Skill UI

This guide teaches targeted verification strategies for historical research. When initial searches suggest a candidate name or figure, do not continue with broad searches. Instead, immediately switch to verifying the candidate against all specific constraints (time period, location, family details, major events) using precise search queries to confirm identity and eliminate false positives.

This guide details the operational workflow for namespace-scoped tenants within KubeSphere DevOps. It outlines how users without cluster-admin privileges can securely perform DevOps operations by strictly utilizing KubeSphere's dedicated APIs (`/kapis/`) and authenticating via OAuth tokens, ensuring strict resource isolation and access control within their designated namespace.

This skill is designed for understanding complex database structures. It allows users to list all available tables, intelligently filter relevant tables based on natural language queries, retrieve detailed schemas (columns, types, primary/foreign keys), and map inter-table relationships. It culminates in generating the M-Schema format, which is essential for accurately preparing the context needed for advanced SQL query generation.

A technique for dramatically increasing the hit rate when searching for TV series. Instead of using generic terms like genre or era, combine 2-3 highly distinctive plot details (e.g., character sacrifices, specific season incidents) into a single query. This method leverages the unique nature of plot points to pinpoint specific shows quickly.

This technique guides users in solving trivia or puzzle questions where the target band's country is not explicitly named. Instead, the country is hinted at through indirect geographic clues such as land area ranges, EU membership dates, or national flag descriptions. It emphasizes systematic deduction, requiring the user to treat the clue as a multi-candidate puzzle and test all viable countries before refining the search to the specific band name.

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.

Transforms the AI into a highly personalized virtual tutor, adopting a defined persona, teaching style, and emotional relationship. This skill uses the Socratic method and advanced psychological principles (like Self-Determination Theory and memory scaffolding) to guide users through discovery learning. It doesn't just deliver answers; it fosters genuine engagement, maintains continuity across sessions, and motivates the user to learn by simulating the experience of a caring, challenging human mentor.

KubeSphere Core acts as the central operating system for cloud-native application management built on Kubernetes. It provides essential functions, including API aggregation, multi-cluster routing, and multi-tenant separation. It manages core resources and simplifies complex cloud-native deployment and operation tasks.

This technique leverages structured data from music awards (like Grammy nominations and wins) to act as powerful search anchors. By extracting clues such as the award category, year, and status (nominated/won), users can cross-reference potential musicians against verifiable award databases. This method significantly narrows down the pool of candidates, allowing for highly accurate identification and verification of artists.

A specialized technique for searching Asian dramas that frequently use multiple titles in various languages (Korean, Turkish, etc.). This method advises using the original language title or leveraging the 'alternative titles' pattern when standard English searches fail, significantly improving search discrimination and accuracy for global media content.

This guide teaches advanced search techniques for identifying specific video games. It emphasizes using highly discriminative technical details such as specific platforms (e.g., DOS, NES, PS4), distribution methods (shareware), game engines, era, and detailed review scores from niche sites. By combining multiple constraints (Platform + Era + Detail), users can dramatically narrow down search results, even locating specific developers or artists involved in the game's creation.

A powerful utility for performing semantic searches across a large knowledge base of 105K+ skill cards. It allows users to find the most relevant skills based on natural language queries, supporting both Python API integration and Command Line Interface (CLI). This tool is zero-dependency and highly efficient for skill discovery and knowledge retrieval in development workflows.