Skill UI

This tool is designed for managing and retrieving user contact information within the Lark/Feishu ecosystem. Users can search for employees using their name or email to obtain their unique OpenID. Furthermore, it allows querying detailed user profiles (such as department, email, and contact details) using a known OpenID. It is essential for automating message sending, scheduling meetings, or fetching user details when only an ID is available.

A comprehensive skill for managing and retrieving video meeting information from Feishu. Users can search detailed meeting records (only for concluded meetings) using various filters (keywords, time range, participants, etc.). Furthermore, it supports generating, retrieving, and organizing structured meeting minutes, including summaries, action items, chapters, and full transcripts.

Automate Amplitude product analytics by orchestrating Composio's Rube MCP toolkit to send events, resolve user activity, manage cohorts, and sync identities while always querying RUBE_SEARCH_TOOLS for current schemas.

Automate Datadog monitoring tasks via Rube MCP’s Composio toolkit—query metrics, search logs, manage monitors/dashboards, and create events or downtimes while respecting live tool schemas.

Connects to Google NotebookLM to query your uploaded documentation via Gemini’s source-grounded answers, automating authentication, notebook management, and question workflows through the provided run.py wrapper.

This tool functions as a persistent, structured knowledge base for the entire research lifecycle. It accumulates and maps all core research entities—including published papers, generated ideas, experiments, and testable claims—along with their complex relationships (e.g., contradiction, support, inspiration). Inspired by LLM Wiki patterns, it ensures that knowledge is compounded and perpetually queryable, moving beyond single-use literature reviews.

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.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

Paperzilla is a comprehensive skill for interacting with curated academic research data. Users can query the latest project recommendations, fetch canonical papers with detailed summaries (markdown), track project feeds, and provide feedback. It allows deep analysis and export of research insights, making it essential for researchers and students needing structured academic information.

Expert technique for narrowing down Asian drama candidates by leveraging streaming platform and broadcast channel information. Use this when the query mentions services like Netflix, Viki, or Amazon Prime, combined with genre and year details. This method significantly increases search precision, especially when combining platform data with temporal or thematic keywords.

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.

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.