Skill UI

This guide details how to configure webhook notifications and utilize audit logs from CAST AI. It allows users to set up real-time alerts for critical cluster events like node scaling, cost threshold breaches, and security findings. By integrating with Slack, PagerDuty, and custom HTTP endpoints, users can achieve comprehensive monitoring and automated response for their infrastructure.

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

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 guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).

A comprehensive solution for monitoring CoreWeave cluster events and GPU workload status using webhooks. It tracks critical lifecycle events such as pod readiness, job completion, volume attachment, and node health. Essential for building automated scaling, alerting, and recovery pipelines for large-scale GPU-accelerated inference and training workloads.

This skill guides users through setting up robust, production-grade infrastructure on Fly.io. It covers creating and connecting to Fly Postgres clusters for reliable data storage, provisioning persistent NVMe Volumes for durable file storage, and configuring private internal networking (6PN) for secure service-to-service communication. It is essential for modern microservices requiring stable databases and persistent state.

This skill provides a comprehensive solution for deploying containerized applications to Oracle Cloud Infrastructure (OCI). It supports two primary deployment paths: OKE (Oracle Kubernetes Engine) for full Kubernetes orchestration, and Container Instances for a simpler, serverless alternative. Use this skill for automating the entire lifecycle, including pushing images to OCIR, provisioning OKE clusters via Python SDK, and executing Kubernetes deployments using kubectl.

This internal agent skill automates the complex process of determining the most likely owner or team for a bug cluster. It follows a strict 6-level precedence model, starting with cached overrides, and sequentially querying sources like Service Owners, Oncall status, CODEOWNERS, recent assignees, and committers. The process includes staleness detection and confidence scoring, providing highly accurate and auditable routing recommendations for internal development triage.

A comprehensive guide for diagnosing and resolving slow search performance in Qdrant. This resource covers issues related to low QPS (throughput), high latency for single queries, and slow filtered searches. Solutions include tuning HNSW parameters, managing memory pressure, implementing quantization, optimizing indexing, and configuring cluster settings for maximum efficiency.

This internal agent skill defines a systematic, multi-step procedure for scanning GitHub repositories to gather comprehensive evidence supporting or explaining bug clusters. The process covers searching open issues, inspecting recent commits, analyzing code paths, and checking deployment history. It assigns evidence confidence tiers (Exact, Strong, Moderate, Weak) to provide a deep, actionable understanding of potential software defects.

A comprehensive guide to optimizing query performance in Snowflake. Learn advanced techniques such as implementing clustering keys, utilizing materialized views, leveraging result caching, and analyzing query profiles. This guide also covers best practices for warehouse tuning and optimizing common SQL anti-patterns to ensure efficient data querying and cost management.

This guide provides comprehensive instructions on managing performance and concurrency within Snowflake. It covers key concepts like warehouse sizing, multi-cluster configuration, and detecting query queuing issues. Users learn how to right-size resources and implement application-level rate limiting to prevent throttling and optimize data warehouse throughput for complex analytics workloads.