Skill UI

A comprehensive guide to optimizing Webflow usage costs. This skill teaches advanced strategies for reducing API expenses by prioritizing CDN-cached reads (free and unlimited), implementing bulk endpoint operations, caching collection schemas, and replacing inefficient polling with Webhook-driven updates. It also provides methods for API usage monitoring and recommending the optimal site plan.

Implement a comprehensive observability stack for Webflow Data API v2 integrations. This skill provides structured code using Prometheus metrics, OpenTelemetry distributed tracing, and structured logging (pino) to monitor API call frequency, latency, error rates, and rate limit usage. Ideal for developers building robust, production-grade Webflow-connected SaaS applications that require deep performance insights and reliable alerting.

A comprehensive guide to implementing production-ready patterns for the Webflow SDK. Covers essential architectural patterns such as singleton client management, robust typed error handling (including specific HTTP status codes like 401, 403, 429), efficient pagination helpers, rate limit monitoring via raw response access, and multi-tenant client factory setup. Essential for building scalable, enterprise-grade Webflow integrations.

This module provides the core `Logger` abstraction for structured logging within the Webiny API context. It supports multiple logging levels (e.g., debug, info, warn, error), ensuring that developers can record application events and operational data systematically. Using this abstraction helps in centralized logging, which is crucial for debugging, monitoring, and maintaining system health.

This extension component is integral to the KubeSphere Observability Platform. It provides robust mechanisms for collecting, processing, and storing Kubernetes events using the kube-events-exporter. Users can leverage the detailed event query API to gain deep insights into cluster activity, making it essential for comprehensive system monitoring and observability setup.

This skill provides high-performance, real-time object detection utilizing the Google Coral Edge TPU accelerator. It processes live camera frames to identify up to 80 COCO classes (e.g., person, car, dog). Optimized for native macOS and Linux deployment, it achieves extremely low latency (e.g., ~4ms at 320x320), making it ideal for embedded vision systems, surveillance, and low-latency monitoring applications.

A comprehensive server providing research-grade access to arXiv. Users can search papers by topic, author, or date, download full texts, and analyze the scientific landscape by traversing citation graphs. It also supports local semantic search over downloaded documents and enables monitoring for new papers with real-time alerts. Ideal for academic research and literature review.

WizTelemetry Ruler is a core extension component within the KubeSphere Observability Platform. It provides comprehensive capabilities for defining, managing, and evaluating advanced alerting rules. Users can configure rules based on native Kubernetes events, KubeSphere auditing events, and application logs, ensuring proactive monitoring and timely alerts through sinks like AlertManager.

This extension provides comprehensive distributed tracing capabilities for the KubeSphere Observability Platform, adhering to the OpenTelemetry standard. It guides users through the entire lifecycle, including component installation (Generator, Collector, Agent), configuration, and querying complex tracing data, spans, and service graphs. It is essential for monitoring microservices and understanding complex system interactions.

This CLI alias serves as the primary interface for the Codex /pua:pro protocol, designed for advanced code operations within the Claude Code environment. It manages complex workflows related to self-evolution, platform telemetry monitoring, key performance indicators (KPIs), and system configuration updates, ensuring robust and consistent protocol adherence.

A specialized Codex CLI alias designed to check the operational status of the PUA agent or team. This tool reports current active states, corresponding Process IDs (PID), Time To Live (TTL), and identifies stale or inactive records. It is primarily used for system debugging and monitoring agent health via the command line.

This CLI alias acts as a dedicated command to disable the PUA always-on mode feature, typically used within AI coding environments like Claude Code. By modifying the user's configuration file (`~/.pua/config.json`), it sets `always_on=false` and `feedback_frequency=0`. This function provides users with granular control, effectively turning off continuous monitoring or background feedback mechanisms managed by the AI assistant.