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Found 14 Skills
Integrate Firebase AI Logic (Gemini in Firebase) for intelligent app features. Use when adding AI capabilities to Firebase apps, implementing generative AI features, or setting up Firebase AI SDK. Handles Firebase AI SDK setup, prompt engineering, and AI-powered features.
Azure AI Evaluation SDK for Python. Use for evaluating generative AI applications with quality, safety, agent, and custom evaluators. Triggers: "azure-ai-evaluation", "evaluators", "GroundednessEvaluator", "evaluate", "AI quality metrics", "RedTeam", "agent evaluation".
Amazon Bedrock patterns using AWS SDK for Java 2.x. Use when working with foundation models (listing, invoking), text generation, image generation, embeddings, streaming responses, or integrating generative AI with Spring Boot applications.
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
Amazon Web Services cloud platform with Lambda, EC2, S3, and RDS. Use for AWS infrastructure.
Create a new specification file for the solution, optimized for Generative AI consumption.
Guide for generating and editing images using generative AI with the nanobanana CLI
Creates and registers Tambo components - generative (AI creates on-demand) and interactable (pre-placed, AI updates). Use when defining components, working with TamboComponent, withInteractable, propsSchema, or registering components for AI to render or update.
WildWorld large-scale action-conditioned world modeling dataset with 108M+ frames from a photorealistic ARPG game, featuring per-frame annotations, 450+ actions, and explicit state information for generative world modeling research.
Stability AI integration. Manage data, records, and automate workflows. Use when the user wants to interact with Stability AI data.
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.
Update an existing specification file for the solution, optimized for Generative AI consumption based on new requirements or updates to any existing code.