Skill UI

Process videos, audio, images, and documents on Transloadit’s cloud using 86+ robots to encode formats, generate thumbnails, resize/watermark, run OCR, speech-to-text, moderation, and chain multi-step pipelines for scalable media automation.

This skill functions as an expert GPU optimization engineer, transforming CPU-bound Python code into highly accelerated GPU kernels. It provides expertise across scientific computing, deep learning, and data analytics, covering core libraries like NumPy, pandas, and scikit-learn. Use cases include massive data parallelism, geospatial analysis, physics simulations, image processing, and advanced vector search on NVIDIA hardware.

PathML is a robust computational platform engineered for local, research-only workflows in computational pathology. It facilitates comprehensive analysis pipelines, including whole slide image (WSI) loading and tiling, preprocessing, quality control (QC), quantification of multiplex images, and the construction of spatial graphs. Designed with strict adherence to data privacy and research reproducibility, it supports advanced biological data analysis.

pydicom is a comprehensive Python library designed for reading, inspecting, writing, and transforming local DICOM datasets and associated pixel data. It supports complex metadata handling, various transfer syntaxes, and provides tools for bounded de-identification and preflight checks. It is essential for medical image processing pipelines that require robust handling of Protected Health Information (PHI).

A comprehensive guide for debugging complex Claude integration issues beyond basic error handling. It covers advanced patterns such as managing inconsistent outputs (temperature control), validating tool use calls, recovering from streaming connection drops, handling max_tokens truncation, and correctly processing image/vision inputs. Essential for building robust, production-grade AI applications with Anthropic's API.

This bridge provides bidirectional integration between Home Assistant (HA) and advanced AI pipelines. It streams HA camera feeds into the AI system for sophisticated analysis (detection, VLM, REID). Conversely, the analysis results—such as detected objects, descriptions, and person identities—are sent back to HA as image_processing entities, enabling powerful automation and smart notifications.

This guide outlines production-ready patterns for integrating SerpApi into robust search services. It covers essential best practices, including defining typed result interfaces for type safety, implementing LRU caching to conserve API credits, and building multi-engine abstractions to handle various search types (web, news, images). It also demonstrates asynchronous search processing for non-blocking operations.

LiteParse is a fast, open-source, and local document parser designed for layout-aware text extraction from PDFs, images, and office files. It outputs structured JSON with bounding boxes and metadata, making it ideal for building advanced RAG systems, citation grounding, and complex data pipelines without needing cloud APIs. Features include OCR, batch processing, and page screenshot capture.

This utility detects and redacts sensitive personally identifiable information (PII) from screenshots and images. It can mask various data types, including IDs, credit card numbers, API keys, phone numbers, and emails. Crucially, all processing is conducted locally and offline, ensuring that sensitive data never leaves the user's machine. It is essential for secure debugging, data sharing, and analysis workflows.

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.

This skill provides comprehensive guidance for authors seeking to submit manuscripts to the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). It helps evaluate the fit of research in signal processing, speech, audio, and image processing. It guides the framing, required empirical evidence (mathematical models, reproducible validation), and optimal positioning of the paper to maximize acceptance chances.

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.