readability-scorer
Calculate readability scores (Flesch-Kincaid, Gunning Fog, SMOG) and grade levels for text. Analyze writing complexity and get improvement suggestions.
Calculate readability scores (Flesch-Kincaid, Gunning Fog, SMOG) and grade levels for text. Analyze writing complexity and get improvement suggestions.
Clean up and modernize technical Markdown notes: fix formatting errors, typos, and overly colloquial phrasing; normalize style and spacing; carefully update clearly outdated tools or libraries while staying faithful to the original content. Use when the user asks to polish, correct, or update existing Markdown notes or documentation.
Extract text from PDFs for LLM consumption using AI-powered or traditional tools. Use when converting academic PDFs to markdown, extracting structured content (headers/tables/lists), batch processing research papers, preparing PDFs for RAG systems, or when mentions of "pdf extraction", "pdf to text", "pdf to markdown", "docling", "pymupdf", "pdfplumber" appear. Provides Docling (AI-powered, structure-preserving, 97.9% table accuracy) and traditional tools (PyMuPDF for speed, pdfplumber for quality). All processing is on-device with no API calls.
Transform CSV/Excel data into narrative reports with auto-generated insights, visualizations, and PDF export. Auto-detects patterns and creates plain-English summaries.
Analyze customer interview transcripts (SRT or plain text) to generate thematic breakdowns with summary, quotes, topics, timestamps, and full transcript. Use when given video transcripts or asked to create chapter markers.
Upload, edit, and export documents via Nudocs.ai. Use when creating shareable document links for collaborative editing, uploading markdown/docs to Nudocs for rich editing, or pulling back edited content. Triggers on "send to nudocs", "upload to nudocs", "edit in nudocs", "pull from nudocs", "get the nudocs link", "show my nudocs documents".
PDCA document writing standards. Defines document formats, naming conventions, version control, and status tracking. Triggers: document standard, naming convention, template, 문서 표준, ドキュメント標準, 文档标准
Fill PDF forms programmatically with data from JSON, CSV, or dictionaries. Support for text fields, checkboxes, and dropdowns. Batch filling available.
Transcribe audio files using Google's Gemini API or Vertex AI
Extract frames from videos at specific timestamps or intervals, find best frames, and generate thumbnail grids for previews.
Transcribe audio/video with AssemblyAI (local upload or URL), plus subtitles + paragraph/sentence exports.
Generate thumbnails from images with smart cropping, multiple sizes, and batch processing. Ideal for web galleries, social media, and app icons.
Remove backgrounds from images using segmentation. Support for color-based, edge detection, and AI-assisted removal methods. Batch processing available.
Source royalty-free images and videos from Pexels API for design, placeholders, or content. Supports search, curated/popular content, collections, multiple resolutions, and ALWAYS creates detailed sidecar metadata files.
Detail every TabularPredictor.fit argument, including presets, ensembling, resources, HPO, and deployment settings; depends on autogluon-tabularpredictor-class and feeds autogluon-tabularpredictor-fit-summary, predict-proba, calibrate-decision-threshold, set-model-best, save/load.
Consolidated AutoGluon Tabular tutorial summary (essentials + how-it-works + in-depth), explaining the workflow and every highlighted argument; depends on autogluon-tabularpredictor-class and autogluon-tabularpredictor-fit, plus autogluon-tabularpredictor-calibrate-decision-threshold/set-decision-threshold for threshold tuning.
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
Calibrate binary classification decision thresholds with TabularPredictor.calibrate_decision_threshold, detailing every argument and trade-off; depends on autogluon-tabularpredictor-fit and pairs with autogluon-tabularpredictor-set-decision-threshold and autogluon-tabularpredictor-predict-proba.