Skill UI

This skill provides comprehensive methods to optimize Deepgram API usage and manage costs. It implements budget-aware transcription pipelines that preemptively check projected spending against set limits, utilizes audio preprocessing techniques (like silence removal via ffmpeg) to minimize billable minutes, and includes API calls to monitor current usage and spending against defined budgets. Ideal for cost-sensitive, production-grade deployments.

A comprehensive checklist and guide for deploying Deepgram integrations into a production environment. It covers critical areas including robust authentication practices, system resilience design (e.g., backoff strategies, circuit breakers), performance optimization (singleton clients, audio preprocessing), security measures (PII redaction), and mandatory monitoring setup (Prometheus metrics and alert rules). Essential for any go-live phase.

This skill builds and executes automated pipelines to clean, validate, and transform data for machine learning, covering ETL workflows, error handling, and quality reporting so Claude can deliver analysis-ready datasets.

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

Automates clustering workflows on supplied datasets with K-means, DBSCAN, or hierarchical algorithms, handling preprocessing, validation, metrics, and visualizations to expose groupings and anomalies.

This guide provides a comprehensive method for optimizing the latency and reliability of the Speak API integration. It covers techniques such as audio preprocessing, implementing response caching, and managing connection pooling. Use this skill when establishing a production-grade, low-latency language learning platform or managing high-volume API operations.

This guide provides production-ready patterns for integrating the Speak language learning API. It covers managing conversation sessions, performing pronunciation assessment, preprocessing audio files using ffmpeg, implementing robust retry mechanisms (with backoff), and tracking user learning progress. Use this when developing a full-featured, high-reliability language learning application or troubleshooting complex API integrations.

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.

FlowIO reads and writes Flow Cytometry Standard (FCS) files, exposing metadata, channels, and NumPy event arrays while allowing CSV/DataFrame conversion, making it ideal for cytometry pipelines and backend preprocessing services.

End-to-end Neuropixels recording analysis covering SpikeGLX/OpenEphys/NWB loading, preprocessing, motion correction, Kilosort4 sorting, quality metrics, Allen/IBL curation, and AI-assisted visualization for extracellular electrophysiology.

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology data.

Guides developers through scikit-learn workflows for classical supervised and unsupervised tasks, data preprocessing, evaluation schemes, hyperparameter tuning, and end-to-end pipelines when building interpretable production models.