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.

This skill assists developers in understanding, writing, and validating EAS workflow YAML files for Expo projects. It provides comprehensive guidance on CI/CD pipelines, job definitions, triggers, and expression syntax, ensuring configurations are accurate and up-to-date by referencing official Expo schemas. Use this when automating builds or deployments via EAS.

Semgrep is an open-source static analysis tool used to find bugs, detect vulnerabilities, and enforce coding standards. This guide covers writing custom rules in YAML format to implement advanced SAST checks, such as detecting SQL injection, hardcoded secrets, or tracking tainted user input. It is essential for building robust DevSecOps pipelines and ensuring compliance.

Kubesec is a powerful open-source tool designed to perform comprehensive security risk analysis on Kubernetes resource manifests (YAML/JSON). It automatically detects critical misconfigurations, potential privilege escalation vectors, and deviations from industry security best practices. It provides actionable scoring and hardening recommendations, making it essential for CI/CD pipelines, security auditing, and automated compliance checks.

A comprehensive guide and toolset for Go code quality assurance using `golangci-lint`. It enables developers to run multiple linters in parallel, configure best practices via `.golangci.yml`, automate fixes, and interpret complex lint output. Ideal for maintaining code standards, setting up CI/CD pipelines, and cleaning up legacy codebases.

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.