Document Claude Skills
2 253 document skills from 443 authors. They read and assemble documents, spreadsheets and slide decks. Half of them fit into 1 829 tokens or less — that is what one costs your context window when the agent loads it. 571 ship runnable scripts rather than instructions alone. 24 of them cannot work without an MCP server, most often rube. We also found 384 copies of these same skills sitting in other people's repositories — counted once here, not 384 times.
2 253 unique 443 authors 1 301 updated this month 183 from vendors
Extract calendar events from PDF by mapping colored rectangles to a 15-min grid using PyMuPDF.
How to fill California SC-100 Small Claims Court PDF form using fillable field IDs; includes complete field mapping, checkbox behavior, and field fill strategy.
Handle {{IF_X}}...{{END_IF_X}} conditional blocks in Word .docx templates — keep or remove content, with correct ordering relative to placeholder replacement.
Replace {{PLACEHOLDER}} tokens in Word .docx templates using python-docx, correctly handling split runs, nested tables, headers/footers, and verification.
Improved keyword-based document classification with false-positive prevention, manual review of zero-score files, and post-classification verification.
Extract text from PDF files using PyPDF2 with pdfplumber fallback; optimized for title/abstract extraction for classification.
Extract text from PPTX and DOCX files using python-pptx and python-docx, including tables and all shapes.
Precisely parse a PDF day-view calendar to extract event times, durations, and colors by reading visual block positions and the 15-minute grid.
Techniques for mapping case descriptions to PDF form fields.
Advanced techniques for filling PDF forms with fillable fields.
Techniques for preparing and formatting data from JSON/API sources for inclusion in documents.
Advanced techniques for Word document automation, including handling split runs, headers/footers, and complex conditionals.
Advanced document classification using weighted keyword scores and expanded subject vocabularies.
Robustly extracts text from various document formats with multiple fallback options.
Advanced PDF parsing for calendar extraction, including sidebar filtering and color-based classification.
Improved skill for filling XFA/non-standard PDFs using FreeText annotations when field mapping is unavailable.
A robust skill for template processing in DOCX files, handling placeholders and complex conditional blocks spanning multiple paragraphs.
Robust extraction of text from various file formats (PDF, DOCX, etc).
Skill for extracting text and layout.
A comprehensive skill for mapping, understanding, and filling complex interactive PDF forms using PyMuPDF (fitz).
Deep parsing and structural querying of nested JSON documents, resolving implicit relations across multi-modal corporate datasets like Slack messages and PRs.
Advanced Word document manipulation using raw XML regex replacements to perfectly preserve formatting across split text runs.
Robustly extracts text from PDF, DOCX, and PPTX files in Python using PyPDF2, python-docx, and python-pptx, suitable for document classification workflows.
An improved skill to programmatically parse PDF calendars, mapping drawing coordinates to precise time intervals using PyMuPDF.
Use this skill first to discover the actual field names in a PDF form. Extracts and prints all form field names from a PDF file to ensure accurate field mapping before filling the form.
Use this skill to fill the California Small Claims Court form (SC-100) with plaintiff and defendant information, case details, and amounts. Requires actual field names from the PDF — run Extract PDF Form Field Names skill first.
Use this skill to safely find and replace placeholder text in Word document paragraphs at the run level. This handles cases where placeholder text is split across multiple runs, which is common in Word documents and breaks simple string replacement.
Use this skill to read employee/candidate data from a JSON file and validate that all required fields exist before attempting document generation. This prevents placeholder errors caused by missing data.
Use this skill to handle conditional content blocks marked with {{IF_CONDITION}}...{{END_IF_CONDITION}} in Word documents. Keep or remove the entire block based on a condition, and clean up the marker text. Apply this during paragraph processing, not as a separate pass, to avoid structural issues with modified paragraphs.
Use this skill to write a modified python-docx Document object back to a .docx file, handling file paths and permissions correctly.
Use this skill when you need to find and process text in Word documents beyond just `doc.paragraphs`. Include tables, headers, and footers to ensure no content is missed.
Use this skill to analyze extracted text content from documents and classify them into the correct subject folder based on keyword matching and content analysis.
Use this skill to extract text and structure from Word documents (.docx files) to determine their subject for classification into the correct folder.
Use this skill to extract the full text, title, abstract, and keywords from PDF files to determine their actual subject matter. Essential for content-based sorting when PDF filenames may be arXiv IDs or other non-descriptive identifiers.
Use this skill to extract text, slide titles, and content from PowerPoint presentations to accurately determine their subject matter for classification.
Extracts calendar structure, time slots, existing appointments, and timezone from a PDF calendar document. Maps pixel positions to actual times using axis labels, identifies appointment blocks with their colors, and determines which dates are covered by the calendar.
Use this skill when you need to fill out the California Small Claims Court form SC-100. It maps the form's PDF field names to their meanings and expected values.
Use this skill when you need to programmatically fill PDF form fields using Python. Covers inspecting field names and writing values to fillable PDFs.
Core skill for retrieving information from enterprise data files (JSON, JSONL, CSV, Parquet, Markdown, etc.) located at /root/DATA, answering questions from /root/question.txt, and writing structured answers to /root/answer.json. Handles multi-hop reasoning, cross-referencing, and entity resolution across enterprise documents.
How to open a .docx Word document template with python-docx, replace placeholder tags like {{PLACEHOLDER}} in all document locations (body, headers, footers, tables including nested tables), handle conditional sections, and save the result.
Detailed guidance on recursively processing nested tables in python-docx documents. Use this when a Word template contains tables inside table cells and you need to reach all paragraphs.
How to extract text from PDF files for content-based classification. Use this when you need to read PDF content to determine what subject/topic a paper belongs to. Covers both text-based and scanned PDFs.
Use this skill to extract visual calendar data from a PDF file using PyMuPDF (fitz), including reading text for time labels and appointments, detecting colored rectangular blocks, and measuring their vertical positions to determine start/end times based on a 15-minute-per-row grid.
Use this skill to fill in the California SC-100 Small Claims Court PDF form with case data and save the filled PDF. Handles text fields, checkboxes, and radio buttons using pypdf. Run the inspection skill first to confirm field names, then use this skill to write the filled PDF to /root/sc100-filled.pdf.
Use this skill to inspect all form fields in a PDF file, printing their names, types, and current values/export values. Useful for understanding the structure of a PDF form before filling it.
Use this skill to classify and move 100+ PDF/PPTX/DOCX files into 5 subject folders (LLM, trapped_ion_and_qc, black_hole, DNA, music_history) based on content analysis. Handles keyword scoring with filename tiebreaking and music_history as catch-all default.
Fills a PDF form with provided data. It maps field keys to their respective pages and applies values. It handles multi-line text areas by assigning specific strings to the identified sequential keys. For checkboxes, it uses the precise export values found during inspection. All dates must be formatted as 'xxxx-xx-xx'.
Splits a long string of text (like a claim description or reason) into multiple parts that fit into sequential PDF field keys. Use this when the SC-100 form provides multiple lines (e.g., 'Reason_Line1', 'Reason_Line2') for a single explanation.