Skill UI

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

Automates environment prep, service orchestration, and cleanup for executing integration test suites, covering database setup, migrations, and optional coverage reports.

Guides sales engineers through RFP/RFI analysis, competitive feature matrices, POC planning, demo prep, and proposal delivery using Python tooling and checkpoints to ensure coverage, differentiators, and win/loss insights.

This skill provides an automated solution for transforming diverse, unstructured technical data sources—including documentation websites, GitHub repositories, and PDFs—into structured, actionable 'Claude AI skills.' It significantly streamlines the process of knowledge base creation, data structuring, and preparing large volumes of technical information for advanced AI applications.

Automates sklearn pipeline builder tasks within ML training, activating on relevant triggers to guide data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

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.

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.

Master advanced techniques for scaling Supabase projects to handle high production load. This skill covers six critical layers: implementing read replicas for analytics, optimizing connectivity using connection pooling (Supavisor), upgrading compute resources, utilizing CDN caching for Storage, deploying Edge Functions regionally, and performing database table partitioning. Ideal for engineers preparing for traffic spikes or optimizing large-scale database architectures.

This comprehensive guide provides systematic methods to optimize Supabase query and database performance. Techniques cover PostgreSQL indexing (basic, composite, partial), using EXPLAIN ANALYZE for query plan analysis, optimizing client calls (column selection, pagination), and managing infrastructure resources like connection pooling. Use this when facing slow queries, high latency, or preparing a project for production-scale traffic.

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

This tool collects comprehensive diagnostic data for TwinMind issues. It gathers crucial information including environment details (OS, Node.js version, memory), API health status, network connectivity, and configuration snapshots. It is essential for generating detailed bug reports or preparing for technical support requests, helping developers quickly pinpoint the root cause of complex problems.