Skill UI

Applies proven testing patterns (AAA, Given-When-Then, builders, spies, doubles) to help write maintainable unit, integration, and end-to-end suites, covering structuring, naming, TDD/BDD, and mocks across frameworks like Jest, Vitest, or pytest.

This skill is designed to manage design tokens within a design system, ensuring that the source-of-truth files—including JSON manifests, CSS variables, and dark-mode values—remain perfectly synchronized. It allows users to list, safely add, or update tokens (like colors, shadows, or spacing) while automatically propagating changes across all implemented files, maintaining system integrity.

This skill helps enhance the robustness of data-dependent user interfaces (UIs) by implementing the four critical feedback states: loading, empty, error, and success. It ensures that applications provide clear, graceful, and actionable feedback regardless of data fetching status, making the product reliable and user-friendly.

This skill provides comprehensive guidelines for contributing and managing commands within VS Code extensions. It covers crucial best practices, including naming conventions, defining visibility (e.g., Command Palette vs. Side Bar), and structuring commands for optimal user experience. Use this when adding, updating, or understanding the architectural requirements for extension commands.

This skill helps users maintain a structured and comprehensive record of their daily activities, progress, key decisions, and learning outcomes. It creates a dedicated file for each day using the ISO date format (yyyy-mm-dd), ensuring that all entries are organized chronologically. By adhering to concise, third-person reporting, it helps users build a valuable, reviewable knowledge base of their journey and accomplishments.

This guide details advanced search techniques for researching musical artists from non-Western regions, including K-pop, Middle Eastern, African, and Latin American genres. It instructs users on moving beyond standard English search terms by incorporating regional keywords, cultural identifiers (like UN Ambassador status), specific platforms (e.g., Coke Studio), and systematic regional branching to find comprehensive information.

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.

This skill helps translate vague or descriptive language used in questions about historical events, figures, or cultures into specific, proper historical terms or nouns. It moves beyond literal search queries by recognizing underlying concepts (e.g., translating 'exaggerate facts about a region' to 'tall tales'). It is particularly effective for researching global history, providing crucial techniques for narrowing down searches related to South Asian, Middle Eastern, and African contexts where standard Western sources might be insufficient.

A comprehensive guide for configuring and troubleshooting Jenkins when embedded within KubeSphere DevOps. Learn how to manage Jenkins via Configuration as Code (CasC), integrate advanced authentication methods like LDAP and OIDC, customize agent images, and connect to external sources such as GitLab. Covers everything from initial access to backup and recovery.

Provides programmatic access to the CZ CELLxGENE Census (61M+ cells) for querying standardized single-cell expression, metadata, embeddings, and stats across tissues and diseases, supporting scanpy and ML workflows.

Combines ESM3 and ESM C protein language models to generate sequences, predict structures, condition functions, and extract embeddings via local or Forge API inference for protein design and engineering workflows.

Geniml is a comprehensive Python package designed for applying machine learning techniques to genomic interval data stored in BED files. It provides advanced methods like Region2Vec, BEDspace, and scEmbed to learn embeddings for genomic regions, single cells, and metadata. Use it for dimension reduction, clustering, similarity searches, and sophisticated bioinformatic analyses in genomics. Core functionalities include: learning region embeddings (Region2Vec), joint embeddings of regions and metadata (BEDspace), single-cell chromatin accessibility embedding (scEmbed), and building consensus...