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Force the states a happy-path run never reaches (a failing API, an empty list, a slow request, a timeout, an expired session, a toast that auto-dismisses) and check the UI actually handles them. Use when error handling was written but never run, when a loading or empty state needs verifying, when a bug only happens on a slow connection, or when a timer, poll, debounce or retry needs testing without sleeping.

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。

Builds and reviews SwiftUI views with modern MV architecture, state, composition, isolated previews, and migration guidance. Covers @Observable ownership, @State/@Bindable/@Environment wiring, view decomposition, ViewModifiers, environment values, .task loading, iOS 26+ handoffs, Writing Tools, clipboard availability, and performance. Use when structuring SwiftUI state, managing @Observable, composing views, previewing meaningful UI states, or correcting SwiftUI patterns.

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 iOS augmented reality and 3D experiences with RealityKit and ARKit. Use when adding RealityView content, loading entities or USDZ models, anchoring objects to planes or world positions, distinguishing entity hit tests from ARKit real-world raycasts, handling AR camera availability, world tracking, scene updates, or RealityKit entity gestures and interactions.

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。

Resolve Swift concurrency compiler errors, adopt approachable concurrency (SE-0466), and write data-race-safe async code. Use when fixing Sendable conformance errors, actor isolation warnings, or strict concurrency diagnostics; when adopting default MainActor isolation, @concurrent, nonisolated(nonsending), or Task.immediate; when designing actor-based architectures, structured concurrency with TaskGroup, or background work offloading; or when migrating from @preconcurrency to full Swift 6 strict concurrency.

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。

Embeds and controls web content in SwiftUI with WebKit for SwiftUI, including WebView, WebPage, navigation policies, JavaScript execution, observable page state, link interception, local HTML or data loading, and custom URL schemes. Use when building iOS 26+ article/detail views, help centers, in-app documentation, or other embedded web experiences backed by HTML, CSS, and JavaScript.

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。

Audit, diagnose, or optimize website loading and interaction performance, Core Web Vitals, and Lighthouse performance scores.

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。

Audit, diagnose, or optimize website loading and interaction performance, Core Web Vitals, and Lighthouse performance scores.

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 this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

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。

Read or write data files in MATLAB. Use when the task involves tables, spreadsheets, delimited text, or structured files in CSV, Excel, Parquet, JSON, or XML format — including but not limited to importing, exporting, loading, parsing, converting, validating, configuring import options, reading from URLs, handling locales or encodings, diagnosing file errors, and modernizing legacy file I/O code. MATLAB provides built-in functions for these workflows with no additional products required.

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。

Import HD Map or OpenDRIVE files into a RoadRunner scene using MATLAB. Use when loading driving scenes in RoadRunner or RoadRunner Scene Builder, importing RRHD, OpenDRIVE, or other RoadRunner-supported formats for simulation, or verifying Lanelet2-to-RRHD conversion results visually. Requires rrApp handle from roadrunner-core.

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。

Read and write 3-D point cloud data using Lidar Toolbox file I/O. Covers PLY, PCD, LAS/LAZ, PCAP (Velodyne/Ouster/Hesai), E57, and IDC (Ibeo) formats. Use when loading point clouds from disk, saving to disk, choosing the correct reader or writer for a file format, extracting or preserving lidar point attributes, reading Ibeo IDC sensor recordings, or converting between formats.

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 the user wants to train, compare, or export machine learning models using Classification Learner or Regression Learner — including opening the app, loading data, training models, evaluating metrics, comparing results, visualizing plots, testing on held-out data, exploring model interpretability, and exporting trained models. Programmatic access to Classification Learner and Regression Learner apps via AppController.

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。

AWS S3 object storage for bucket management, object operations, and access control. Use when creating buckets, uploading files, configuring lifecycle policies, setting up static websites, managing permissions, or implementing cross-region replication.

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。

Store speed audit — lazy loading, image compression, app bloat removal, theme code optimization

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。

Theme speed and UX optimization — Core Web Vitals, Liquid code, image loading, mobile responsiveness

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 unsure which of many installed skills applies to a request, if any, or when asked to make skill loading cheaper or more accurate. Jev ranks the whole catalog and may say no skill is needed.

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。

Orchestrate App Store releases with asc, including staging a version, uploading or building an artifact, publishing, and submitting for review. Use when the user wants to prepare or execute a release. Keep Game Center item preparation in this skill; route other readiness failures, stuck submissions, cancellation, and retry decisions to asc-submission-health.

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。

Import Claude Code or Codex history and move complete CCAM datasets between machines. Use when rescanning provider history, importing a copied directory, uploading JSONL or archives, exporting a backup, restoring it idempotently, or verifying that tokens, workflows, runs, rules, and pricing survived.

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。

Exhaustively extract UX patterns from a reference web app. Walks every screen, captures screenshots of every state, records interaction patterns, copy verbatim, keyboard shortcuts, responsive treatments, motion, and empty/error/loading states. Produces a reusable pattern library that other audits can compare against. The inverse of ux-audit — asks 'what is the bar?' rather than 'does this match the bar?'. Trigger with 'learn from X', 'extract patterns from X', 'study X's UX', 'reverse engineer the UX of X', 'build a pattern library from X'.

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 subtitles or the spoken text of a YouTube video is needed: pasted video links or IDs, requests to translate a video, read along, follow foreign-language content, or extract what was said. Also use for language learning or accessibility. Fetches timestamped subtitles from any YouTube video. Not for uploading subtitles or account 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。

Use when captions, subtitles, or the spoken text of a YouTube video is needed — even if not explicitly requested: pasted video links or IDs, requests to read, quote, or translate a video, accessibility needs, deaf/HoH use cases, content review, or language learning. Fetches timestamped caption data from any YouTube video. Not for uploading subtitles or account 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。

Backend plugin architecture, DeviceInterface, dynamic loading, and command encoding.

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。