「feed」の検索結果

When you want to monitor known sources on a schedule and feed the good stuff into your second brain. Configure sources once (YouTube, RSS/newsletters, subreddits, Hacker News, Bluesky, Mastodon, X, LinkedIn, keyword searches); each run fetches only what's new, scores it against your stated focus, writes one digest to the vault, and auto-captures high-signal items into raw/. Everything else waits in the digest until you promote it. Modes — run, digest, promote, add / sources / pause, doctor, schedule (daily launchd or systemd job). Triggers on "/radar," "run my radar," "check my sources," "what's new from my sources," "add a source," "monitor this channel," "watch this subreddit," "track this account," "daily digest," "promote item 4," "radar doctor." Complements second-brain (radar fills raw/, second-brain compiles it) and deep-research (standing surveillance vs a one-off dive).

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。

Optimise a learning activity for flow by balancing challenge level, skill, clear goals, and immediate feedback. Use when students are bored, anxious, or disengaged during a task.

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 multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.

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 a focused lesson observation protocol with specific look-fors and evidence collection methods. Use when planning peer observations, coaching visits, or developmental classroom visits.

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 a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.

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 a wise systems intervention from an existing analysis. Maps proposed actions against Meadows' leverage points, checks for unintended consequences, and generates alternatives.

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 an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.

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。

Select the right formative assessment technique for a specific learning moment, purpose, and age group. Use when choosing how to check understanding during, between, or after lessons.

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 a checking-for-understanding protocol with specific techniques for each lesson stage. Use when planning systematic comprehension checks during explicit or direct instruction.

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。

Direct game art and creative vision. Turn GAME_DESIGN into ART_DIRECTION defining a recognizable visual identity, functional visual and audio feedback, and key in-game moments. Use for what should the game look like, set the art direction, define the visual style. 游戏美术与创意方向。把 GAME_DESIGN 转成 ART_DIRECTION，定义可辨识的视觉身份、服务玩法的视听反馈和关键游戏时刻。用于判断游戏应该长什么样、制定游戏美术方向等需求。

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 game experience, systems, and levels. Converge the chosen concept into one GAME_DESIGN defining the player promise, core loop, how the world responds, the systems actually needed, level pacing, feedback, failure, and a fully playable prototype. Use for design the game world, deepen the gameplay and levels, write the game design document, design the branching story structure, scenes, dialogue and consequences. 游戏体验、系统与关卡策划。把选定概念收束为一份 GAME_DESIGN，定义玩家承诺、核心循环、世界响应、必要系统、关卡节奏、反馈、失败和可完整游玩的原型。用于设计游戏世界、深化玩法和关卡、设计互动叙事的场景对白与分支因果等需求。

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 GPU particle systems (explosions, magic effects, weather, trails) using GPUParticles2D/3D, ParticleProcessMaterial, gradients, sub-emitters, and custom shaders. Use when creating VFX, environmental effects, or visual feedback. Keywords GPUParticles2D, ParticleProcessMaterial, emission_shape, color_ramp, sub_emitter, one_shot.

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 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.

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 GPU particle systems (explosions, magic effects, weather, trails) using GPUParticles2D/3D, ParticleProcessMaterial, gradients, sub-emitters, and custom shaders. Use when creating VFX, environmental effects, or visual feedback. Keywords GPUParticles2D, ParticleProcessMaterial, emission_shape, color_ramp, sub_emitter, one_shot.

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 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.

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 to edit, review, or improve existing marketing copy. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' or 'copy sweep.' This skill provides a systematic approach to editing marketing copy through multiple focused passes.

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 achievement celebration UI with confetti animations, badge unlocks, progress milestones, haptic feedback, and optional share-to-social. Use when user wants to celebrate achievements, show confetti, display milestone badges, or trigger rewards on key thresholds.

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 skills for Apple platform UI — Liquid Glass, animations, game feel (haptics, sound, celebrations), UI prototyping, UX writing, SF Symbols, and typography. Use when implementing design language features, adding juice/feedback, writing interface copy, or choosing type and iconography.

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。

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。

Game feel ("juice") for Apple apps — celebration choreography, haptic vocabulary design, sound-effect layers, and the event×channel feedback audit. Use when big moments fall flat, when designing haptics or SFX, or when auditing whether every meaningful event actually reaches the user.

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。

An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

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 knowledge for Azure AI Metrics Advisor development including decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring data feeds, tuning anomaly detection, managing alerts/hooks, or calling Metrics Advisor REST/SDKs, and other Azure AI Metrics Advisor related development tasks. Not for Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Monitor (use azure-monitor), Azure Stream Analytics (use azure-stream-analytics).

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 knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using NoSQL/Mongo/Cassandra APIs, change feed, multi-region HA, vector search, or Cosmos DB for PostgreSQL, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Data Explorer (use azure-data-explorer).

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 knowledge for Azure Artifacts development including best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, .npmrc auth, GitHub Actions CI/CD, or package retention limits, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Boards (use azure-boards).

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。