Skill UI

This guide provides comprehensive instructions for searching Tabular Editor (TE) documentation and understanding complex configuration files. It details the usage of the `pbi-search` CLI tool, allowing users to query multiple sources (DAX, Power BI, SQLBI) simultaneously. It also covers the structure and best practices for model-specific files like `.tmuo` and application preferences, ensuring efficient data modeling and development.

Provides a comprehensive CLI reference for Tabular Editor 2 (TE2). It enables automated model management, including deployment to XMLA endpoints, running embedded scripts, performing Best Practice Analysis (BPA), and handling model exports (TMDL). This skill is essential for integrating data model lifecycle management into CI/CD pipelines and automating repetitive tasks from the command line.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

Arbor executes an Autonomous Optimization (AO) loop, allowing users to iteratively improve complex artifacts—such as models, agents, or data pipelines—against a measurable objective. It implements Hypothesis Tree Refinement (HTR), structuring research into a persistent tree to track hypotheses, insights, and evidence. This process is crucial for long-horizon optimization tasks (e.g., Kaggle-style benchmarking) and utilizes a held-out test set to prevent overfitting and ensure cumulative, auditable progress.

Aria functions as a System Architect, responsible for establishing the definitive structural foundation of the entire system. She designs comprehensive blueprints covering the data model (schema, relationships, constraints), API contract specifications (endpoints, auth, pagination), modular file structure, and selection of core architectural design patterns. Her output ensures that no code is written without a rigorously defined, approved architectural plan, minimizing ambiguity and technical debt.

A protocol for token-efficient, accurate, and structured web research. It guides the model to fetch minimum data when accessing URLs, GitHub repositories, or performing web searches, preventing token waste.

This skill guides you through a comprehensive, multi-phase interview to capture every necessary detail of a new project idea. It is designed to run BEFORE any code is written, forcing detailed specification gathering across purpose, features, data models, and technical constraints. The output is a clean, self-contained prompt.md file, ensuring subsequent development is based on solid, unambiguous requirements and preventing costly mistakes.

This comprehensive guide details idiomatic best practices for writing high-performance, safe code in Rust. It covers advanced topics such as understanding Ownership and Borrowing rules, robust error handling using the '?' operator, optimizing functional programming with Iterators, implementing structured concurrency, and defining robust data models with Structs and Enums.

This comprehensive canvas guides teams through developing robust AI-powered features. It ensures product decisions are rigorous by covering problem definition, model selection (LLM, RAG, etc.), data governance, evaluation metrics, user experience design (including fallbacks), and responsible AI compliance. Ideal for assessing AI readiness and preventing common product failures.

This skill generates comprehensive, production-ready database schema documents. It covers Entity-Relationship (ER) diagrams, detailed SQL DDL definitions, constraint analysis, index strategies, and access pattern optimization. Use it when designing a new database, documenting an existing one, or modeling complex data relationships for engineering review.

A comprehensive framework for creating defensible, bottom-up sales forecasts. This model guides users through building detailed pipeline stage models, conducting scenario analysis (Upside, Base, Downside), and logging key assumptions. Ideal for sales and revenue leaders who need a systematic, data-driven approach to projecting future revenue, moving beyond simple guesswork.

A comprehensive template designed to structure complex technical specifications, ensuring a complete record of engineering decisions. It guides users to document problem statements, proposed solutions, system architecture, data models, API designs, security considerations, and rollout plans, bridging product requirements and technical implementation.