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Found 4 Skills
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
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`.
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`.