Results for “pdf”

Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

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Process PDFs, Office documents, HTML, and images through the Nutrient DWS API. Convert formats, extract text and tables, recognize text in scans with OCR, remove sensitive information, add watermarks or digital signatures, and fill PDF forms.

• Converting Office documents or HTML into PDF
• Making scanned documents searchable with OCR
• Extracting PDF tables into Excel files

Check generated PDFs with Python and PyPDF2 before delivery. Verify expected page counts, detect unreadable or zero-page files, and collect results across multiple documents, with optional file size checks for unusually small outputs.

• Checking PDFs against required page counts
• Detecting unreadable or zero-page PDFs
• Verifying several generated PDFs together

Extract text from PDFs when read_file fails or returns unreadable data. Use dedicated command-line tools or Python libraries to retrieve text and tables, preserve spacing where possible, inspect document properties, and validate the extracted content.

• Extract text when PDF reading fails
• Retrieve text while preserving layout spacing
• Extract tables as structured data

Troubleshoot PDF creation by trying pandoc, then reportlab, then fpdf2. Check tool availability, install missing Python libraries as needed, run generation with an explicit working directory, and verify that the output file exists.

• Converting Markdown or HTML to PDF
• Trying another tool after PDF generation fails
• Checking PDF tools and installing missing libraries

Download PDFs, check that the files contain valid content, and try several text extraction tools in sequence. The workflow helps detect downloaded error pages, insufficient text, and extraction failures while recording diagnostic details.

• Extract text from official PDF handbooks
• Check whether a downloaded PDF is actually an error page
• Try another extractor when PDF text extraction fails

Extract text from PDFs, create new PDF documents, and work with forms. Use it for press releases, branded one-page documents, and printable design deliverables. The full workflow requires installing the upstream skill bundle.

• Extracting text from an existing PDF
• Preparing a press release as a PDF
• Creating a branded one-page document

Convert PDF text and tables into Markdown for later summarizing, searching, or data extraction. Embedded images are saved separately and grouped by page in an appendix. Process individual files or entire folders, including subfolders.

• Prepare PDF reports for summarizing
• Convert a folder of PDFs in one batch
• Extract embedded images grouped by page

Download PDFs or process local files, extract their text, and turn processed data into structured PDF reports with ReportLab. Includes alternative extraction methods and text output when PDF generation fails.

• Extracting text from technical manuals
• Processing PDFs downloaded from websites
• Creating PDF reports with sections and tables

Retrieve PDFs from web sources and extract their text through an ordered sequence: direct reading, pdftotext, then PyMuPDF. Includes file validation, extraction checks, and guidance for documenting inaccessible sources and distinguishing supplementary content.

• Retrieving and reading PDFs from government websites
• Extracting text from technical manuals
• Handling PDF extraction failures in automated workflows

Check generated PDFs with PyPDF2 before reporting completion. A Python script verifies that each file can be opened and has the expected page count, helping identify outputs that need regeneration.

• Checking a generated one-page summary
• Verifying page counts across multiple PDFs
• Checking PDFs before reporting completion

Check PDF page counts, document details, and required text using command-line tools. This skill provides a verification workflow for environments without Python PDF libraries, including text extraction from selected pages and searches for expected sections.

• Checking whether a PDF has the expected number of pages
• Searching for required sections or terms in a PDF
• Extracting text from a selected page range

Extract text from difficult PDFs by trying several libraries and command-line tools in sequence, then using OCR, which reads text from images. Record failed attempts and check that extracted content meets the task requirements.

• Extracting text from scanned PDFs
• Retrying extraction when PDF text is garbled
• Reading PDFs with tables or complex layouts

Extract text, tables, and images from PDFs, create documents, combine or split files, and fill forms. It also covers OCR, which reads text from scanned pages, plus watermarks, page rotation, and password protection.

• Extract PDF text and tables for reuse
• Merge documents or split selected pages
• Create PDFs and fill PDF forms

Recover readable text from PDFs when read_file or Python extraction fails. Use pdftotext, a command-line text extractor, with options to preserve layout, specify character encoding, or save extracted content to a file.

• Recovering text after PDF reading fails
• Extracting text while preserving page layout
• Saving text from large PDFs to a file

Extract text and tables from PDFs, create new documents, and reorganize pages. Use it to merge or split files, fill forms, add watermarks or password protection, and turn pages into images for visual inspection.

• Extracting PDF tables into an Excel workbook
• Merging PDFs and splitting or rotating pages
• Filling application forms and checkboxes

Download PDFs from web URLs or process local files, verify their format, and extract text with pdftotext. If extraction fails or looks poor, try PyMuPDF, check the results, and document any remaining source limitations.

• Download a PDF from a web URL and extract its text
• Convert a local PDF into a text file
• Try another method after failed or garbled extraction

Read, create, and review PDFs with checks of both content and appearance. Uses Python tools for document generation and text extraction, then inspects rendered page images for spacing, alignment, and readability.

• Create a PDF with consistent formatting
• Extract text from an existing PDF
• Check tables and text for layout problems

Generate PDF documents from JSON definitions with headings, tables, images, and page numbers. Use file, in-memory, or streaming output, and extend document layouts with custom components and externally managed application state.

• Generate invoice PDFs from JSON definitions
• Build documents containing tables and images
• Stream generated PDFs through an HTTP response

Extract PDF text using a sequence of tools when one method fails. Validate downloaded files, check whether the extracted text is substantial, record the successful method, and investigate alternative sources if extraction remains unsuccessful.

• Extracting text from regulatory documents and handbooks
• Checking whether a PDF download returned an error page
• Trying another tool after empty or garbled text extraction

Extract text from PDFs by trying several methods in sequence and checking the results. When ordinary extraction yields too little text, switch to OCR, which recognizes text in images, to process scanned documents.

• Extracting text from PDFs of unknown types
• Reading text from scanned documents
• Batch processing PDFs in different formats

Extract readable text from PDFs when normal file reading returns garbled or unusable content. The workflow switches between extraction tools, checks the results, and includes optical character recognition for scanned pages when needed.

• Recovering readable text from garbled PDF output
• Preparing PDF text for data processing
• Extracting text from scanned PDFs

Generate DOCX, PDF, and HTML from Markdown through separate, observable conversion steps. The workflow replaces problematic characters for PDF output, captures error messages, and switches PDF engines when needed to troubleshoot document generation failures.

• Export one Markdown document to DOCX, PDF, and HTML
• Investigate unclear document conversion errors
• Replace special characters before PDF conversion

Process PDF forms, extract tables and text, and recognize text in scanned documents with OCR. Included scripts support data validation, batch workflows, merging, and page splitting, with table exports to CSV or Excel.

• Fill PDF forms after validating submission data
• Export tables from reports to CSV or Excel
• Extract text from scanned PDFs with OCR