Skill UI

Guided onboarding that converses about your role, audiences, and examples so the Roundup skill can learn your voice and auto-generate tailored status briefings.

This skill manages a persistent, structured personal knowledge base (Wiki). It provides core operations for ingesting external articles, synthesizing insights from conversations, and querying stored knowledge. By maintaining structured files, index lists, and cross-references, it helps build a comprehensive knowledge graph, ensuring all insights are captured and easily retrievable.

This skill enables the proactive, one-way sending of messages to specific users, sessions, or channels (e.g., Feishu, Discord). It is ideal for automated notifications, task completion alerts, or status updates when a reply is not expected. Before sending, the workflow requires querying available sessions to obtain necessary target IDs.

This comprehensive tool suite provides programmatic automation for Todoist task management. Users can manage the entire task lifecycle, including creating, updating, completing, and deleting tasks. It also supports structured organization by managing projects and sections, and filtering tasks using advanced workflows. Ideal for integrating task management into automated business processes or applications.

Guides Copilot through the WorkIQ CLI/MCP server to fetch live Microsoft 365 data—emails, meetings, docs, Teams chatter, and people info—providing context, summaries, blockers, and follow-up recommendations for planning or action.

A comprehensive automation toolkit for managing Zendesk operations via Rube MCP. It provides workflows to list, search, create, update, and delete support tickets, manage user accounts (agents and end-users), and handle organization details. Ideal for integrating support workflows and automating customer service processes.

This guide details advanced techniques for searching cricket matches by leveraging precise numerical scoring data. Instead of broad terms, users should utilize specific details—such as team totals (e.g., 168/9), runs off balls, wickets taken, or over details—combined with the match type and year to create highly targeted search queries. This method significantly improves search accuracy and efficiency when researching sports statistics.

This technique guides users on decomposing multi-constraint questions, especially in the domain of football trivia or historical research. Instead of searching all criteria simultaneously, it instructs users to rank constraints by distinctiveness (e.g., specific match results first, then historical milestones). By performing incremental, cascading searches, the system dramatically narrows down the result set, leading to more accurate information retrieval.

This skill analyzes cultural clues within a query to determine the potential origin (e.g., Korean, Turkish, Indian, Thai) of a non-English TV drama. By identifying specific regional indicators—such as actor naming conventions, cultural themes, or broadcast channels—the system can generate highly precise, region-specific search keywords. This drastically improves search accuracy when the source country is not explicitly mentioned.

A guide for refining search queries when researching Indian or South Asian actors. It emphasizes the necessity of including domain keywords such as "Bollywood," "Hindi film," or specific Indian award names (e.g., "Filmfare Award") to filter out irrelevant Western search results and drastically improve search precision.

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 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.