Skill UI

Guides teams through intentional brainstorming before implementation, surfacing intent, constraints, and design options, then formalizing a spec so every creative change starts with validated direction.

Interactive workflow that uses the joyride_request_human_input tool to probe scope, deliverables, and constraints before executing any task, ensuring thorough clarification and structured planning.

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 advanced tool acts as a specialized prompt engineer, guiding users to create highly optimized, production-ready prompts for various AI models (LLMs, Midjourney, etc.). It enforces strict writing rules, guides detailed intent extraction (Task, Context, Constraints), and provides model-specific best practices (e.g., Claude vs. GPT-5.x) to maximize AI output quality and maintain consistency. Essential for professional content creators, developers, and researchers needing precise AI interaction.

This guide details a systematic methodology for identifying specific music tracks. Since searching solely by song title is often too generic, users must first establish constraints on the artist (e.g., nationality, birth year, genre, career type) to narrow the search space. Once the artist is identified, the search should focus on their discography, album tracklists, or specific release years to pinpoint the desired song.

This technique is designed for the final verification stage when analyzing a candidate TV series. Instead of accepting initial matches, users must perform targeted searches combining the show name and specific character details (e.g., birth year, specific plot event, relationship). This ensures that the candidate show satisfies all complex constraints, eliminating false positives and confirming the accuracy of the plot details.

A meta-skill designed to generate comprehensive and detailed travel itineraries and schedules. It intelligently orchestrates multiple steps, including extracting user preferences, checking local weather, searching Points of Interest (POI), and identifying practical time constraints (like opening hours and transit times). This ensures the final output is highly actionable, whether for a leisure trip or a business journey.