Skill UI

This technique guides users to use precise chemical compound names, specific concentration values (e.g., "1.5 mg/L"), and taxonomic synonym counts as exact-match search anchors when querying biological literature. By leveraging quotation marks and combining multiple specific terms, it uniquely identifies research papers or species details, greatly improving search precision in biochemistry and botany.

This technique optimizes search precision when querying blog posts or articles. By wrapping unique phrases, specific titles, or direct quotes in quotation marks, the system treats the phrase as a single, exact unit. This prevents the search engine from breaking the phrase into individual words, which dramatically reduces noise and increases the likelihood of finding the intended source material.

This technique focuses on extracting highly distinctive and unique personal details—such as daily habits, specific routines, or unique life experiences—from personal blog posts. Instead of relying on generic topic keywords, it leverages these unique details to construct precise, quoted search queries, significantly improving the accuracy of information retrieval across vast web corpora.

This skill is designed to manage complex, multi-agent workflows by enabling communication and consultation with specialized AI agents. Use it when the user's request requires the expertise, second opinion, or specific output of another designated agent. It supports both synchronous front-end conversations (maintaining conversation context via session_id) and asynchronous background task submission and monitoring, ensuring seamless multi-agent collaboration.

This skill guides users on operating the FrontendIntegration resources and the associated frontend-forge extension using kubectl. It covers the full lifecycle management: creating FI from complete YAML, updating status, enabling/disabling, troubleshooting build output, and managing the extension's installation plan. Ideal for DevOps engineers responsible for robust CI/CD pipeline management.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

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 skill provides read-only functionality to query and list comprehensive information about KubeSphere clusters. It is designed for viewing cluster lists, checking current status, retrieving detailed specifications, and identifying component versions across multiple clusters. IMPORTANT: This operation is strictly read-only. Never use these commands for creating, updating, or deleting resources; always use the KubeSphere console for management actions.

Transform raw ideas, articles, or technical material into polished, engagement-optimized LinkedIn posts. This skill leverages Unicode typography (bold, italic) to simulate rich formatting that respects LinkedIn's plain text limitations. It utilizes proven structural patterns (Hook → Content → CTA, Listicle, Story) and best practices for professional social media marketing, helping users create high-impact, scannable thought leadership content.

This technique helps users solve highly complex movie trivia or cinematic research questions that involve multiple, intertwined constraints (such as specific awards, historical eras, directors, or plot elements). Instead of attempting to input all variables at once, it teaches the methodology of decomposing the main question into 2-3 independent, searchable dimensions, ensuring a systematic and accurate search process.

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.