Skill UI

Refactors a specified method into smaller helper routines so the cognitive complexity stays below a given threshold while keeping validation, error handling, and behavior intact.

A comprehensive template for planning large-scale, multi-file refactoring tasks. It enforces a systematic approach by requiring developers to map out the current state, target state, affected files, and execution phases (Types, Implementation, Tests). Crucially, it mandates detailed verification and a robust rollback plan to ensure code stability during major changes.

Acts as a senior software engineer to meticulously review and refactor entire codebases. This skill ensures that the code is clean, highly maintainable, and strictly adheres to defined coding standards and best practices specified across multiple project instruction files. It guarantees the integrity of the existing file structure and ensures all unit tests remain passing after modifications.

A comprehensive guide covering best practices for Terraform and OpenTofu. It provides structured advice on module hierarchy, directory organization, naming conventions, and advanced testing strategies (including native testing and policy as code). Use this skill when designing, refactoring, or implementing robust Infrastructure as Code (IaC) pipelines and multi-environment deployments.

This skill systematically reviews test code to identify and correct common anti-patterns. It detects issues such as flaky tests, overly mocked dependencies (over-mocking), brittle assertions, excessive test size (The Giant), and structural dependencies. It explains the flaw, provides best practices, and suggests refactored, reliable implementations, helping maintain high test quality and stability.

This skill analyzes cultural clues within a query to determine the potential origin (e.g., Korean, Turkish, Indian, Thai) of a non-English TV drama. By identifying specific regional indicators—such as actor naming conventions, cultural themes, or broadcast channels—the system can generate highly precise, region-specific search keywords. This drastically improves search accuracy when the source country is not explicitly mentioned.

This guide details strategies for finding the full legal birth names of European actors, particularly those from French and Eastern European backgrounds. When English sources or IMDb only list stage names, this technique emphasizes using regional resources like French Wikipedia and specific cultural identifiers (e.g., "People's Artist" USSR) to perform deep, targeted searches, ensuring the retrieval of accurate birth details.

A guide for refining search queries when researching Indian or South Asian actors. It emphasizes the necessity of including domain keywords such as "Bollywood," "Hindi film," or specific Indian award names (e.g., "Filmfare Award") to filter out irrelevant Western search results and drastically improve search precision.

This technique is designed for identifying target Indian actors when the clues are indirect, often pointing to a relative (father, spouse, grandfather) instead of the actor directly. It requires systematic, step-by-step tracking through the family lineage to deduce the identity. Mastering this method allows users to solve complex Bollywood trivia questions by following the family connections.

This technique is designed for entity recognition and hypothesis generation when identifying Indian or Bollywood actors based on indirect clues. Instead of running broad searches, users should hypothesize a small set of candidate names (1-3) using the provided clues (e.g., awards, family history, controversies). Subsequently, targeted verification searches must be executed for each constraint against the candidate names to confirm the correct identity.

This technique is designed to identify South Asian (Indian/Bollywood) films when the question provides insufficient information to name the movie directly. Instead, it requires chaining together the biographical details of the actor or director—such as birth year, debut era, family relations, or awards—to first uniquely identify the person, and then use that identity to locate the target film and verify all constraints.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.