Skill UI

EverOS OpenClaw Plugin integrates persistent, long-term memory capabilities into OpenClaw using the ContextEngine API. It goes beyond simple memory slots by automatically recalling relevant past context before a reply and saving new conversation content after a turn. This allows users to chat naturally while ensuring context, preferences, and key details persist across multiple sessions, eliminating the need for manual memory calls.

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 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 tool generates canonical, submit-ready FrontendIntegration YAML from a simplified, single-menu authoring model. It is ideal for converting preliminary configurations (like those derived from a CRD) into the full, structured YAML required for deploying frontend integrations, ensuring all fields and placements are correctly mapped.

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.

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.

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.