Skill UI

A comprehensive, multi-phase checklist for deploying and validating CAST AI cost optimization and autoscaling features on a production Kubernetes cluster. It guides users through prerequisites, initial monitoring setup (Phase 1), full autoscaling enablement (Phase 2), security hardening (RBAC, secrets), and critical validation commands for safe go-live deployment.

This skill performs comprehensive, systematic cleanup across 11 critical dimensions of code quality, including dead code removal, security vulnerability checks, weak type elimination, and performance optimization. It analyzes code, assigns confidence scores, and verifies all suggested changes using build and test gates. Ideal for projects suffering from accumulated technical debt, or before major release cycles to ensure robust, maintainable code.

Manages the complete data lifecycle within ClickHouse. Includes setting Time-To-Live (TTL) policies for automatic data expiration, implementing GDPR/CCPA compliant deletion, PII masking, partition management, and building comprehensive audit trails. Essential for robust data governance and compliance.

A comprehensive, step-by-step runbook designed for diagnosing and resolving critical operational issues in ClickHouse databases. Covers tiered severity levels (P1-P3) for scenarios ranging from server outages and OOM kills to performance degradation and stuck queries. Provides essential system tables queries and commands for service recovery and system health checks.

This comprehensive guide details how to set up end-to-end observability for ClickHouse. It covers extracting key metrics using built-in system tables (e.g., query logs, merge status), integrating the metrics with Prometheus, and finally visualizing performance, throughput, and health indicators on Grafana dashboards. Essential for monitoring production-grade ClickHouse deployments and configuring reliable alerting.

This skill provides comprehensive guidance for deploying and managing GPU-accelerated AI inference services on CoreWeave Kubernetes (CKS). It covers best practices including containerization using NVIDIA CUDA base images, configuring specific GPU resource limits (A100/H100), setting up robust health checks, and executing controlled rolling updates. Ideal for managing multi-model inference and scaling demanding AI workloads in a cloud environment.

This runbook provides structured steps for responding to critical production incidents on the CoreWeave platform. Use it when dealing with GPU workload failures, inference service outages, or general Kubernetes resource issues. It guides users through checking pod status, node health, and diagnosing common model loading errors to ensure rapid service recovery.

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 guide provides a comprehensive framework for investigating production issues on the Nexu platform using the Datadog Logs API. It details how to query crash events, analyze OpenClaw stderr output, check gateway startup status, and review API request logs (including 5xx errors). It includes best practices for authentication, filtering by pod/time, and parsing raw JSON logs using Python for actionable insights.

The skill generates professional diagrams (flowcharts, architecture, UML, ERD) using the native draw.io desktop CLI. It creates editable .drawio XML files and exports them to common formats like PNG, SVG, PDF, and JPG. Ideal for visualizing complex systems, data flows, or component relationships.

This guide outlines the rigorous structure and technical requirements for drafting comprehensive 'embodiment descriptions' within patent specifications. It instructs users on how to detail methods, systems, and variations to teach a Person Having Ordinary Skill In The Art (POSITA) how to make and use the invention. Crucially, it emphasizes describing the 'how' (structure and process) and strictly prohibits the inclusion of empirical data, accuracy percentages, or performance test results.

This comprehensive checklist guides developers through ensuring that Fathom API integrations are robust, secure, and fully compliant for production use. It covers critical areas including secrets management, rate limiting, webhook verification, advanced error handling (like circuit breakers), PII compliance, and comprehensive monitoring setup, ensuring reliable meeting data pipelines.