Skill UI

A comprehensive reference for diagnosing and fixing silent breaking changes in React 18 class components. This guide covers how React 18 automatically batches state updates in asynchronous contexts (e.g., setTimeout, Promises, async/await), providing patterns for handling state reads after awaiting, and offering guidance on when to correctly use `flushSync` versus refactoring.

This guide maps user questions and topics (e.g., configuration, memory, skills, CLI) to specific, relevant documentation paths and source code entry points within the QwenPaw ecosystem. It is designed for built-in QA Agents to eliminate aimless searching, ensuring the agent reads the most accurate files to provide comprehensive and precise answers about the system's features.

This skill provides comprehensive knowledge on setting up and handling real-time webhook events from Lucidchart. It details how to receive notifications—such as document creation, updates, sharing, and shape additions—via signed JSON payloads over HTTPS. Users can implement robust integrations for compliance auditing, triggering CI/CD pipelines, or notifying external collaboration tools like Slack.

A comprehensive developer resource for diagnosing and resolving common Canva Connect API errors and HTTP status codes. It details issues such as expired tokens (401), insufficient permissions (403), rate limiting (429), and validation failures (400), providing specific causes, error payloads, and code-level fixes for seamless integration development.

Comprehensive solution for conducting load testing, performance benchmarking, and auto-scaling setup for various Adobe APIs (Firefly, PDF Services, Photoshop). This guide utilizes k6 for stress testing, simulating real-world user traffic, and includes Kubernetes HPA configuration to ensure the application maintains high availability and stability under peak loads.

This comprehensive guide details how to optimize Apify Actors for maximum efficiency, cost-effectiveness, and reliability. Learn to select the appropriate crawler (e.g., Cheerio, Playwright), tune concurrency limits, manage memory consumption, and implement robust proxy rotation strategies for diverse web scraping workloads, ensuring your scraping tasks are fast and stable.

A comprehensive guide on managing rate limits when interacting with the Apify Platform API. It details best practices for building robust, high-throughput automation workflows, covering built-in retries, request queuing using p-queue, batching large payloads, and staggering concurrent operations to prevent 429 errors and maintain system stability.

This comprehensive guide details how to implement robust webhook endpoints to receive, validate, and process data deliveries from Bright Data's Web Scraper and Datasets APIs. Learn best practices for handling large JSON/NDJSON payloads, verifying authorization headers, processing status notifications, and ensuring data deduplication in automated scraping pipelines.

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 guide details setting up and managing CoreWeave GPU cloud infrastructure across Development, Staging, and Production environments. It ensures strict resource separation using dedicated Kubernetes namespaces for GPU quotas and scaling policies. Use this when deploying complex ML/AI workloads requiring robust environment isolation and controlled promotion workflows.

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.