Skill UI

This skill guides the setup of distributed tracing and performance monitoring using Sentry. It covers configuring the SDK (e.g., setting tracesSampleRate) to capture latency, identify performance bottlenecks, and trace requests across the entire application stack. It demonstrates how to create manual spans for business logic and utilizes profiling for deeper performance insights, ensuring comprehensive application observability.

This guide provides enterprise-grade architectural patterns for integrating Sentry into multi-service, microservices environments. It covers centralized configuration modules, standardized project topologies (e.g., one project per service), distributed tracing setup, and robust error handling middleware for Node.js, Express, and Python/FastAPI. Ideal for organizations planning large-scale Sentry rollouts or managing complex service ecosystems.

A comprehensive guide covering observability patterns for service meshes like Istio and Linkerd. Learn how to implement the three pillars—Metrics, Traces, and Logs—to monitor service health, visualize dependencies, track golden signals (latency, traffic, errors, saturation), and debug microservice communication issues across complex distributed systems.

Automates setting up distributed tracing across a microservices landscape by guiding backend selection, instrumentation strategy, and configuration generation to enable end-to-end observability and aid performance troubleshooting.

SkyPilot orchestrates ML training, batch jobs, and serving across 20+ clouds with automatic cost-aware region selection, spot-instance recovery, managed clusters, and GPU-aware distributed training, letting you optimize spend and avoid vendor lock-in.

A comprehensive guide and expert resource for mastering durable workflow orchestration using the Temporal Python SDK. Covers advanced patterns including distributed transactions, saga implementation, signal handling, and query patterns. Essential for building highly reliable, scalable, long-running backend systems, and mastering async/await execution models for production deployment.

torchforge is Meta's advanced PyTorch-native library designed for agentic Reinforcement Learning (RL). It fundamentally separates core RL algorithms from complex distributed infrastructure concerns. This allows researchers to rapidly prototype and experiment with novel RL methods (like GRPO) using clean, PyTorch-native abstractions, while automatically handling large-scale tasks like distributed training across thousands of GPUs using Monarch and TorchTitan.

This advanced workflow orchestrates complex, multi-node GPU clusters on Vast.ai. It is designed for large-scale distributed machine learning training, automatically handling spot instance preemption and resuming jobs from checkpoints. Furthermore, it provides comprehensive cost analysis by tracking billing history, allowing users to optimize GPU spending and manage cluster teardown efficiently.

Guides training large language models with Volcano Engine's verl library, covering PPO/GRPO workflows, distributed backends, multi-turn rollouts, reward configs, critic-enabled PPO, and production deployments using Ray workers.

Handles semantic version updates across Claude Code plugin metadata, marketplace catalog, and git tags whenever a bump or release request appears, keeping all distributed files consistent.

Master the architecture of reliable distributed systems using advanced workflow orchestration patterns. This guide covers fundamental design decisions, including the distinction between Workflows and Activities, and introduces critical patterns like Saga for distributed transactions, Entity Workflows, and Fan-Out/Fan-In. Learn best practices for handling long-running processes, state persistence, and failure recovery to build robust business logic.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.