Skill UI

Automates College Football Data operations through Composio's Rube MCP connectors, ensuring tools are discovered and executed with up-to-date schemas before running workflows.

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 guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.

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.

Provides advanced search strategies and domain knowledge for football queries that fall outside the major European leagues (e.g., Premier League, La Liga). This guide covers critical contexts in African leagues (CAF, PSL), South Asian leagues (I-League, ISL), Russian football, and tracing players' origins, ensuring comprehensive and non-biased research on global football history and facts.