extractor
Extract content from any document using the Preset-First Agentic Pipeline. Auto-detects format and document type (scientific papers, requirements specs, etc.). Supports PDF, DOCX, HTML, XML, PPTX, XLSX, EPUB, M...
Skills correspondant au nom, à la description ou à l’auteur GitHub.
Extract content from any document using the Preset-First Agentic Pipeline. Auto-detects format and document type (scientific papers, requirements specs, etc.). Supports PDF, DOCX, HTML, XML, PPTX, XLSX, EPUB, M...
Document processing using document-context library. Use when working with PDFs or image rendering. Triggers: document-context, PDF, page rendering, RenderOptions, page range expressions, documents domain, image...
Prepares PDF books for LLM analysis. Creates analysis-ready chunks through TOC-based splitting and markdown conversion while preventing Context Overflow. Use when you need PDF book analysis, chapter-based split...
Extract text, tables, metadata from PDFs with merge and annotation support
Extract text, tables, metadata from PDFs with merge and annotation support
Extracts wine awards from PDF documents. Use when importing competition results, processing wine ratings, or when user mentions "extract awards", "parse awards PDF", "import competition results", or "process wi...
Phase 7 of MIP pipeline - Generate final PDF document from the essay and appendix using pandoc.
Convert documents and files to Markdown using markitdown. Use when converting PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls), HTML, CSV, JSON, XML, images (with EXIF/OCR), audio (with transcription)...
Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).
Completes Java OOP university assignments from PDF specifications with minimal implementation. Use when user provides a PDF assignment file or mentions completing a Java/FOP/OOP homework exercise. Focuses on wr...
Orchestrates PDF processing tasks including text extraction, form filling, merging, splitting, and creation. Detects PDF type and recommends appropriate tools. Use for any PDF manipulation task.
將文字或 Markdown 報告轉換為 PDF、Word (DOCX) 或 Markdown 檔案。
Intelligent multi-pass PDF analyzer for extracting document structure, content inventory, and metadata. Uses overview-first approach - scans for TOC, infers structure from semantics when TOC missing. Handles la...
Generate PDF and Word documents programmatically. Use for invoices, reports, contracts, or data exports.
PDF 遊戲規則轉換與翻譯流程。Use when processing PDF files, extracting content, splitting chapters, or translating game documentation.
This skill should be used when the user asks to "split PDF by chapters", "divide book into chapters", "extract chapters from PDF", or "break PDF into sections". Splits PDF documents based on table of contents o...
Extract content from documents (PDF, Word, Excel, PowerPoint) and answer questions with source citations
Run the complete PDF processing pipeline to convert the manual PDF into Next.js application data. This command executes all steps automatically: split, render, extract, translate, build, and manifest.
Translate and adapt resumes for different formats, languages, and audiences. Use when converting resumes to PDF/HTML, translating to other languages, or tailoring for specific roles.
Use when generating PDFs from markdown with Pandoc - covers differences from Python-Markdown, blank line rules, fix scripts for labels/anchors/metadata, and visual testing workflow
Process large documents (200+ pages) with structure preservation, intelligent parsing, and memory-efficient handling. Use when working with complex formatted documents, multi-level hierarchies, or when you need...
Interactive review sessions with academic PDFs (lectures, research papers, book chapters). Extract concepts, run Q&A sessions, generate quizzes with scoring. Preserves mathematical formulas in LaTeX format. Pri...
PDF export implementation patterns using html2pdf.js with iframe isolation
Extracting text and tables, merging/splitting documents. Use when needing to convert PDFs while preserving some structure.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.