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.

A comprehensive automation toolkit designed to manage the entire Vercel deployment lifecycle and associated infrastructure. Users can programmatically monitor, trigger, and manage deployments, configure custom domains and DNS records, manage environment variables across multiple targets, and oversee project settings and project listings. Essential for implementing robust CI/CD pipelines and managing cloud infrastructure.

LaminDB is an open-source lineage-native lakehouse designed for biological datasets and models. It enables queryable, traceable, and validated data management across various storage formats like S3 and Postgres. Users can track computational workflows, curate data with ontologies via Bionty, and ensure FAIR compliance. Ideal for managing scRNA-seq, spatial transcriptomics, and integrating ML pipelines while maintaining reproducibility and collaboration.

Build clinical and healthcare deep learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train, and evaluate using a modular five-stage workflow.

PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining flexibility. It automates training workflows, supports multi-GPU/TPU orchestration, and implements best practices for neural network training. Features include LightningModules, Trainers, data pipelines, callbacks, logging (W&B, TensorBoard), and distributed training strategies like DDP and FSDP for scalable training.

Load Hugging Face Hub models, run pipeline inference, generate text, and fine-tune with the Trainer API across NLP, vision, audio, and multimodal tasks using Python and PyTorch.

Vaex is a high-performance Python library designed for processing and analyzing large tabular datasets that exceed available RAM. It supports out-of-core DataFrame operations, lazy evaluation, fast aggregations, and efficient visualization of big data. Ideal for working with massive CSV, HDF5, Arrow, or Parquet files, performing statistics on billions of rows, and building machine learning pipelines without memory constraints.

A comprehensive guide to setting up robust CI/CD pipelines for Algolia indexing. This includes running unit and integration tests against live indices, validating index settings against defined configurations, and implementing automated reindexing upon deployment (e.g., triggering on version tags). It ensures the stability and data consistency of your search service.

Leverages the Anima SDK to convert visual designs from Figma into production-ready code across multiple modern frameworks (React, Vue, HTML). It supports clean TypeScript integration and Tailwind CSS styling, making it ideal for developers building automated design-to-code pipelines or rapidly prototyping complex UI components directly from design mockups.

This guide provides a reference architecture for automating the design-to-code workflow. It outlines the complete pipeline—from Figma design input to structured React/Vue output—detailing components like Figma API integration, code generation, and crucial post-processing steps (token mapping, normalization). Ideal for establishing scalable design systems and managing large-scale design handoffs in a professional environment.

This skill demonstrates how to create an event-driven pipeline using Figma's Webhooks. It listens for design file changes and automatically triggers Anima to regenerate production-ready code components (e.g., React/TypeScript/Tailwind). Ideal for integrating continuous design updates into CI/CD workflows.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.