pool——它产出的是给人开工的 task，不是给 AI 的契约）、整页界面方案（用 page-solution-design）、PRD/验收标准/测试用例文档（用 prd-test-writer）、框架计划和版本路线（用 issue-pool）、以及用户其实是想让你**直接把这件事做了**的情况——那就直接做，不要走本 skill 把活变成一份文档。

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Flatten deeply nested conditionals into readable, early-return code. Use on any function with 3+ levels of indentation.

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Cialdini's seven principles of compliance (the classic six — reciprocation, commitment-consistency, social proof, liking, authority, scarcity — plus Unity, added 2016) and Pre-Suasion. Use when crafting ethical persuasion (marketing, sales, negotiation), defending against manipulation, or designing systems that need cooperation. Includes replication-crisis caveats and known factual corrections (Bickman parking-meter numbers, Hofling modern replication, Drive Carefully condition disambiguation, Genovese myth).

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Review Java concurrency code for thread safety, race conditions, deadlocks, and modern patterns (Virtual Threads, CompletableFuture, @Async). Use when user asks "check thread safety", "concurrency review", "async code review", or when reviewing multi-threaded code.

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Build multi-sequence email automation flows with branching logic. Triggers on: "build email automation", "create email funnel", "email automation flow", "welcome series with branches", "conditional email sequence", "set up automation", "email workflow builder", "segmented email flow", "advanced email sequence", "nurture funnel", "cart abandonment sequence", "win-back email flow".

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。