Skill UI

This guide details setting up and managing CoreWeave GPU cloud infrastructure across Development, Staging, and Production environments. It ensures strict resource separation using dedicated Kubernetes namespaces for GPU quotas and scaling policies. Use this when deploying complex ML/AI workloads requiring robust environment isolation and controlled promotion workflows.

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 skill provides methods for managing GPU quota limits and rate restrictions for CoreWeave cloud services. It includes using `kubectl` to check resource quotas and demonstrates implementing robust request queuing mechanisms using Python's `asyncio` library, ensuring stable and efficient resource allocation for high-concurrency inference endpoints.

Provides comprehensive guidelines for securing CoreWeave cloud deployments on Kubernetes. Covers critical security areas including credential management, implementing Role-Based Access Control (RBAC), enforcing network isolation policies, and handling sensitive secrets for ML model access. Essential for hardening GPU workloads and protecting data in cloud environments.

This skill provides expert guidance on building and maintaining applications using Amazon Aurora DSQL, a serverless, PostgreSQL-compatible distributed SQL database. It covers critical areas including schema management, multi-tenant architecture, IAM authentication, and safe data migrations (e.g., MySQL to DSQL). Users can execute direct queries, analyze performance bottlenecks using query plan explainability, and adhere to industry best practices for robust, scalable database design.

This skill provides comprehensive guidance for building and managing applications using Amazon Aurora DSQL, a serverless, distributed, PostgreSQL-compatible SQL database. Topics cover schema management, multi-tenant isolation, IAM authentication, complex DDL migrations (column, constraint), MySQL data type mapping, and advanced query performance tuning using plan explainability tools, ensuring robust and scalable data architecture.

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.

This skill acts as a rigorous, Socratic interviewer designed to stress-test a proposed plan against an established domain model and existing project documentation. It forces the user to sharpen terminology, resolve cross-cutting dependencies, and identify inconsistencies between stated requirements, code implementations, and documented decisions. It ensures the resulting architecture is precise, consistent, and well-documented by automatically updating the context and generating Architecture Decision Records (ADRs).

This skill provides a comprehensive, multi-phase guide for authors to draft a Specification Enhancement Proposal (SEP) adhering to rigorous governance standards. It walks users through prerequisites, initial vetting (Gate), detailed interviews (e.g., breaking changes, security), and technical research, ensuring the proposed change is viable and community-vetted before formal submission.

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.