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.

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.

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

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.

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.

This comprehensive guide provides techniques for acquiring, analyzing, and extracting artifacts from memory dumps. It covers best practices for digital forensics, crucial for incident response and malware analysis. Users learn how to use industry-standard tools like Volatility 3 on Windows, Linux, and macOS, including process, network, and registry deep dives.

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.

Automates guidance for model registry manager tasks within ML deployment, covering serving, MLOps pipelines, monitoring, and production optimization whenever that trigger phrase appears.

Enables Claude to manage AI/ML model versions through the model-versioning-tracker plugin, automating logging, lineage tracking, performance summaries, and version retrieval whenever users request registry or deployment support.

Weights & Biases (W&B) automates experiment logging, real-time dashboards, hyperparameter sweeps, artifact tracking, and model registry/versioning for ML teams using PyTorch, TensorFlow, or HuggingFace workflows.

This skill automates the entire software release workflow within Windsurf. It analyzes commit history based on conventional commits to determine the correct semantic version bump (major, minor, or patch). It then automatically generates comprehensive changelogs, manages Git tags, updates version files, and publishes the final release package to a registry. It is essential for teams implementing robust CI/CD practices.