Loading...
Loading...
Found 25 Skills
Deploys static HTML to a public URL instantly with no authentication required. Use when asked to "host this", "deploy this site", "get a public link", "share this HTML", "quick deploy", "publish this page", or any request to make an HTML file publicly accessible via URL. Supports self-contained HTML files with inline CSS/JS.
Shared launch intake for any TAO workflow or action. Use when the user wants to run TAO AutoML, train, evaluate, infer, export, generate TensorRT engines, or launch DEFT/workflow jobs on an execution platform.
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.
Sagemaker Endpoint Deployer - Auto-activating skill for ML Deployment. Triggers on: sagemaker endpoint deployer, sagemaker endpoint deployer Part of the ML Deployment skill category.
Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Azure ML pipelines, AutoML, managed online/batch endpoints, prompt flow, or MLflow deployments, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Data Science Virtual Machines (use azure-data-science-vm).
Design and implement a complete ML pipeline for: $ARGUMENTS
Flask Ml Api Creator - Auto-activating skill for ML Deployment. Triggers on: flask ml api creator, flask ml api creator Part of the ML Deployment skill category.
Azure Ml Deployer - Auto-activating skill for ML Deployment. Triggers on: azure ml deployer, azure ml deployer Part of the ML Deployment skill category.
Guidance for setting up HuggingFace model inference services with Flask APIs. This skill applies when downloading HuggingFace models, creating inference endpoints, or building ML model serving APIs. Use for tasks involving transformers library, model caching, and REST API creation for ML models.
Vertex Ai Deployer - Auto-activating skill for ML Deployment. Triggers on: vertex ai deployer, vertex ai deployer Part of the ML Deployment skill category.
Эксперт ML API. Используй для model serving, inference endpoints, FastAPI и ML deployment.
You are an **AI Engineer**, an expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. You focus on building intelligent featu...