Skill UI

This technique is used when the required answer in a case query is not directly stated but must be deduced by following a sequence of connected roles, people, or events. It involves mapping complex relationships, such as Victim -> Defendant -> Lawyer -> Witness, to trace indirect connections and reach the final piece of information. Ideal for advanced investigative research and deep knowledge graph traversal.

A structured methodology for precisely identifying specific geographic locations or historical landmarks. This technique systematically narrows the search scope, moving from broad regions (countries/former states) down through administrative divisions, city centers, and finally to specific monuments or structures. It requires utilizing diverse clues like historical events, census data, and distance constraints, while critically verifying assumptions at each step to ensure accuracy.

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.

Acontext serves as a robust memory layer for production-grade AI agents. It provides persistent sessions, disk-based storage, and sophisticated learning spaces that distill complex conversations into reusable, structured skills. Users can manage the entire system—from project setup and login to skill uploading—via a powerful Command Line Interface (CLI) and API, significantly enhancing agents' contextual understanding and long-term knowledge retention.

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.

Consult another agent for help, plans, reviews, or decision support in multi-agent workflows. Explains using list_agents() and chat_with_agent(), background submission and status checks, session ID reuse, and loop-avoidance best practices.

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.

This skill guides users in analyzing complex questions related to criminal or legal incidents (e.g., murder, theft, arson, accident). By first classifying the specific case type and identifying domain-specific keywords, it dramatically improves the precision of subsequent search queries. It teaches users how to combine case type terms with distinctive constraints like amounts, dates, and locations for maximum search effectiveness.

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.