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Found 21 Skills
Use when debugging Foundation Models issues — context exceeded, guardrail violations, slow generation, availability problems, unsupported language, or unexpected output. Systematic diagnostics with production crisis defense.
Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.
Reference — Complete Foundation Models framework guide covering LanguageModelSession, @Generable, @Guide, Tool protocol, streaming, dynamic schemas, built-in use cases, and all WWDC 2025 code examples
Write, review, or integrate Apple's on-device FoundationModels framework (iOS 26.0+, macOS 26.0+). Use when building generative AI features, structured data extraction, tool calling, or streaming text generation natively on Apple Silicon devices.
Use when implementing on-device AI with Apple's Foundation Models framework — prevents context overflow, blocking UI, wrong model use cases, and manual JSON parsing when @Generable should be used. iOS 26+, macOS 26+, iPadOS 26+, axiom-visionOS 26+
Use this skill when working with Apple's Foundation Models framework for on-device AI and LLM capabilities in iOS/macOS apps
Use when implementing on-device AI with Apple's Foundation Models framework (iOS 26+), building summarization/extraction/classification features, or using @Generable for type-safe structured output.
List and explore foundation models available in SAP AI Core GenAI Hub: browse supported providers, model names, versions, capabilities, and executable IDs needed to create deployments. WHEN: user wants to see what models are available, find an executable-id or model-name for deployment, browse providers (Azure OpenAI, Anthropic, Google, Mistral, Cohere, etc.), check model capabilities or context length, get the correct model-name to pass to create_llm_deployment, or get curl inference examples for a deployed model (chat completions, Responses API, embeddings, image generation, etc.). DO NOT USE FOR: creating or managing deployments — use `aicore-lifecycle-management`.
Manage SAP AI Core lifecycle resources: scenarios, executables, configurations, and deployments. WHEN: user wants to list or inspect scenarios or executables, create or list configurations, create or list or stop or delete or patch deployments, get deployment logs, or remove duplicate LLM deployments. DO NOT USE FOR: listing foundation models or getting inference examples — use `genai-hub-foundation-models`; managing resource groups, secrets, or GitOps resources — use `aicore-admin-resources`.
Verify and configure SAP AI Core credentials, retrieve auth tokens, and manage resource groups. WHEN: user wants to check if AI Core is set up, configure credentials, get a Bearer token, fix authentication errors, run a setup health check, or list, create, update, or delete resource groups. DO NOT USE FOR: managing deployments — use `aicore-lifecycle-management`; listing foundation models — use `genai-hub-foundation-models`.
Apple Intelligence skills for on-device AI features including Foundation Models, Visual Intelligence, App Intents, and intelligent assistants. Use when implementing AI-powered features.
Generate correct code for whole-slide image (WSI) analysis using TIAToolbox and foundation models (H-optimus-0, UNI, Prov-GigaPath). Triggers on requests involving whole-slide images, WSI, digital pathology, histopathology, SVS/NDPI/pyramidal TIFF, tissue segmentation, patch extraction, stain normalization, H-optimus-0, TIAToolbox, CAMELYON16/17, SlideGraph, MIL aggregation, HoVer-Net, PanNuke, or SageMaker deployment of pathology models. Produces deterministic commands and Python snippets for slide-info inspection, tissue masking, tile extraction at specified mpp, foundation-model feature embedding, slide-level aggregation, and regulated cloud inference.