Skill UI

Defines Salesforce Lightning Web Component, Aura, and Visualforce quality/security standards covering SLDS 2 styling, accessibility (WCAG 2.1 AA), data access patterns, component communication rules, XSS/CSRF protections, view state management, and Jest testing requirements for guided UI development and reviews.

A comprehensive guide for writing and reviewing robust, behavior-first unit tests for Vue 3, TypeScript, and Pinia stores. This skill covers advanced patterns for testing components, composables, and state management using Vitest and Vue Test Utils, emphasizing black-box assertions and minimizing implementation coupling.

This skill is designed to generate high-quality UX microcopy, including button labels, error messages, empty states, and toast notifications. It ensures the copy maintains a casual but polite, active, and concise tone, adhering to modern best practices in UX writing and design language guidelines.

This skill guides the implementation of essential user feedback states—Loading (Skeleton), Empty (Zero Data), Error, and Success—for data-dependent components or entire pages. It ensures a robust and polished user experience by providing structured, best-practice patterns for handling various data scenarios, preventing visual gaps or dead ends in the UI flow.

A comprehensive guide and mechanism for ensuring development work occurs in an isolated environment using native worktree tools or falling back to manual git worktree creation. It guides users through mandatory steps like detecting existing isolation, prioritizing native tools, and verifying the clean state before development begins.

This technique is used when the required answer in a case query is not directly stated but must be deduced by following a sequence of connected roles, people, or events. It involves mapping complex relationships, such as Victim -> Defendant -> Lawyer -> Witness, to trace indirect connections and reach the final piece of information. Ideal for advanced investigative research and deep knowledge graph traversal.

A structured methodology for precisely identifying specific geographic locations or historical landmarks. This technique systematically narrows the search scope, moving from broad regions (countries/former states) down through administrative divisions, city centers, and finally to specific monuments or structures. It requires utilizing diverse clues like historical events, census data, and distance constraints, while critically verifying assumptions at each step to ensure accuracy.

This specialized coach provides comprehensive guidance for finance professionals, covering diverse fields such as investment banking, private equity, asset management, and fintech. It teaches core technical skills (DCF, LBO, 3-statement modeling) and prepares users for rigorous industry recruiting, including case studies and technical interviews. It addresses both Chinese and global financial markets, helping users strategically navigate career barriers and build professional expertise.

A comprehensive guide for configuring ArgoCD within KubeSphere DevOps. It details implementing GitOps continuous deployment, covering application management, multi-cluster setup, SSO integration via Dex, and best practices for managing application state synchronization from Git repositories. Learn the differences between direct ArgoCD and KubeSphere's tenant-friendly GitOps application creation.

Pymoo is a comprehensive Python framework designed for solving both single-objective and multi-objective optimization problems. It implements state-of-the-art evolutionary algorithms (like NSGA-II, NSGA-III, MOEA/D) and is specialized in identifying Pareto-optimal fronts. This tool is essential for engineering design and scientific problems requiring the analysis of trade-offs among multiple conflicting objectives.

This skill guides the creation of highly competitive research grant proposals for major funding bodies like NSF, NIH, DOE, DARPA, and NSTC. It covers all critical components, including significance statements, innovation narratives, budget planning, compliance with specific agency guidelines (e.g., PAPPG, R01), and structuring the proposal to maximize funding success. It is essential for academic and R&D professionals.

Stable Baselines3 is a robust, PyTorch-based library providing reliable implementations of state-of-the-art reinforcement learning algorithms (PPO, SAC, DQN, etc.). It is designed for standard RL experiments, quick prototyping, and developing complex single-agent systems. The library supports training agents, creating custom Gymnasium environments, and running vectorized simulations for accelerated learning.