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Found 7 Skills
Use when managing AI Hub account, API keys, balance, usage, or API endpoints. Use when user says "AI Hub", "add AI credits", "create API key", "check AI usage", "auto-recharge", "AI Hub endpoint", "AI Hub base URL", "how to use AI Hub API", "LLM API", "AI API", "OpenAI compatible", "Anthropic API", "GPT", "Claude", "Gemini", "DeepSeek", or "Grok" in the context of Zeabur.
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`.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use whenever the user wants to stress-test a plan, get grilled on their design, mentions "grill me", "challenge my thinking", "poke holes in this", "play devil's advocate", "review my approach", "what am I missing", "pressure test", "tell me why this won't work", or asks for feedback on a plan, proposal, architecture decision, or technical design. Even if they're just explaining an idea or asking "does this make sense?", treat it as a signal to invoke this skill and interview them thoroughly. Don't wait for an explicit "grill me" — any request for input on a plan or decision warrants this.
Integrates the SAP Cloud SDK for AI for Python (sap-ai-sdk-gen, formerly generative-ai-hub-sdk) into Python applications. Use when building Python apps with SAP AI Core, Generative AI Hub, or the Orchestration Service: chat completion, embeddings, streaming, LangChain integration, templating, content filtering, data masking, and document grounding. Supports OpenAI GPT models, Llama, Gemini, Amazon Nova, and other foundation models via SAP BTP.
Guidance for Microsoft Defender for Cloud — AI workload protection (AI-SPM and runtime threat detection for generative AI). Covers AI Security Posture Management (discovery of Azure OpenAI / Azure AI Foundry / Amazon Bedrock / Google Vertex AI resources, identification of grounding data exposure, model deployment posture), runtime threat detection on Azure OpenAI (prompt injection / jailbreak attempts, sensitive data leakage in prompts/responses, wallet abuse, credential leakage), integration with Azure AI Content Safety Prompt Shields, attack path analysis for AI workloads, alert investigation in Defender XDR, and pairing with Purview DSPM for AI (user side) and Azure AI Content Safety (model side). WHEN: Defender for Cloud AI, AI-SPM, AI workload protection, Azure OpenAI threat detection, prompt injection alert, jailbreak alert Azure OpenAI, AI wallet abuse, AI workload posture, Amazon Bedrock posture, Vertex AI posture, generative AI security Azure. DO NOT USE for end-user AI usage governance (use purview-ai-hub), content moderation API (use azure-ai-content-safety), or M365 Copilot rollout (use copilot-for-m365-readiness).
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`.