Skill UI

Analyzes a codebase to surface subdomains (Core, Supporting, Generic) and recommend bounded contexts using DDD strategic design, helping teams clarify domain boundaries, cohesion, and service split decisions.

This skill acts as a comprehensive security scanner specializing in detecting Cross-Site Scripting (XSS) vulnerabilities in client-side code. It performs static analysis across React, Vue, Angular, and vanilla JavaScript, specifically identifying unsafe practices like direct HTML manipulation (innerHTML), raw rendering (v-html), and insecure URL handling. Use it when auditing frontend code for security flaws and needing best practices for secure coding.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

This skill orchestrates a comprehensive, multi-stage Git development workflow, simulating the process from code commit to PR preparation. It leverages specialized AI agents (code reviewers, test automators, prompt engineers) to systematically enforce best practices. Key functions include code quality assessment, breaking change analysis, running unit/integration/E2E tests, and generating Conventional Commit messages, ensuring code readiness and high quality before merging.

Automated heap dump analysis guidance for performance testing, offering step-by-step instructions, best-practice patterns, production-ready code and configs, and validation against common standards.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

This sophisticated skill automates the entire Pull Request (PR) quality assurance lifecycle. It systematically fetches and categorizes CI check failures and detailed review feedback using the LOGAF priority scale. The workflow guides users through root cause analysis, proposing minimal, targeted fixes, replying to reviewers, and committing updates iteratively until all checks pass, ensuring high code quality before merging.

This comprehensive guide addresses common technical and business logic errors encountered when integrating with the Juicebox API. It covers HTTP status codes (e.g., 401, 403, 429), including authentication failures, quota exhaustion, rate limiting, data validation issues, and analysis timeouts. The resource provides detailed causes, specific fixes, and best practices for building reliable and robust integrations into recruiting and research workflows.

A mandatory workflow for ensuring high code quality across multiple ecosystems, including Node.js/TypeScript and Python. This pipeline runs static analysis tools—such as linters, type checkers (MyPy, tsc), and security scanners (Bandit)—after every code change. It mandates that no task is considered 'done' or committed until all validation checks pass, resolving style, syntax, and type mismatches.

An expert guide for performing complex data analysis, cleaning, and transformation using pandas DataFrames. This skill covers advanced techniques such as vectorized operations, efficient merging, time series resampling, group-by aggregations, and memory optimization, ensuring production-grade code and robust data validation.

This skill enables the coordination of multiple specialized AI agents within a single session. It is designed for complex, multi-domain tasks such as comprehensive code reviews, security audits, or full-stack feature implementations. It coordinates expertise across various domains (backend, frontend, testing, architecture) to provide holistic analysis and actionable recommendations.

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.