Skill UI

This comprehensive guide covers essential operations for handling PDF documents, including text, table, and metadata extraction using libraries like pdfplumber. It details document creation with reportlab, merging/splitting with pypdf/qpdf, and advanced OCR for scanned PDFs. Ideal for automating complex document workflows and structured data ingestion.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

This skill automatically processes MP4 videos by extracting key frames at configurable intervals. It then applies Tesseract OCR to recognize text on these frames, generating a comprehensive, structured Markdown report. The report includes full video metadata and timestamped, searchable transcripts, making it ideal for analyzing lectures, presentations, or screencasts programmatically.

A unified CLI tool that queries multiple web and academic search engines (Crossref, Brave, Tavily, DuckDuckGo) in parallel. It aggregates results, extracting structured metadata like DOI, publication year, and authors. Ideal for deep research, cross-checking claims, or comparing scholarly coverage against general web results.

This utility reads structured JSON outputs from multi-search-engine APIs (e.g., Google Scholar, arXiv) provided via standard input. It converts these normalized records into a clean, minimal BibTeX format, suitable for academic papers. It intelligently preserves crucial metadata like DOI, authors, and years while automatically handling data sanitization, deduplication of DOIs, and protecting complex fields like corporate names.

Provides expert guidance for connecting to Power BI Desktop's local tabular model using the Tabular Object Model (TOM) and ADOMD.NET via PowerShell. This skill covers model enumeration, executing DAX queries, metadata modification (tables, measures, relationships), query tracing, and model refreshing. It is specifically designed for managing the underlying data model structure when external tools like Tabular Editor CLI are unavailable.

This skill provides comprehensive knowledge of the Power BI Enhanced Report (PBIR) JSON schema. It details the structured metadata for report components, including visuals, pages, themes, bookmarks, and complex formatting rules. Users learn how to read and manipulate the underlying PBIR JSON to understand how Power BI reports are built and customized, adhering to strict technical guidelines and operational best practices.

An interactive workflow for auditing and standardizing naming conventions (tables, columns, measures) in Power BI semantic models (TMDL). It ensures names are human-readable, consistent, and aligned with core business terminology, addressing technical issues like abbreviations, mixed casing, and inappropriate technical prefixes.

A systematic skill designed to investigate data incidents, including freshness delays, volume anomalies, schema changes, and ETL failures. It guides the agent through a comprehensive process using Monte Carlo's observability metadata and optional direct data querying to pinpoint the root cause.

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

A comprehensive guide for authors submitting manuscripts to the American Sociological Review (ASR). It outlines best practices for creating self-contained, highly readable, and reproducible tables and figures (exhibits). Learn how to present complex empirical data, quantitative findings, and qualitative evidence while adhering to ASA style and meeting rigorous reviewer expectations.

A comprehensive guide for positioning Natural Language Processing (NLP) research papers for the Conference on Computational Natural Language Learning (CoNLL). It helps authors optimize their manuscripts by focusing on conference fit, structuring evidence, identifying necessary baselines, and adapting the narrative for a selective CS conference audience. Essential for submission strategy and manuscript re-framing.