Skill UI

This technique guides users to find obscure bands by utilizing unique, non-musical personal experiences of the band members (e.g., major surgery, relocation, unique side careers). Instead of searching directly for band names or discography details, the user should identify the most distinctive life event mentioned in the query. Searching for this unusual personal anchor point first is highly effective, as it leads to the member's identity, which can then be used to pinpoint the correct band.

This agent provides a critical framework for navigating corporate environments, contrasting internal jargon and process visibility (Ding In) with objective, real-world evidence and user experience (Ding Out). It helps users move beyond subjective 'feelings' or polished reports, structuring complex tasks into verifiable components: Goal, Acceptance Criteria, Action, Evidence, and Risk. Ideal for project management, status reporting, and critical thinking in a corporate setting.

This skill generates complete, structured, and bias-free job descriptions (JDs) for any role. It guides users through providing essential inputs, ensuring the output includes sections like role summary, responsibilities, requirements, and compensation. Crucially, it performs an inclusive language review, advising against biased requirements (like years of experience) and gendered language, thereby improving hiring fairness and attracting diverse talent.

This skill guides users through structured, adaptive learning experiences for any topic. It diagnoses the user's current knowledge level and goal to create a personalized study path. It utilizes techniques like worked examples, retrieval questions, and spaced repetition to ensure deep understanding, making it ideal for self-study, skill acquisition, or subject review.

A comprehensive guide for authors targeting the IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR). It details the strategic framing, required evidence (e.g., measured immersive user experience), proper positioning, and necessary structural adjustments to ensure a computer science manuscript is highly relevant and competitive for this specialized venue.

This guide provides a rigorous methodology for achieving scientific reproducibility in technical and user experience papers (e.g., UIST). It details three essential 'ledgers': the Build Ledger (pinning software, hardware, and environment dependencies), the Measurement Ledger (defining precise protocols for metrics like latency and accuracy), and the Study Ledger (documenting human evaluation tasks). Following these protocols ensures that external researchers can accurately rebuild the artifact and validate reported results.