Skill UI

This skill outlines how to implement and manage Role-Based Access Control (RBAC) and namespace isolation for multi-team GPU workloads running on CoreWeave's Kubernetes platform. It provides guidance on setting strict resource quotas, preventing noisy-neighbor issues, and defining granular roles (e.g., ML Engineer, Inference Operator). This is essential for maintaining resource isolation, ensuring compliance (SOC 2, HIPAA), and managing access across large-scale cloud deployments.

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 reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

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.

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 comprehensive CI/CD pipeline setup for Fly.io edge deployments. It automates unit testing on pull requests, deploys to staging environments, and promotes changes to production upon merging to the main branch. It handles Docker builds, uses flyctl for deployment, and includes health checks to ensure service reliability and seamless automated delivery.

This comprehensive bundle collects all necessary diagnostic evidence for Fly.io applications, including machine status, application logs, volume metadata, and network diagnostics. It is crucial for generating detailed support tickets and troubleshooting complex infrastructure issues such as stuck deployments, boot failures, or edge networking problems.

This utility provides comprehensive solutions for managing rate limits when interacting with the Fly.io Machines API. It implements advanced patterns like Token Bucket rate limiting, exponential backoff, and request batching. This ensures that automation scripts remain robust and reliable, even during high-churn operations such as large-scale deployments or auto-scaling events, preventing common 429 throttle errors.

This guide provides methods for robust monitoring and automation tailored for services deployed on Fly.io. It covers key areas including polling the Machines API to detect state changes, implementing comprehensive health check endpoints, processing structured logs using tools like `jq`, and automating deployment notifications within CI/CD pipelines. Essential for achieving high availability and reliable operations.

A comprehensive guide for configuring and managing Role-Based Access Control (RBAC) in Grammarly Enterprise deployments. This covers role hierarchies (Org Admin, Team Admin, Member, Guest), implementing secure API access, auditing user permissions, and maintaining compliance standards like HIPAA and SOC 2.

A comprehensive guide for setting up Intercom across development, staging, and production environments. This method ensures proper environment isolation, manages environment-specific access tokens and webhooks, and implements safety guards to prevent accidental data modification in critical workspaces. Essential for secure CI/CD deployments of SaaS applications.

This skill provides advanced techniques for robust prompt engineering within LangChain applications. It solves common production issues, such as f-string template breakage when user input contains literal curly braces, and guides developers on consolidating scattered prompts. Crucially, it details how to use the LangSmith Prompt Hub to version, pin, and A/B test prompts using immutable commit hashes, ensuring stable, reproducible LLM deployments.