Skill UI

This comprehensive tool manages the complete lifecycle of ML experiments, providing robust deployment capabilities across diverse environments. It supports running training jobs on local GPUs, dedicated remote servers, Vast.ai, and serverless platforms like Modal. The workflow handles environment detection, code synchronization (rsync/git), dependency pinning, and integrates advanced logging tools (e.g., W&B), ensuring reliable, scalable, and reproducible ML training runs.

Guides on using SwanLab for open-source ML experiment tracking, offering local or self-hosted dashboards, rich media logging, and integrations with PyTorch, Transformers, Lightning, or Fastai for comparing runs and monitoring metrics.

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.

This skill provides comprehensive guardrails for handling Protected Health Information (PHI) in US healthcare software. It guides development teams on data classification, security best practices, access control, and audit logging to ensure strict adherence to HIPAA regulations. Use it when dealing with patient data storage, transmission, or processing to prevent breaches and maintain compliance.

Guide for implementing Firebase Analytics in Flutter apps, covering setup, event logging, user properties, default parameters, and screen tracking best practices.

This skill provides comprehensive guidelines for writing idiomatic, robust, and production-ready error handling in Go. It covers best practices for error creation, proper wrapping using `%w`, inspection techniques with `errors.Is` and `errors.As`, adhering to the single handling rule, and utilizing structured logging with `slog`. Use it when auditing, reviewing, or writing new Go code to ensure reliable error propagation and logging.

This skill provides comprehensive best practices for monitoring Go services in production. It covers the five critical pillars of observability: structured logging (slog), Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling, and Real User Monitoring (RUM). Use this skill to proactively instrument new features, set up advanced alerting, correlate logs with traces, and ensure full visibility into system health and user experience.

A comprehensive toolkit for building robust, structured logging pipelines in Golang. It provides advanced modules for handler composition (slog-multi), throughput control (slog-sampling), attribute transformation (slog-formatter), and integrates with major backend logging sinks (e.g., Sentry, Loki, Datadog, Elasticsearch). Use this when building complex Go applications that require deterministic log routing, sampling, and data sanitization.

Customer Support Agent persona centralizes ticket tracking, email-to-task conversion, status logging, urgency escalation, and follow-up scheduling using Gmail, Sheets, Chat, and Calendar utilities.

Creates a Google Sheets spreadsheet prepopulated with headers, a sample expense entry, and sharing permissions so teams can immediately start logging and reviewing travel costs.

This skill guides the generation of a complete, production-ready Model Context Protocol (MCP) server using C# and .NET. It covers advanced architectural patterns, including Host builder setup, proper dependency injection, structured logging, and implementing robust functional tools. Ideal for developers building sophisticated AI agents or backend services that require standardized communication and tool invocation capabilities.

This comprehensive guide outlines best practices for writing high-quality, scalable, and maintainable code in .NET/C#. It covers core architectural principles, including SOLID, dependency injection, and design patterns (e.g., Command Handler). Furthermore, it addresses modern challenges such as structured logging, async/await patterns, secure coding, robust testing standards, and advanced AI integration using Semantic Kernel.