「foundation」の検索結果

Use when working with camera, photos, audio, haptics, ShazamKit, on-device music analysis, face grouping and video highlights, screen recording, casting to non-AirPlay routes, SharePlay playback, the user's Apple Music library, lock-screen metadata, or CarPlay app design, templates, and navigation. Covers AVCaptureSession, AVFoundation, Core Haptics, MediaPlayer, MusicKit, NowPlaying, ScreenCaptureKit, DockKit.

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, testing, or evaluating ANY Apple Intelligence, on-device AI, or speech-to-text feature. Covers Foundation Models, @Generable, LanguageModelSession, Tool protocol, eval suites, model-as-judge scoring, SpeechTranscriber, CoreML.

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。

Formats and parses locale-aware numbers, decimals, currencies, percentages, dates, durations, measurements, names, lists, byte counts, and URLs with Foundation FormatStyle and ParseableFormatStyle. Use when replacing legacy Formatter subclasses, designing reusable format APIs, using SwiftUI Text(_:format:), correcting availability, or testing user-facing formatted output across locales.

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。

Implement, review, or improve photo picking, camera capture, and media handling in iOS apps using PhotoKit and AVFoundation. Use when working with PhotosPicker, PHPickerViewController, camera capture sessions (AVCaptureSession), photo library access, image loading and display, video recording, or media permissions. Also use when selecting photos from the library, taking pictures, recording video, processing images, or handling photo/camera privacy permissions in Swift apps.

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 private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp. Use when choosing an Apple-local model runtime, building an Apple Intelligence chatbot or tool-calling feature, running an LLM on Apple Silicon, converting or compressing a Python model for Core ML, or comparing on-device inference backends. For Swift Core ML loading and prediction code, use the coreml 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。

Create media playback experiences using AVKit. Use when adding video players with AVPlayerViewController, enabling Picture-in-Picture, routing media with AirPlay, using SwiftUI VideoPlayer views, configuring transport controls, displaying subtitles and closed captions, or integrating AVFoundation playback with system UI.

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。

Model a Phase-Locked Loop (PLL) IC from its datasheet or system specs using Mixed-Signal Blockset. Without this skill, agents universally select the wrong solver and produce non-functional PLL models — 100% of unguided attempts fail. Covers Integer-N, Fractional-N, Dual Modulus architectures, loop filter design, lock time optimization, VCO phase noise configuration, and msbPllArchitectures/msbPllFoundation block assembly. Use when: PLL modeling, frequency synthesizer design, phase noise simulation, lock time analysis, charge pump design, loop filter tuning, datasheet-to-model, Mixed-Signal Blockset PLL, msbPllArchitectures.

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。

Foundation skill for all RoadRunner workflows: MATLAB path setup, connection, project/scene/scenario lifecycle, world settings, handle management, status, and close. Use when connecting to RoadRunner, managing projects/scenes/scenarios, setting world origin, checking status, closing RoadRunner, or when any downstream RoadRunner skill needs initialization.

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。

Etsy shop launch guide — policies, branding, first listings, SEO foundation, payment setup

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。

Patterns for individual long-form content pieces. Case studies, whitepapers, research reports, definitive guides, manifestos, ebooks, long-form tutorials. The structural disciplines that distinguish publication-quality long-form from bloggy-long padding or academic bloat. Different from pillar-content-architecture (which covers hub structure); this skill covers individual deep-dive pieces. Triggers on long-form content, case study writing, whitepaper, research report, definitive guide, ebook, manifesto, long-form tutorial, deep-dive article, anchor piece, foundational article, structural archetypes. Also triggers when a piece is over 3,000 words and the team is unsure how to structure it, when a long-form draft feels padded or saggy, or when a flagship asset needs the depth to actually earn the length.

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。

Grant writing and proposal architecture: funder fit, proposal structure, budget design, and success-factor scoring. Use when writing a grant proposal, evaluating funder fit, auditing a draft for competitiveness, or planning a budget.

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。

Create and maintain the foundational context doc (positioning, ICP, competitive landscape, brand voice, customer language) that other marketing skills read first. Use when setting up context, defining the ICP, or setting brand voice.

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 create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing.md` that all other skills reference for product, audience, and positioning context.

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。

Research, drafting, and analysis involving the International Criminal Court (Rome Statute system). Enforces a verification-first methodology — every case-law, decision, filing, warrant, and statement citation is verified against an authoritative primary source (icc-cpi.int, legal-tools.org) in the current conversation before it appears in an output. Foundational texts (Rome Statute, Elements of Crimes, RPE, Regulations of the Court) may be cited from project knowledge when present. Use whenever the user is working with ICC case law, OTP statements, ASP documents, or ICC procedure.

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 Godot 4 project organization (feature-based folders, naming conventions, version control). Enforces snake_case files, PascalCase nodes, %SceneUniqueNames, and .gitignore best practices. Use when starting new projects or refactoring structure. Keywords project organization, naming conventions, snake_case, PascalCase, feature-based, .gitignore, .gdignore.

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。

Bevy ECS skill for a Bevy Rust project layout. Keywords: Bevy, Cargo, features, project layout.

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 create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Creates `.agents/product-marketing-context.md` that other marketing skills reference.

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。

Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate

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。

Guide to macOS 26 Tahoe APIs and features. Covers Apple Intelligence (FoundationModels), MLX framework, and Continuity. Use when implementing macOS 26 specific features.

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。

Apple Intelligence skills for on-device AI features including Foundation Models, Visual Intelligence, App Intents, and intelligent assistants. Use when implementing AI-powered features.

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。

Foundation skill that captures and stores core brand context — identity, audience, positioning, values, and voice. Use when the user says "set brand context", "save my brand info", "store brand details", "brand profile", "create brand context file", "update brand context", or when starting any brand project for the first time. Every other brand skill reads this file first. Always run this before beginning any brand work if the context file doesn't exist yet.

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。

Create a visual identity brief for a brand — logo direction, color palette, typography, imagery style, and design system foundations. Use when the user says "visual identity", "brand identity", "logo brief", "logo direction", "design brief", "brand design", "color palette for my brand", "typography for my brand", "visual language", "design system", "brand look and feel", "what should my brand look like", or is briefing a designer or design agency. Also use when the user has a brand strategy and wants to translate it into visual design direction.

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 a brand's messaging hierarchy — taglines, value propositions, key messages, and proof points for each audience. Use when the user says "brand messaging", "messaging framework", "value proposition", "tagline", "brand tagline", "key messages", "messaging hierarchy", "what should we say about our brand", "brand copy foundation", "messaging architecture", "elevator pitch", "one-liner", "brand statement", or needs consistent language across all communications. Also use when messaging feels inconsistent across channels or teams.

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。