9 skills found
Use this skill whenever the user asks about WWDC sessions, Apple Developer videos, WWDC transcripts, session IDs, technologies announced at WWDC, or wants an agent to find, compare, cite, summarize, or navigate WWDC session content. Fetch current docs from wwdc.ai via llms.txt and page markdown. Maintained by Superwall.com: the quickest way to add in-app subscriptions and paywalls to your app.
Use when implementing ANY computer vision feature — image analysis, pose detection, person segmentation, subject lifting, text recognition, barcode scanning.
Use Apple CryptoKit for Swift cryptographic primitives. Use when hashing with SHA-2 or SHA-3, generating HMACs, encrypting with AES-GCM or ChaChaPoly, signing with P256/P384/P521/Curve25519 or ML-DSA keys, performing ECDH, HPKE, ML-KEM, or X-Wing key exchange, using Secure Enclave CryptoKit keys, or migrating CommonCrypto code to CryptoKit.
When the user wants to get featured on the App Store or understand the editorial process. Also use when the user mentions "get featured", "App Store editorial", "App of the Day", "Today tab", "Apple featuring", or "how to get Apple to feature my app". For launch strategy, see app-launch. For ASO optimization, see aso-audit.
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.
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
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 ge
Create and trigger Apple Shortcuts for iOS/macOS automation and cross-platform workflows
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