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Found 1,596 Skills
The base44 CLI is used for EVERYTHING related to base44 projects: resource configuration (entities, backend functions, ai agents), initialization and actions (resource creation, deployment). This skill is the place for learning about how to configure resources. When you plan or implement a feature, you must learn this skill
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Queries Tilt resource status, logs, and manages dev environments. Use when checking deployment health, investigating errors, reading logs, or working with Tiltfiles.
Security audit guidelines for web applications and REST APIs based on OWASP Top 10 and web security best practices. Use when checking code for vulnerabilities, reviewing auth/authz, auditing APIs, or before production deployment.
Complete guide for building scalable microservices with Express.js including middleware patterns, routing strategies, error handling, production architecture, and deployment best practices
Creates safe rollback procedures for deployments with automated workflows, rollback runbooks, version management, and incident response. Use for "rollback automation", "deployment recovery", "incident response", or "production rollback".
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.
Creates and validates Azure Resource Manager (ARM) templates for infrastructure deployment. Use when creating ARM templates, deploying Azure infrastructure as code, or validating Azure templates.
Complete guide to Kernel CLI - cloud browser platform with automation, deployment, and management
Expert ML engineering covering model development, MLOps, feature engineering, model deployment, and production ML systems.
Expert delivery management covering continuous delivery, release management, deployment coordination, and service operations.
Expert-level ArgoCD GitOps deployment, application management, sync strategies, and production operations