Skill UI

Provides a detailed, multi-platform workflow to monitor long-running computational experiments. It checks the status of jobs across various environments (SSH, vast.ai, Modal), collects screen logs, fetches structured JSON results, and pulls detailed training metrics (loss curves, accuracy) from Weights & Biases (W&B). Essential for MLOps and automated result review.

This skill provides comprehensive automation for managing GitHub workflows across the entire development lifecycle. It covers detailed issue triage, PR review status checks, CI/CD failure debugging, official release management, and security monitoring using the GitHub CLI. Ideal for maintaining open-source project health and streamlining team development processes.

This skill generates deep, clickable URLs to various resources within the Arize monitoring UI. It supports linking to specific traces, spans, sessions, datasets, evaluators, and annotation configurations. It is essential for sharing specific debugging artifacts or navigating directly to key data points within the MLOps platform by requiring precise resource IDs and time ranges.

This toolkit provides comprehensive automated control over CircleCI CI/CD pipelines via Rube MCP. Users can trigger new builds, monitor workflow and job statuses, retrieve detailed job information, download build artifacts, and extract detailed test metadata. It streamlines complex DevOps operations by managing the entire CI/CD lifecycle.

The comprehensive command-line interface (CLI) for interacting with all major features of GitHub. This tool allows developers to manage repositories, create and review pull requests, track issues, utilize Codespaces, and perform advanced organizational tasks—all directly from the terminal. It is essential for streamlining DevOps workflows and automating GitHub operations.

A comprehensive guide detailing best practices for building, reviewing, and deploying Salesforce Flows. This skill covers selecting the correct Flow type (e.g., Screen, After-Save), ensuring bulk safety by preventing DML/SOQL in loops, implementing robust fault handling paths, and performing automation density checks to ensure scalable and resilient automation.

Provides comprehensive command-line management for all Azure DevOps resources. Users can manage projects, repositories, CI/CD pipelines, pull requests, work items, and artifacts using the Azure CLI with the dedicated extension. This tool is essential for automation scripting, integration testing, and streamlining complete DevOps workflows.

This skill guides the management of CI/CD pipelines within KubeSphere DevOps. It utilizes Kubernetes Custom Resources (CRs) to define, trigger, and monitor complex build workflows, seamlessly integrating with Jenkins concepts in a cloud-native manner. Use it for automating build, test, and deployment processes for applications running on Kubernetes.

This skill guides users on operating the FrontendIntegration resources and the associated frontend-forge extension using kubectl. It covers the full lifecycle management: creating FI from complete YAML, updating status, enabling/disabling, troubleshooting build output, and managing the extension's installation plan. Ideal for DevOps engineers responsible for robust CI/CD pipeline management.

PyTorch Lightning is an advanced deep learning framework built on PyTorch. It simplifies the entire ML workflow, allowing users to organize models into LightningModules, automate training loops using the Trainer, and manage complex data pipelines with DataModules. It supports advanced features like multi-GPU/TPU scaling (DDP, FSDP), integrated logging (W&B, MLflow), and custom callbacks, making large-scale neural network development efficient and reproducible.

This guide details the process of deploying production-grade inference services on CoreWeave using KServe. It covers setting up autoscaling, GPU scheduling, and implementing scale-to-zero functionality for efficient ML model serving endpoints. Ideal for MLOps engineers deploying large language models (LLMs) or other complex AI models in a production environment.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.