Skill UI

Automated help for npm script workflows inside DevOps Basics, offering guided best practices, production-ready configs, and validation for CI/CD or infrastructure requests.

This skill guides the creation of structured, importable JSON templates for the Obsidian Web Clipper. It facilitates the analysis of a target website's content structure, including DOM, Schema.org data, and CSS selectors. Users can map complex web elements into a robust template format, incorporating advanced logic like conditionals and loops, ensuring the resulting clipboard data is consistently formatted and ready for immediate use.

Enables Claude to act as a FinOps expert by analyzing cloud usage, generating cost reports, and recommending configuration changes to lower AWS, Azure, or GCP spending whenever the user asks to optimize cloud costs.

Automated guidance for DevOps practitioners handling package json manager tasks, offering best practices, configuration generation, and validation for foundational CI/CD and infrastructure workflows.

Automates the steps for configuring pre-commit hooks, guiding through best practices, generating configs, and validating results for DevOps or version-control workflows.

Automates Project Bubble workflows via Rube MCP and Composio toolkits, emphasizing schema discovery, connection checks, and multi-tool execution for reliable project ops.

Generates Prometheus configuration files on demand, guiding DevOps engineers through best practices for Kubernetes, Terraform, CI/CD, and monitoring while validating outputs against common standards.

Guides adding PyTorch FSDP2’s fully_shard integration to training loops, covering correct distributed init, bottom-up sharding, mixed precision/offload policies, DTensor-aware optimizers, and distributed checkpointing so oversized models or DeviceMesh sharding goals work reliably.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

Automate Ramp corporate finance ops: query transactions, reimbursements, cards, and user data for accounting, expense reviews, and ERP sync readiness across departments.

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.

Auto Readme Generator offers guided documentation workflows for DevOps basics, triggering whenever "readme generator" is mentioned, and delivering best-practice code, configurations, and validations to support version control, containerization, and CI/CD.