Skill UI

A comprehensive Python SDK for interacting with Azure Container Registry (ACR). This tool allows developers to manage the full container lifecycle, including listing repositories, handling tags, manipulating manifests, downloading artifacts, and managing container images within Azure's cloud environment. Ideal for implementing robust CI/CD pipelines and container automation.

A comprehensive Python SDK for interacting with Azure Container Registry (ACR). This tool allows developers to manage the full container lifecycle, including listing repositories, handling tags, manipulating manifests, downloading artifacts, and managing container images within Azure's cloud environment. Ideal for implementing robust CI/CD pipelines and container automation.

This Python SDK provides comprehensive management capabilities for Azure API Center. It allows developers to programmatically handle the entire API lifecycle, including API registration, version control, metadata definition, definition import, and deployment management across an organization's API estate, ensuring robust governance and centralized control.

A comprehensive guide for integrating and managing VS Code extensions within the Cursor IDE environment. This resource details the transition from the Microsoft Marketplace to the Open VSX Registry, covering installation methods (in-app, VSIX, CLI), conflict resolution with advanced AI features (e.g., Copilot, TabNine), and best practices for ensuring optimal performance and recommended extension stacks for various languages like Python, JavaScript, and Go.

Generates end-to-end tests for web workflows using Playwright, Cypress, or Selenium, automating browser steps and assertions so QA teams can validate registration, login, shopping cart, and other multi-step flows across browsers and devices.

Provides a workflow for automating GitHub Gist operations by discovering Rube MCP tool schemas, ensuring active connections, and invoking Composio toolkit calls consistently with session reuse and schema compliance.

This blueprint provides a scalable, production-grade reference architecture for advanced LLM applications using LangChain. It guides the implementation of layered design, provider abstraction (allowing seamless switching between LLMs like OpenAI and Anthropic), dynamic chain registry, robust RAG pipelines, and complex multi-agent orchestration. Ideal for building enterprise-level, maintainable, and highly flexible AI systems.

This skill guides developers through setting up and processing Lokalise webhooks for real-time content synchronization. It covers the API integration steps to register webhook endpoints, handling various translation lifecycle events (key creation, translation updates, file imports), and implementing secure payload verification using webhook secrets. It demonstrates building robust, asynchronous event handlers in Node.js/Express, perfect for automating workflows based on content changes in a SaaS environment.

Helps manage container registries like AWS ECR, Google GCR, and Harbor by generating configuration code, guiding access controls, and advising best practices for secure, production-ready image storage and deployment automation.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

A comprehensive guide for developers to diagnose and fix the six most common errors encountered when building Obsidian plugins. It covers critical technical issues such as null pointer exceptions, manifest loading failures, incorrect CSS paths, command registration timing, and state persistence bugs, ensuring reliable and stable plugin functionality.