Skill UI

This technique guides users on how to construct highly effective search queries for movies. When a movie question contains both general plot descriptions and highly specific details (such as exact quotes, precise rating language, or specific award names), the user must prioritize the most discriminative clue. By focusing on unique identifiers, this method significantly improves the accuracy and efficiency of information retrieval.

This skill teaches users how to optimize search queries for specific, structural information found within academic theses or dissertations. It emphasizes the critical need to include qualifying terms like "acknowledgment," "thesis," or "dissertation" in searches to successfully retrieve details about committee members, dedicating recipients, or other section-specific data, rather than just topic abstracts. This dramatically improves the relevance and accuracy of information retrieval.

This skill is designed to facilitate communication and collaboration between different AI agents. Use it when you need to consult an expert agent, request a plan, seek a review, or continue a context-aware conversation with a specialized agent. It handles both real-time foreground chat and asynchronous background task submission, ensuring robust multi-agent workflows.

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.

Utilize advanced media intelligence to analyze audio, image, video, and text for synthetic manipulation and AI-generated content. This skill detects deepfakes, traces the original AI synthesis source, applies and detects watermarks, verifies speaker identity, and provides comprehensive media provenance analysis, ensuring media authenticity claims are rigorously supported.

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.

This skill is designed to translate vague, colloquial, or descriptive biological details (such as behaviors, appearances, or cultural uses) into precise scientific or common species names. It is essential for constructing effective search queries in biological databases when the question does not use formal taxonomic names. By accurately identifying candidate species names from descriptive clues, users can drastically improve search relevance and accuracy.

This technique helps locate video games when the primary information provided is not the game title, but rather indirect clues about the game's developing company. Clues can include the company's name meaning, founder background, corporate history, or foundation activities. You must first decode these clues to identify the company, and then search the company's associated game catalog.

This coach guides users through a progressive mastery of AI, structured across three layers: AI User (prompting), AI-Enhanced Worker (workflow integration), and AI Builder (ML foundations). It uses Socratic questioning, layered scaffolding, and spaced repetition to build genuine, transferable AI literacy, moving beyond simple copy-pasting prompts to deep conceptual understanding of models, transformers, and RAG.

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.

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

ESM provides advanced protein language models (ESM3, ESMC) for deep biological understanding, generation, and structural prediction. This skill enables users to generate novel protein sequences with specific functions, perform structure prediction from sequence, execute inverse folding (sequence design from structure), and extract high-quality protein embeddings for downstream machine learning tasks. Ideal for drug discovery and advanced bioinformatics research.