Skill UI

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 specialist that combines automated static analysis (SonarQube, CodeQL, Semgrep) with advanced AI capabilities. This tool performs multi-layered code reviews assessing security vulnerabilities, performance bottlenecks, architectural coherence, and maintainability across various programming languages, integrating seamlessly into CI/CD pipelines.

This comprehensive guide provides a step-by-step walkthrough for deploying a minimal, functional full-stack application to Vercel. It covers setting up both static HTML landing pages and robust serverless API routes using TypeScript. This skill is ideal for beginners starting new Vercel projects, testing deployment pipelines, or learning the core patterns of modern serverless function implementation.

This skill provides AI-assisted capabilities for creating and managing Continuous Integration/Continuous Deployment (CI/CD) workflows using GitHub Actions. It can generate new workflows from project requirements, optimize existing pipelines, debug failures, and implement industry-standard security best practices, streamlining the entire development and deployment process.

PyOpenMS provides Python bindings for OpenMS to drive complete mass spectrometry workflows, covering data IO, spectral processing, feature/peptide-protein identification, quantitation, and metabolomics pipelines.

Use Kubesec to analyze Kubernetes YAML/JSON manifests for misconfigurations, privilege escalation risks, and missing controls, delivering scores and remediation hints via CLI, Docker, kubectl plugin, or REST API for DevSecOps and CI/CD pipelines.

A comprehensive guide and toolset for Go code quality engineering using `golangci-lint`. It covers setting up production-grade linters via `.golangci.yml`, running automated checks, auto-fixing issues, and interpreting complex lint output. Use this when enforcing coding standards, improving CI/CD pipelines, or managing code compliance in large Go projects. It treats linting as an integral part of the development workflow.

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.

This tool provides comprehensive automation for core GitLab operations, streamlining the entire development lifecycle. Users can manage projects, track issues, review merge requests, manage branches, and monitor CI/CD pipelines. It leverages a unified API interface to handle complex workflows, ensuring efficiency in version control and collaborative development environments.

This tool provides comprehensive automation capabilities for managing cloud infrastructure on the Render platform via Rube MCP. Users can list, filter, and browse various services (web services, static sites, etc.), manually trigger new deployments, and continuously monitor the deployment status (e.g., checking for 'live' status or catching 'build_failed' errors). It is essential for streamlining CI/CD pipelines, managing projects, and ensuring reliable, automated service updates without manual intervention.

This comprehensive automation toolkit manages core Bitbucket functionalities, including repositories, workspaces, pull requests, and issues. It enables programmatic control over development workflows—from creating and reviewing pull requests to listing repos, tracking issues, and managing workspace members. Essential for integrating CI/CD pipelines and streamlining the entire software development lifecycle.