「process」の検索結果

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 input handling covering InputMap actions, InputEvent processing, controller support, rebinding, deadzones, and input buffering. Use when setting up player controls, implementing input systems, or adding gamepad/accessibility features. Keywords InputMap, InputEvent, gamepad, controller, rebinding, deadzone, input buffer.

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。

Navigation skill for genre project scaffolds: pick platformer/RPG/FPS (or other genre) then load matching template scripts and route gameplay fill to godot-genre-* skills. Use when bootstrapping architecture, bootstrap order, pause modes, PCK DLC, or feature tags — not for pasting FPS/inventory recipes. Keywords: project templates, bootstrap, Subsystem Locator, PCK, feature tags, PROCESS_MODE_ALWAYS, genre scaffold.

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 input handling covering InputMap actions, InputEvent processing, controller support, rebinding, deadzones, and input buffering. Use when setting up player controls, implementing input systems, or adding gamepad/accessibility features. Keywords InputMap, InputEvent, gamepad, controller, rebinding, deadzone, input buffer.

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。

Start (or restart) a local Temps control plane built from this checkout, and the web dev server (`bun dev`) in `<checkout>/web`. Invoke when the user says "start temps", "restart temps", "launch the server", "kill and restart temps", or asks to bring the local server up after backend changes. Ports, database and data dir are allocated PER CHECKOUT (a "slot") so several worktrees/branches run side by side without killing each other or corrupting each other's schema — the first checkout you start in claims slot 0 (the familiar `:8080` / `:3000`); every other worktree gets its own slot and its own `temps_s<N>` database. Uses the `fast` cargo profile (release semantics, no debug symbols, parallel codegen) for quick rebuilds. Pass `split` (e.g. "start temps split", "/start-temps split") to launch the two-process proxy/console topology for testing that feature.

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。

Design, build, test, and distribute external Temps plugins with TypeScript/Bun; provide development and local-testing guidance for existing Rust plugins. Use for creating a plugin, adding an embedded console UI or deployment-event automation, testing host permissions and AI, installing from GitHub, or submitting a plugin to the public catalog. End-to-end publishing covers TypeScript GitHub-source plugins, not Rust native-package releases or in-process TempsPlugin backend crates.

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。

Use when implementing shaders — Godot shader language, visual shaders, common visual recipes, and post-processing effects

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。

Use when implementing particle effects — GPUParticles2D/3D, ParticleProcessMaterial, emission shapes, subemitters, trails, attractors, collision, and common VFX recipes

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Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.

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。

Structure and draft Confidential Information Memorandums (CIMs) for A-share sell-side M&A processes. Organizes company information into professional, investor-ready documents with Chinese market context. Triggers on "A股CIM", "保密信息备忘录", "CIM China", "confidential memorandum A-share", or "offering memorandum [company]".

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。

Track A-share sell-side M&A and deal pipeline with milestones, deadlines, and action items. Adapted from the original deal-tracker skill for Chinese market deal processes. Triggers on "A股交易跟踪", "并购进度", "deal tracker China", "M&A pipeline A-share", or "track progress on [deal]".

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。

Draft anonymous one-page company teasers for A-share sell-side M&A processes. Creates compelling blind profiles without revealing the company's identity, adapted for Chinese market conventions. Triggers on "A股Teaser", "盲审材料", "blind profile China", "anonymous teaser A-share", or "sell-side teaser".

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。

Configuration module patterns for LlamaFarm. Covers Pydantic v2 models, JSONSchema generation, YAML processing, and validation.

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。

Electron patterns for LlamaFarm Desktop. Covers main/renderer processes, IPC, security, and packaging.

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。

Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node. Render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video completes but looks wrong, such as artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, or a two-stage refiner or upscale degrading the result. For errors/OOM/missing nodes use the troubleshooting skill.

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。

Handle App Store rejections, prepare submissions to avoid common rejection reasons, write Resolution Center responses, and navigate the appeal process. Use when user's app was rejected, is preparing for submission, or needs help with App Review.

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Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, feature flags, app icons, image caching, pagination, HTTP caching, share cards, social export, subscription lifecycle, referral systems, watermarks, streak tracking, milestone celebrations, what's new screens, lapsed user re-engagement, usage insights, variable rewards, consent flows, account deletion, permission priming, force updates, state restoration, debug menus, offline queues, feedback forms, announcement banners, quick win sessions, Spotlight indexing, App Clips, screenshot automation, background processing, app extensions, data export, or SwiftUI preview sample data and variant matrices.

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。

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.

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。

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progre

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Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more

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This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and qua

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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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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。