Skill UI

This bundle is designed to collect comprehensive diagnostics for CoreWeave clusters, including GPU node status, Kubernetes Pod logs, event history, and API connectivity. It aggregates critical cluster-level resource allocation and failure data into a single, actionable archive. Use it when preparing a support ticket to expedite the diagnosis of complex infrastructure issues like stuck pods, OOM errors, or resource bottlenecks.

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.

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.

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 helps users design robust and optimal software interfaces (APIs) for a module by applying the 'Design It Twice' philosophy. It generates multiple, radically different architectural designs using parallel agents, allowing for comprehensive comparison of trade-offs (e.g., simplicity vs. flexibility, specialized vs. general-purpose). Use this when you need to explore various structural options or compare module shapes before committing to a final design.

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 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 security tool designed for AI environments (OpenClaw). It functions as an 'antivirus' for AI skills, performing deep audits, scanning skills before installation, and running full security health checks. Its primary goal is to detect vulnerabilities, prevent data leaks, and mitigate privacy risks, ensuring the integrity and safety of the AI workspace.

This comprehensive guide provides a production-grade reference architecture for building robust Text-to-Speech (TTS) and voice applications using the ElevenLabs API. It covers detailed project structure, service layer design (including TTS orchestration, voice management, and caching), configuration handling, and best practices for streaming and multi-model orchestration, ensuring scalability and reliability in a commercial SaaS environment.

A comprehensive guide detailing security best practices for integrating with ElevenLabs. It covers secure API key management using environment variables, implementing Git pre-commit hooks, and demonstrating robust webhook signature verification using HMAC-SHA256. This ensures secure data handling, replay protection, and proper voice data governance.