Skill UI

An expert skill for generating, auditing, and improving Best Practice Analyzer (BPA) rules for Power BI semantic models. It guides users through an interactive, multi-phase Q&A process (Double Diamond) to gather comprehensive requirements, analyze model structures, and deliver tailored, actionable rule sets for governance and compliance.

This tool traces the entire downstream dependency chain from a core semantic model to all connected reports and assets across multiple Fabric workspaces. Use it for crucial impact analysis before modifications, auditing model usage, or mapping cross-workspace data dependencies. It helps data stakeholders understand the full scope of influence a model has on the reporting layer.

This skill provides tools to audit, enforce, and standardize Power BI report themes. It addresses 'formatting debt' by centralizing bespoke visual overrides into the theme JSON, ensuring global consistency. Use it when reports suffer from formatting inconsistency, or when manual visual formatting has cluttered the visual JSON, allowing developers to maintain a clean, scalable report structure.

Provides a structured, actionable review of Power BI reports across their entire lifecycle—from development to active use. The tool assesses report effectiveness, usage adoption, data performance (load times), and structural integrity. It generates a prioritized list of findings and concrete recommendations, helping users determine if reports are worth maintaining, optimizing, or retiring.

A comprehensive guide for managing the entire lifecycle of tabular data models (Semantic Models) in Power BI/Analysis Services. It provides best practices and a tool cascade (CLI, TOM, TMDL) for designing, building, optimizing, refreshing, and auditing models. Use this skill when performing advanced model operations, optimizing performance, or ensuring data quality.

An interactive workflow designed to audit and standardize naming conventions within Power BI semantic models stored as TMDL files. It ensures that tables, columns, measures, and display folders adhere to consistent, human-readable, and business-aligned standards. The tool guides users through gathering critical business context before auditing and applying complex, cross-file reference updates to maintain data integrity.

The Atlas Contract is a goal-integrity governance protocol designed for complex, multi-stage tasks such as backend development, API integration, or data persistence. It prevents 'goal drift' by enforcing critical checkpoints (Goal Contracts, Phase Checks, Final Audits). It is essential for high-risk, multi-part requests where maintaining strict alignment with the user's original objective is paramount.

Atlas Ledger is a critical companion designed to provide lasting 'memory' to project contracts. It automatically processes detected deviations or 'drift' (e.g., from audits or reviews) and distills these instances into formal, actionable contract clauses (WHEN/DON'T/INSTEAD). These clauses are written to a persistent project memory file, Atlas.md. This mechanism ensures that lessons learned from failure are systematically captured, allowing future contract cycles to incorporate these rules, thereby strengthening overall project governance and reliability.

This skill maps engineering changes (code diffs, architecture designs, IaC) to specific financial compliance controls, such as PCI-DSS v4.0 and MAS TRM. It generates an auditable report detailing compliance gaps, required evidence, and concrete remediation steps, helping teams catch violations before formal audits.

This skill provides a deterministic, git-only method to verify claims made by AI agents (e.g., 'done', 'shipped', 'fixed'). Instead of trusting the agent's self-report, it audits the commit's diff and git history using the DOS kernel (`dos verify`, `dos commit-audit`) to confirm if the claimed change genuinely occurred and is backed by non-forgeable evidence. It acts as a critical quality gate.

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.

This guide helps developers audit and clean up Vite + React projects initialized using the Lovable scaffolding tool. It systematically removes all boilerplate artifacts, including placeholder assets, dedicated dependencies (like lovable-tagger), generated documentation, and unnecessary components (e.g., unused Radix deps). The goal is to deliver a clean, fully owned, and production-ready codebase.