Skill UI

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

This tool executes the foundational SPARC Specification Phase (Phase 1) for new features or projects. It guides the user through systematically gathering comprehensive requirements, including functional/non-functional needs, defining concrete acceptance criteria (Given/When/Then), identifying technical constraints, and mapping edge cases. It creates a robust, formalized specification document that serves as the blueprint for all subsequent design and development phases.

A comprehensive, human-guided onboarding skill designed for users new to AI coding agents and Power BI agentic development. It provides structured setup help, deep conceptual explanations, and conversational guidance, mimicking a mentor's session. Modes include concise setup assistance and deep conceptual learning. Use cases: Setting up a new agent environment, understanding core AI concepts (models, prompts, tools), or auditing existing agent configurations.

A comprehensive skill for integrating agentic operational flows into the RuView sensing ecosystem (RVF). It allows external agents to read, reason about, and respond to real-time cross-component sensing data, including BFLD events, vitals, and pose estimation. Use cases include automated caregiving responses, complex event negotiation, and maintaining auditable agent state within the sensing data stream.

This tool generates a complete, structured blueprint for developing powerful case studies and success stories. It guides users through defining the Challenge-Solution-Results (CSR) narrative, creating detailed client and internal interview questions, planning data visualizations, and mapping out multiple output formats (white paper, video, web page). This systematic approach ensures content is highly credible, optimized for sales enablement, and ready for multi-channel distribution.

This skill provides a comprehensive guide to submitting primary research in catalysis to ACS Catalysis. It helps authors assess whether their work meets the journal's high standards, which require an interlocking case of activity/selectivity data, advanced characterization, and robust mechanistic insight. Use it for venue selection, manuscript re-framing, and understanding the rigorous evidence bar expected in modern catalysis research (heterogeneous, homogeneous, electro-, etc.).

This system acts as an orchestrator for a three-tier model team (Advisor, Workers). It is designed for complex, large-scale tasks that exceed the capability of a single model pass, requiring decomposition, parallelization, and mandatory verification loops. Use cases include researching multiple competitors, executing multi-stage plans, or managing iterative development cycles involving different specialized AI agents.

This guide helps authors structure and write a highly persuasive cover letter for advanced materials submissions. Its purpose is to proactively convince the journal editor that the work represents a genuine, broad-impact scientific advance, rather than just incremental optimization. It focuses on arguing the impact case, naming specific beneficiary communities, and structuring the argument for maximum editorial visibility.

This skill provides a rigorous framework for defending the methodology of advanced educational research manuscripts, suitable for top-tier academic journals. It guides users through defining research structure across quantitative (e.g., multilevel, quasi-experimental), qualitative (e.g., ethnography, case study), and mixed methods paradigms. Focus areas include addressing rival explanations, ensuring measurement validity, establishing causal claims, and meeting stringent reporting standards (AERA).

A comprehensive guide for structuring and defending the methodological design of social science manuscripts, particularly for high-impact journals like AJS. It covers diverse traditions—including quantitative, comparative-historical, ethnographic, and network analysis—ensuring the design rigorously supports the theoretical claim, rather than just presenting data. Use this when justifying case selection, identifying assumptions, or linking field observations to major theoretical arguments.

This guide assists researchers in strengthening the methodological rigor of academic work, particularly in political science. It provides structured methods for defending research design, covering rigorous causal identification for quantitative work (e.g., DiD, IV, RDD) and systematic case selection/process tracing for qualitative analysis. Use this when facing reviewer skepticism or preparing a pre-analysis plan to ensure the evidence credibly connects to the theoretical argument.

This skill guides users in elevating empirical findings from diverse sociological methods (quantitative, ethnographic, historical, etc.) into a generalizable, portable theoretical argument. It provides a structured framework to define core mechanisms, concepts, scope conditions, and observable implications, ensuring the contribution meets the rigorous standards of top-tier academic journals by proving its transferability beyond a single case or subfield.