Skill UI

A comprehensive tool for detecting, validating, and generating Schema.org structured data. It primarily focuses on the JSON-LD format, which is preferred for modern SEO best practices and enabling rich results. Use this when optimizing content markup, checking for validation errors, or generating structured snippets for articles, businesses, or products.

This skill scaffolds the complete boilerplate structure for a new mobile screen or page. It ensures that the resulting component adheres to established design system patterns, including Top Bars, Bottom Navigation, and standard content shells. Use it when starting a new dedicated mobile view and needing immediate adherence to design tokens and layout rules.

This guide provides a comprehensive template and best practices for updating technical specification files. It ensures that the resulting documentation is structured, unambiguous, and highly optimized for consumption by Generative AI models. Use this process when refining system requirements, updating interfaces, or documenting architectural changes to maintain machine-readable standards.

This skill provides a comprehensive audit of user interfaces, evaluating screens against Nielsen's 10 Usability Heuristics and modern mobile UX best practices. It systematically checks for critical usability flaws, design inconsistencies, accessibility gaps, and potential dark patterns, ensuring the resulting product delivers a superior and intuitive user experience across all platforms.

This technique guides users on decomposing multi-constraint questions, especially in the domain of football trivia or historical research. Instead of searching all criteria simultaneously, it instructs users to rank constraints by distinctiveness (e.g., specific match results first, then historical milestones). By performing incremental, cascading searches, the system dramatically narrows down the result set, leading to more accurate information retrieval.

This skill guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.

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 advanced research technique is designed for deep dives into football topics. Instead of relying solely on the original query, it mandates tracking every discovered entity—such as player names, club names, or coaches—from search results. By treating these entities as new leads, the user must formulate follow-up searches using the discovered names and remaining constraints, ensuring nothing relevant is overlooked and building a comprehensive knowledge chain.

This guide covers the entire South Asian film ecosystem, including Bollywood (Hindi), Tollywood (Telugu), Kollywood (Tamil), Malayalam, Bengali, and Pakistani cinema. It teaches users how to identify regional clues and conduct comprehensive searches, ensuring results are not limited only to Hindi films.

This technique guides users on how to construct highly specific search queries for academic papers. Instead of relying solely on broad topic keywords, users should prioritize combining concrete numerical identifiers—such as exact sample sizes, population percentages, concentration levels, or specific year ranges—with methodological terms and discipline keywords. Using these numerical anchors significantly increases the uniqueness and precision of the search results, helping to pinpoint the exact study required.

This skill teaches how to dramatically improve the accuracy of historical research queries. When searching for information about a historical figure, rather than relying on generic facts (like era or nationality), users should identify and prioritize the most unique, specific, or niche details—such as a unique object, a rare event, or an uncommon biographical fact. By using these distinctive details as quoted search anchors, the search results become highly targeted, overcoming the limitations of broad, general queries.

This technique guides users on how to solve highly complex questions that require tracking a chain of related entities (e.g., Author -> Article -> Mentioned Person). Instead of attempting a single search, the process must be broken down into sequential steps, using the result of one search as an anchor point for the next, thereby building a comprehensive knowledge chain.