Skill UI

This guide provides a comprehensive reference for diagnosing and fixing common Webflow Data API v2 HTTP errors (400, 401, 403, 404, 409, 429, 500). It covers the root causes and offers concrete code solutions in TypeScript and Bash for issues like invalid tokens, insufficient scope, or rate limits, helping developers effectively troubleshoot failed Webflow integrations and ensure data flow stability.

This utility collects comprehensive diagnostic evidence necessary for troubleshooting Webflow integrations and API usage. It automatically gathers crucial data points into a single, support-ready diagnostic bundle. Key metrics include the Webflow SDK version, API token validation status, rate limit headers, site connectivity reports, CMS health checks, and recent error logs, significantly streamlining the technical support process.

A comprehensive guide for deploying, developing, and debugging Webiny serverless applications locally. Covers deployment using `yarn webiny deploy`, local watching for API and Admin components, managing various environments (dev, staging, prod), and troubleshooting infrastructure errors on AWS.

This guide provides comprehensive troubleshooting and error handling documentation for integrating the Wispr Flow voice-to-text API. It covers common issues across WebSocket streaming, REST API calls, and general dictation workflows. Users will learn how to address errors like 'Unauthorized' keys, network disconnections, and poor accuracy by utilizing best practices for context setting and API reconnection strategies.

This comprehensive guide provides structured troubleshooting and error handling advice for developers integrating with the Workhuman Social Recognition and Rewards APIs. It covers critical API concepts, including OAuth 2.0 authentication, managing point-based nominations, and synchronizing data with major HRIS systems like Workday and SAP SuccessFactors. Use this resource when building complex HR workflows or integrating Workhuman into platforms like Slack or Teams.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.

A comprehensive guide for managing and operating the KubeSphere network extension. This skill provides detailed instructions on installing, upgrading, enabling, or disabling the network extension, managing Calico IPPool resources, configuring NetworkPolicies, and troubleshooting network isolation flows within a Kubernetes cluster environment. Essential for platform engineers and DevOps teams.

This skill provides comprehensive operations for managing NodeGroup resources. It allows users to query, create, update, delete, and manage bindings for nodes, namespaces, and workspaces within the edgewize nodegroup environment. Ideal for infrastructure automation, troubleshooting, and API-driven cluster management.

A comprehensive guide and pattern library for reviewing, troubleshooting, and safely modifying Cisco IOS and IOS-XE configurations. This skill outlines best practices for using read-only 'show' commands, validating ACL wildcard masks, managing configuration hierarchy, and following stringent change control procedures to prevent network disruptions.

This tool provides comprehensive diagnostics for Border Gateway Protocol (BGP) within network environments. It helps engineers troubleshoot connectivity issues, verify routing paths, and diagnose complex peering problems across large-scale networks. Ideal for network operations and advanced troubleshooting.

This guide helps users navigate and understand the complex components of the ECC (Everything Claude Code) ecosystem, including skills, commands, agents, hooks, and rules. It provides structured paths for new user onboarding, feature discovery, installation planning, and troubleshooting, ensuring answers are derived from the current repository state.

This skill parses raw, verbose Simplicité log files from TXT format, filtering out noise and extracting only essential fields (e.g., timestamp, level, body). By converting logs into structured JSON, it significantly reduces the token size required for AI context optimization, making troubleshooting efficient and reliable.