crewai-multi-agent

This skill queries Railway service metrics including CPU, memory, network, and disk usage to monitor performance and debug issues. It's triggered when developers ask about resource utilization or service performance, and requires environment and service IDs from the Railway CLI. The skill provides actionable insights through Bash commands that fetch real-time analytics data.

This skill integrates Weights & Biases for comprehensive ML experiment tracking and MLOps. It automatically logs metrics, visualizes training in real-time, and manages hyperparameter sweeps and model versions. Use it to compare runs, optimize models, and collaborate within team workspaces directly from your development environment.

SGLang is a high-performance LLM serving framework that uses RadixAttention for automatic prefix caching, enabling significantly faster structured generation. It's ideal for developers needing JSON/regex outputs, constrained decoding, or building agentic workflows with tool calls. Use it when you require up to 5× faster inference than alternatives like vLLM in scenarios with shared prefixes.

This skill provides high-performance tokenization using HuggingFace's Rust-based library, processing 1GB of text in under 20 seconds. It supports BPE, WordPiece, and Unigram algorithms while enabling custom tokenizer training and alignment tracking. Use it when you need production-fast tokenization or to build custom tokenizers integrated with the transformers ecosystem.