Skill UI

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.

Guides building, tuning, and validating production Apache Spark jobs, covering DataFrame/RDD choices, partitioning, broadcast joins, skew handling, caching, and cluster configuration for big data pipelines.

This guide provides advanced patterns and best practices for optimizing Apache Spark jobs. It covers critical areas like effective partitioning, memory tuning, minimizing data skew, and optimizing different types of joins (broadcast, bucket). Use this when troubleshooting slow, large-scale data processing workflows or needing to scale Spark pipelines efficiently and reliably.

A comprehensive guide for optimizing costs when integrating the Speak API. This skill covers usage monitoring, selecting optimal API tiers, and implementing efficient audio processing pipelines. It is essential for developers managing language learning platform operations to ensure cost efficiency while maintaining high performance.

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.

Execute systematic SQL injection vulnerability assessments on web applications using reconnaissance, payload tuning, and exploitation workflows to uncover database flaws, capture evidence, and validate remediation recommendations for authorized security testing.

A comprehensive guide for deep PostgreSQL diagnostics, designed for advanced troubleshooting when standard methods fail. Covers using pg_stat_statements for identifying slow queries, analyzing lock contention with pg_locks, detecting connection leaks, debugging RLS policy conflicts, and profiling Edge Function cold starts. Essential for performance regression investigation and escalating issues to support.

This guide provides comprehensive, actionable methods to audit and significantly reduce Supabase operational costs. It covers advanced database tuning (vacuuming, index cleanup, bloat management), efficient storage lifecycle management, connection pooling strategies, and optimal plan selection. Use this when analyzing high billing reports, identifying resource waste, or implementing robust cost-saving measures in a cloud environment.

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.

Automates the end-to-end training of machine learning models by analyzing datasets, selecting suitable algorithms, tuning parameters, performing cross-validated training, and emitting performance metrics plus a saved model artifact.

Guides Claude to automatically tune machine learning model hyperparameters via grid, random, or Bayesian search, generating runnable Python code, executing validations, and reporting best configurations plus performance metrics for accuracy or other goals.