Skill UI

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.

This skill guides the implementation of comprehensive security controls for container registries. It details workflows for vulnerability scanning using Trivy and Grype, generating Software Bill of Materials (SBOMs) with Syft, and enforcing image authenticity using Cosign and Sigstore. It is essential for building robust CI/CD pipelines and preventing supply chain attacks by ensuring only scanned, signed, and compliant images are deployed.

This skill provides comprehensive guidance on hardening GitHub Actions CI/CD pipelines against modern supply chain attacks, credential theft, and privilege escalation. It teaches best practices such as pinning actions to immutable SHA digests, minimizing GITHUB_TOKEN permissions, preventing script injection from untrusted inputs, and implementing required approval gates for critical deployments.

This comprehensive guide outlines the end-to-end security lifecycle for Kubernetes applications deployed via Helm. It covers critical topics such as chart provenance verification (GPG signing), template scanning (using tools like kubesec and checkov), enforcing strict security contexts, managing secrets securely (ExternalSecrets), and defining robust RBAC rules for CI/CD pipelines.

This skill provides comprehensive security hardening guidelines for serverless compute platforms like AWS Lambda, Azure Functions, and Google Cloud Functions. It covers essential security pillars including enforcing least privilege IAM roles, integrating robust secrets management (e.g., AWS Secrets Manager), automating dependency vulnerability scanning within CI/CD pipelines, and implementing strict input validation to mitigate risks such as injection and credential theft. Ideal for DevSecOps practitioners and security architects.

Orchestrates WCAG 2.2 Level A/AA scans, fixes, and verifications across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases, producing before/after diffs, contrast checks, compliance reports, and CI/CD-ready hooks.

Master the use of the `gh` CLI to interact with core GitHub features. This guide covers managing issues, checking Pull Request statuses, listing and debugging CI workflow runs, and executing advanced data queries via the GitHub API. Essential for automating DevOps workflows and debugging CI/CD pipelines directly from the terminal.

A comprehensive guide to designing scalable, cost-effective, and compliant architectures on Microsoft Azure. This skill covers the entire lifecycle, from gathering detailed business requirements (SLA, RPO/RTO) and selecting appropriate service patterns (AKS, Serverless, Data Factory), to generating Infrastructure as Code (IaC) using Bicep. It also includes steps for cost optimization, reserved instance planning, and setting up robust CI/CD pipelines via Azure DevOps or GitHub Actions.

This comprehensive workflow guides the design, optimization, and deployment of scalable, cost-effective Google Cloud Platform (GCP) architectures for both startups and large enterprises. It covers the full lifecycle, from gathering detailed requirements (compliance, SLA) and recommending optimal patterns (Serverless, GKE, Data Pipelines) to estimating costs, generating Infrastructure as Code (IaC) using Terraform, setting up CI/CD pipelines, and completing a thorough security review.

Coordinated end-to-end testing workflow that leverages Playwright for browser automation, visual regression comparisons, cross-browser coverage, and CI/CD integration so teams can validate web apps before release.

A comprehensive tool for establishing performance baselines across multiple dimensions. It measures Core Web Vitals (LCP, CLS, INP), API latency (p50, p95, p99), and development build efficiency. Essential for detecting performance regressions before merging code, ensuring stability in CI/CD pipelines, and comparing alternative stack implementations.

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.