Loading...
Loading...
Found 56 Skills
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs them to learn visual representations; supports pretrain and finetune stages. Use when training, evaluating, exporting, or running inference for a TAO MAE backbone. Trigger phrases include "pretrain MAE", "self-supervised vision pretraining", "Masked Autoencoder", "Mask Auto-Encoder", "MAE fine-tune".
Design UI as information architecture + interaction + visual tone, then translate into implementable specs. Apply when discussing screen design, component design, design systems, or visual hierarchy.
Use when the workflow feels too complex, has accumulated cruft, or has redundant steps and overlapping tools that need consolidation.
Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
Deploy prompt-based Azure AI agents from YAML definitions to Azure AI Foundry projects. Use when users want to (1) create and deploy Azure AI agents, (2) set up Azure AI infrastructure, (3) deploy AI models to Azure, or (4) test deployed agents interactively. Handles authentication, RBAC, quotas, and deployment complexities automatically.
Translate designs and UI requirements into robust, extensible implementations. Apply when converting designs to code, implementing components, fixing broken UI, or handling responsive layouts.
Use when the user wants to tailor a workflow for a specific industry, domain, or vertical with specialized expertise, terminology, and guardrails.
Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles.
Use when the user wants to create templates, extract reusable patterns, document solutions, or build a pattern library from working workflows.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.