Skill UI

A mandatory workflow for ensuring high code quality across multiple ecosystems, including Node.js/TypeScript and Python. This pipeline runs static analysis tools—such as linters, type checkers (MyPy, tsc), and security scanners (Bandit)—after every code change. It mandates that no task is considered 'done' or committed until all validation checks pass, resolving style, syntax, and type mismatches.

This comprehensive guide provides an end-to-end walkthrough for integrating with the Lokalise platform. It demonstrates core localization tasks, including listing projects, creating new multilingual projects, adding translation keys, and updating translations for multiple languages using both the Node SDK and the CLI. Ideal for developers establishing robust internationalization (i18n) pipelines.

Provides a comprehensive workflow for managing feature flags, running A/B/multivariate experiments, and analyzing user cohorts using PostHog. The guide covers implementing flag evaluation both client-side (browser) and server-side (Node.js), and demonstrates how to programmatically set up, track, and query experiment results via API calls (cURL/Bash).

High-level PyTorch Lightning streamlines training loops with automatic distributed support (DDP/FSDP/DeepSpeed), callbacks, checkpointing, logging, and device management so you can scale from a laptop to multi-node clusters without boilerplate.

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

This guide provides a comprehensive, multi-language walkthrough for installing and initializing the Sentry SDK in various environments (Node.js, Python, Browser). It covers essential concepts like DSN setup, using secure environment variables, configuring release versions, and implementing basic error and performance tracking for robust observability in production and CI/CD pipelines.

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.

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.

Explains building secure real-time communication with WebSocket servers, Socket.io rooms, SSE, Redis scaling, and resilient clients for multi-server Node.js apps.

This comprehensive guide covers all aspects of integrating the Google Gemini API into your applications using Python or Node.js. It details basic text generation, advanced multimodal inputs (text, image, audio), streaming responses, function calling/tool use, multi-turn chat implementation, and production best practices like model selection and robust error handling.

This skill automates the creation of isolated, pre-configured development containers (devcontainers). It scans the project structure to detect supported languages (Python, Node.js, Rust, Go) and intelligently generates the necessary configuration files, VS Code extensions, and setup scripts. It ensures all required tools are installed in a sandboxed environment, making it ideal for starting new multi-language projects or needing consistent, reproducible development workspaces.

Build, run, and visualize multi-step AI generation workflows via muapi.ai. Translate natural language intents into runnable node graphs, discover or edit existing pipelines, and execute them interactively or through the CLI with automated monitoring.