Skill UI

This skill provides comprehensive capabilities for working with Microsoft Word documents (.docx). It supports full document lifecycle management, including text extraction (via pandoc), raw XML manipulation, creating new documents, and complex editing. Specialized workflows are available for redlining, handling tracked changes, managing comments, and preserving intricate formatting, making it ideal for professional, academic, and business document processing.

This skill helps discover new Japanese Natural Language Processing (NLP) resources, including GitHub repositories and Hugging Face models/datasets, that are not yet documented in the awesome-japanese-nlp-resources list. It analyzes a given topic, suggests candidates using web search, and outputs contribution-ready markdown for easy integration.

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

A comprehensive skill for capturing and documenting web application and graphical UI states during the development cycle. It supports full-page captures using Playwright, reliable post-processing cropping with PIL, capturing interactive states (hovers, selections), and even handles non-web desktop applications using mss/ctypes.

A comprehensive guide for training or fine-tuning advanced sentence-transformer models. This skill assists in selecting the correct architecture—including Bi-encoders (SentenceTransformer), CrossEncoders (for reranking), and SparseEncoders—for various Natural Language Processing (NLP) tasks. Use cases include semantic retrieval, similarity matching, document clustering, and pair-wise classification.

This comprehensive API provides a robust solution for automated document workflows. Users can perform format conversions (PDF, DOCX, XLSX, etc.), extract structured data (text, tables), perform OCR on scanned documents, redact sensitive information (PII), add watermarks, and apply digital signatures. Suitable for building automated, secure document processing pipelines.

This comprehensive tool automates the process of translating non-English official documents (images) for visa applications. It handles image preprocessing (conversion and rotation), performs robust OCR using multiple frameworks, translates the extracted text into professional English while maintaining document structure, and generates a certified bilingual PDF containing both the original image and the formatted translation.

A comprehensive guide for preparing scholarly manuscripts involving spatial data. It outlines best practices for documenting data provenance (sources, projections, processing chains), ensuring reproducibility, and adhering to critical ethical standards such as geoprivacy and human-subjects protection. Essential for maintaining research integrity in academic publishing.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

This guide provides comprehensive best practices for ensuring scientific reproducibility in technical papers, particularly for electronics and information processing fields. It covers the critical pillars of documentation: environment setup, data sourcing and preprocessing, full algorithmic disclosure (including pseudo-code and hyperparameters), and robust statistical evaluation. By adopting these standards, authors can transform their work from potentially unverifiable results into credible, verifiable scientific contributions suitable for top-tier journals.

This skill converts Portable Document Format (.pdf) files into Markdown format, making the content easily analyzable, summarized, searchable, or extracted. It is designed to handle various user requests involving PDFs, such as summarizing reports, extracting data, or processing folders of documents. It can also be invoked alongside sibling skills for batch processing mixed file types (PDF, DOCX, XLSX).

This skill converts Microsoft Word documents (.docx) into Markdown format. It is essential for accurately analyzing, summarizing, or extracting data from proprietary Word files, as direct parsing is unreliable. It supports single files, batch processing of folders, and is designed to coordinate with sibling skills (PDF and Excel) when handling mixed document types to ensure no data is skipped.