Skill UI

This skill provides comprehensive guidance on leveraging d3.js (Data-Driven Documents) to create sophisticated, interactive, and publication-quality data visualizations. It covers core concepts like data binding, implementing various chart types (network graphs, geo-maps, etc.), handling complex transitions, and ensuring responsiveness across modern JavaScript frameworks (React, Vue, Vanilla JS). Learn the standard workflow for scale definition, axis creation, and drawing custom visual elements with fine-grained control.

This skill provides comprehensive frameworks for conducting thorough competitive landscape analysis. It guides users in identifying market gaps, pinpointing key differentiation opportunities, and developing robust, winning market positioning strategies. Ideal for product managers, strategists, and business analysts needing structured guidance on market entry and growth.

Expert .NET backend architect guidance for C#, ASP.NET Core, Entity Framework, Dapper, caching, testing, and DevOps so teams can build production-grade APIs, microservices, and enterprise applications with clear patterns and performance best practices.

Provides automated guidance for configuring learning rate schedulers during ML training, covering best practices, setup patterns, and framework-ready code snippets for PyTorch, TensorFlow, and Scikit-learn while validating against common standards.

Step-by-step framework for executing complete platform migrations to or from MaintainX, covering prerequisites, sample implementation guidance, data mapping, validation checkpoints, error handling, and rollback planning for CMMS or spreadsheet transitions.

Structured guidance for TAM/SAM/SOM calculations, combining top-down, bottom-up, and value-theory methods for startup market analysis and fundraising planning.

This skill provides comprehensive guidance on training Mixture of Experts (MoE) models, including architectures like Mixtral and DeepSeek-V3. It details techniques for achieving large-scale model capacity with significantly reduced computational costs (e.g., 5x reduction vs. dense models). Topics covered include top-k routing, load balancing, expert parallelism, and integration with frameworks like DeepSpeed and HuggingFace for efficient, resource-constrained training.

Provides a decision framework and best practices for architecting workloads across AWS, Azure, and GCP, covering patterns, service comparisons, migration phases, cost optimization, and cloud-agnostic infrastructure guidance.

Guidance for executing comprehensive end-to-end suites that cover browsers, APIs, databases, and integrations with Playwright, Cypress, Selenium, or Puppeteer, including setup, page objects, multi-browser matrix, retries, and reporting.

Expert AI-powered test automation guidance covering modern frameworks, self-healing suites, CI/CD integration, performance and data strategies for scalable quality engineering.

Test Data Builder Guidance auto-activates when you ask about test data strategies, best practices, or configuration for automation frameworks, delivering step-by-step patterns, production-ready samples, and validation against common standards.

Automates guidance for configuring test retries in automation suites, walking through setup, best practices, and frameworks such as Jest or pytest so teams can stabilize flaky runs.