Skill UI

Details the PBIP (Power BI Project) format, which converts the opaque PBIX binary into a clear, structured, and editable set of text files. This guide covers project structure, cross-cutting operations (like renaming and forking), and the technical differences between Thick and Thin reports, enabling better Git integration and multi-author collaboration.

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.

Provides expert guidance for authoring and editing Tabular Model Definition Language (TMDL) files directly within PBIP projects. This skill is useful for making quick text-level fixes (like descriptions, format strings, or adding measures) when specialized tools (like Tabular Editor CLI or Power BI Desktop) are unavailable, especially for Git workflow or manual adjustments.

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.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

This Main Agent acts as a central orchestrator for a specialized squad of AI agents, managing complex software development lifecycles. It guides projects through defined phases—from requirements gathering and planning to architecture, implementation, testing, and deployment. It maintains strict context discipline by summarizing agent outputs and referencing full artifacts, ensuring efficient, structured, and non-overwhelming communication to the user.

This skill acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.

This skill manages and maintains your personal context portfolio, a structured set of markdown files detailing your identity, professional roles, team information, and system configurations. It guides users through first-time setup interviews, detects missing or incomplete information (e.g., TODO placeholders), and facilitates targeted updates when your professional context changes.

This specialized skill guides the creation of long-form structural critical essays and detailed academic dossiers, primarily for Chinese academic contexts. It utilizes the CrossFrame methodology to first establish a foundational structure, define evidence boundaries, and identify key mechanisms, before applying a critical matrix. The output is highly structured, ensuring depth, rigorous source checking, and comprehensive critical analysis suitable for complex intellectual work.

This skill facilitates structured, bidirectional reading and knowledge synthesis for academic texts, theories, and articles. It forces users to first capture the source material's original intent, before critically mapping its relationships, conflicts, and absorptions against the CrossFrame framework. It ensures structured output with clear source boundaries, making it ideal for deep learning and scholarly work.

This guide serves as a comprehensive reference for writing idiomatic, efficient, and modern Python code. It covers best practices for comprehensions, context managers, error handling (EAFP), data class usage, type hinting, and avoiding common anti-patterns, helping developers write cleaner, more robust, and truly 'Pythonic' code.

This skill generates a formal Architecture Decision Record (ADR) following industry-standard formats. It guides users through documenting the full rationale behind significant technical choices, ensuring that the context, alternatives considered, chosen option, and resulting trade-offs are all captured. Ideal for maintaining architectural integrity and knowledge transfer within engineering teams.