Skill UI

This skill provides comprehensive observability for CoreWeave's GPU-intensive workloads running on Kubernetes. It monitors critical operational metrics, including GPU utilization, memory usage, inference latency (p99), and job completion rates. Use it for setting up DCGM metrics dashboards, configuring proactive alerts for underutilization or Out-Of-Memory (OOM) conditions, and ensuring high reliability for AI/ML deployments.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

This SDK provides production-ready Python patterns for managing GPU workloads on CoreWeave. It includes helpers for defining specific GPU affinity, setting resource limits (A100, H100), and generating complete Kubernetes Deployment YAML. Use this library for building robust inference clients, managing GPU deployments programmatically, or automating complex AI workflows.

This expert acts as a dedicated data analysis partner for Product Managers. It helps users explore data, write complex queries (e.g., SQL, BigQuery), analyze key metrics, and create structured dashboards. Use this tool when you need to derive actionable insights from complex datasets, compare user cohorts, or understand product performance trends to inform critical business decisions.

A comprehensive tool for creating high-quality training datasets for custom object detection and segmentation models. It supports multiple advanced annotation methods including BBox drawing, SAM2 pixel-perfect segmentation, and DINOv3 visual grounding. Features include AI auto-detection, object tracking, and standardized COCO format export, streamlining the entire data pipeline from capture to training.

This skill acts as an expert Design Systems Lead, analyzing complex technical assets (HTML, CSS, screenshots) from existing projects. It synthesizes a comprehensive 'Semantic Design System' into a structured DESIGN.md file. This output serves as the authoritative 'source of truth' for AI prompting, ensuring that any new UI generated by tools like Stitch adheres perfectly to the established visual language, color palette, and component standards.

This utility performs comprehensive diagnostics to verify the correct setup and operational health of Promptbook. It systematically checks the configuration file existence, API key validity via external calls, and recent session activity logs. It is essential for troubleshooting or for users who need to ensure all components are functioning correctly after initial setup or updates.

A comprehensive guide to setting up CI/CD pipelines for ElevenLabs integrations. It implements a robust two-tier testing strategy: running mocked unit tests on every push to save API quota, and executing gated, real integration tests only on main branches or manually. Essential for automated validation of TTS projects using GitHub Actions.

This skill provides methods to optimize ElevenLabs usage costs for Text-to-Speech (TTS) workloads. It covers model selection (e.g., using Flash/Turbo for savings), implementing character-efficient text pre-processing to reduce character count, and setting up real-time quota monitoring with budget alerts. Ideal for developers managing high-volume, cost-sensitive voice applications.

This comprehensive guide details how to install and properly configure the ElevenLabs SDK for both Node.js and Python environments. It covers setting up the required API keys using environment variables, initializing the client connection, and verifying the setup by listing available voices. Use this skill when starting a new project that requires advanced text-to-speech (TTS) or voice synthesis capabilities.

This guide sets up a professional, cost-effective local development workflow for ElevenLabs TTS/voice projects. It includes SDK mocking to prevent character quota overruns during testing, hot-reloading capabilities, and quota-aware scripts. Ideal for developers building robust AI voice applications.