Skill UI

This skill provides comprehensive, structured audit logging for OpenRouter API interactions. It automatically captures essential metadata like total cost, token usage, model details, latency, and status, while also performing PII redaction. The resulting audit trail is stored in an append-only SQLite database, making it ideal for building compliance-ready systems, detailed cost monitoring, and deep debugging of production environments.

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

Collection of battle-tested PyTorch training recipes covering architecture selection, optimizers, LR scheduling, precision tips, and debugging to train or fine-tune LLMs, vision, diffusion, and biomedical models with high GPU throughput.

Utilize Modal, a serverless GPU cloud, for running complex machine learning workloads, including training, fine-tuning, and inference. This tool provides a zero-config environment, eliminating the need for SSH setup or Docker management. It features automatic scale-to-zero billing and local-first execution, making high-powered GPU compute accessible for debugging and small-scale experiments.

Astropy is a core Python library for astronomy and astrophysics workflows. It provides essential functionality for astronomical research and data analysis, including coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing. Use it for implementing or debugging astronomical data analysis code.

This guide provides a quick reference for diagnosing and fixing the top common errors encountered in ClickHouse, including syntax errors, memory limit issues, write permission failures, and data type mismatches. It offers specific error codes, root causes, and actionable SQL solutions for smooth database operation and complex analytics.

This guide provides comprehensive solutions for diagnosing and resolving common errors and exceptions encountered while using the Hex data platform. It covers critical areas such as API authentication failures (401, 403), resource not found issues (404), rate limiting, and various run status errors (ERRORED, KILLED). This is essential for debugging failed data requests, optimizing integration workflows, and ensuring stable data analytics pipelines.

A comprehensive guide for deep debugging complex Snowflake issues. This guide covers advanced techniques including detailed query profiling (spill analysis), diagnosing lock contention and transaction conflicts, analyzing metadata operations, and optimizing partition usage. Use it when standard troubleshooting fails to address slow queries or warehouse performance bottlenecks.

This advanced helper assists users in writing, debugging, and validating complex formulas, pivot tables, and array functions for platforms like Excel and Google Sheets. It ensures formulas are robust by handling common edge cases (e.g., blank rows, mixed data types, time zone quirks) and provides detailed, worked examples. It also manages cross-platform dialect translation.

A comprehensive guide to authoring, optimizing, and validating Power Query M expressions used within semantic model partitions. This resource covers crucial topics such as maintaining query folding for performance, applying early filtering and column pruning, and leveraging the Power Query API for robust data validation and debugging.

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.