データ・AIのスキル

ScalarDBの制約を考慮したデータモデル設計を行います。Partition Key・Clustering Key・Secondary Index の設計、ホットスポット評価、メタデータオーバーヘッド見積もり、バックエンドDB選定を含みます。ワークフローStep 04（データモデル設計）で使用します。

EC2 など別マシンへのセッション引き継ぎが第一用途。現セッションの Recap（LLM 要約）と Next Actions を VCS 共有可能な `.claude/journal.md` の先頭に追記する。ローカル端末固有で他マシンから見えない built-in `/recap` の限界を補う。同一マシン内のセッション区切りにも使える。「EC2 に引き継ぎ」「別マシンに渡したい」「handoff」「ハンドオーバー」「journal に残して」「新セッションに渡したい」「新しい会話を始めたい」「セッションを区切りたい」で発動。

backlog（atoms/pipeline/outputs）と skill ヘルス（usage/duration/unused）を統合分析し 1 レポートで提示する。kawai 氏型 analytics ループの起点。「次に何やる？」「atom 整理」「放置案件」「stale」「振り返り」「retro」「skill 使用統計」「unused skill」「atom-suggest」で発動。

Analyze yesterday's prompts to surface one technical concept worth understanding, delivered as a concise morning lesson with everyday analogy + engineer vocabulary. Use when "morning lesson", "prompt mirror", "today's learning", "what should I learn", or via scheduled automation each morning.

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。

Analyze and improve existing Ozor.ai video prompts for better output quality. Use this skill whenever the user has an Ozor prompt that produced mediocre results, wants to improve a video prompt, needs help debugging why a video didn't turn out well, or wants expert feedback on their prompt before running it. Trigger on phrases like 'improve this prompt', 'optimize my Ozor prompt', 'why did my video look bad', 'fix this prompt', 'make this prompt better', 'review my Ozor prompt', 'the video didn't turn out right', 'prompt feedback', 'better results from Ozor', or any request to refine, debug, or enhance an existing Ozor video prompt.

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。

Generate a batch of social media video prompts for multiple platforms from a single input. Use this skill whenever the user wants to create videos for multiple social platforms at once, repurpose one video across platforms, or create a social media video campaign. Trigger on phrases like 'social media videos', 'videos for all platforms', 'Instagram and TikTok video', 'social campaign', 'batch of social videos', 'repurpose this for social', 'video for every platform', 'social clips', 'create Reels and Shorts', 'multi-platform video', or any request involving creating short-form video content for multiple social media platforms using Ozor.

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。

Generate an AI video end-to-end on Ozor.ai from a short text brief. Use this skill whenever the user wants to actually create a video (not just write a prompt) from a creative description, idea, or short brief — e.g. 'make me a video about X', 'generate a launch video for Y', 'create an explainer for Z', 'render a video', 'produce a 30s ad about ...', 'spin up a quick video for our new feature'. This skill runs the full Ozor MCP pipeline: generate_video → wait_for_job → export_video → wait_for_export → get_embed_code, and returns the shareUrl, editorUrl, downloadUrl, and a ready-to-paste <iframe> embed. Do NOT use this skill when the input is a document/PDF/PPTX/deck/URL — use ozor-document-video or ozor-url-to-video instead.

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。

Automatically translate and sync App Store metadata (description, keywords, what's new, subtitle) to multiple languages using LLM translation and asc CLI. Use when asked to localize an app's App Store listing, translate app descriptions, or add new languages to App Store Connect.

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。

Collect, download, and verify App Store Connect Analytics reports with asc. Use when users need to discover analytics report requests, select report instances by processing date or granularity, download every segment, or verify downloaded files against Apple's size and MD5 metadata before analysis.

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 improve their app's star rating, increase ratings volume, optimize when and how they prompt users for a review, or recover from a bad rating period. Use when the user mentions "app rating", "star rating", "review prompt", "SKStoreReviewRequest", "In-App Review API", "ask for review", "low rating", "rating drop", "get more reviews", or "recover from 1-star". For responding to reviews, see review-management. For overall ASO health, see aso-audit.

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 optimize their subscription business end-to-end — from trial start through renewal, cancellation, and win-back. Use when the user mentions "subscription lifecycle", "trial conversion", "churn", "cancellation", "win-back", "lapsed subscribers", "dunning", "billing retry", "grace period", "renewal rate", "subscriber LTV", or "resubscribe". For paywall design and pricing strategy, see monetization-strategy. For subscription analytics dashboards, see app-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。

Resize and validate App Store screenshots with current asc screenshot-size data and macOS sips. Use when preparing or fixing screenshots for App Store Connect submission.

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。

Fetch current, hourly, and daily weather forecasts and display required attribution using WeatherKit. Use when integrating weather data, showing forecasts, handling weather alerts, displaying Apple Weather attribution, or querying historical weather statistics in iOS 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。

When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-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。

When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For retention metrics, see retention-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。

When the user wants to monitor, triage, or reduce their app's crash rate — including setting up Crashlytics, prioritizing which crashes to fix first, interpreting crash data, and understanding how crashes affect App Store ranking. Use when the user mentions "crash", "crashlytics", "crash rate", "ANR", "app not responding", "crash-free sessions", "crash-free users", "symbolication", "stability", "firebase crashes", "app crashing", or "crash report". For overall analytics setup, see app-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。

Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

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 improve their app's onboarding experience, increase activation rate, reduce Day 1 drop-off, or optimize the first-run flow. Use when the user mentions "onboarding", "first-run", "activation", "tutorial", "day 1 retention", "new user flow", "permission prompts", "sign-up conversion", "onboarding funnel", or "users dropping off early". For overall retention strategy, see retention-optimization. For paywall placement, see monetization-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。

Access research-grade sensor data using SensorKit. Use when reading ambient light levels, accelerometer data, rotation rates, device usage patterns, keyboard metrics, or media events for approved research studies. Requires SensorKit entitlement and research study authorization.

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。

Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

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。

Perform cryptographic operations using Apple CryptoKit. Use when hashing data with SHA256/SHA384/SHA512, generating HMAC authentication codes, encrypting with AES-GCM or ChaChaPoly, signing with P256/P384/P521/Curve25519 keys, performing ECDH key agreement, storing keys in the Secure Enclave, or migrating from CommonCrypto to CryptoKit.

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 3D scenes and visualizations using SceneKit. Use when creating 3D views with SCNView and SCNScene, building node hierarchies with SCNNode, applying materials and lighting, animating with SCNAction, simulating physics with SCNPhysicsBody, loading 3D models (.usdz, .scn), adding particle effects, or embedding SceneKit in SwiftUI with SceneView. Note: SceneKit was deprecated at WWDC 2025 and is in maintenance mode; RealityKit is recommended for new projects.

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。

Display and manipulate PDF documents using PDFKit. Use when embedding PDFView to show PDF files, creating or modifying PDFDocument instances, adding annotations (highlights, notes, signatures), extracting text with PDFSelection, navigating pages, generating thumbnails, filling PDF forms, or wrapping PDFView in SwiftUI.

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。

Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translation to iOS/macOS/visionOS 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。