Skill UI

Automates choosing and configuring database audit logging strategies, offering trigger/application/CDC/log-based options, schema templates, and guidance for compliance or debugging workflows.

This skill provides a comprehensive, multi-phase guided workflow for professional incident response management. Following modern SRE principles and the Incident Command System (ICS), it guides users from initial detection and triage (P0-P3 classification) through deep investigation (observability, debugging, security checks), culminating in structured resolution and post-mortem analysis. Use this when managing critical system outages or performance degradation.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

This skill provides a comprehensive diagnostic reference for common errors and exceptions encountered with the Instantly.ai API v2. It covers critical areas including HTTP status codes (400, 401, 429, 500), campaign lifecycle issues (e.g., stuck in draft), account health checks, lead duplication handling, and implementing rate limit backoff. Use this when debugging failed API requests or troubleshooting complex campaign, account, or lead management issues.

This skill collects a comprehensive debug bundle from the Instantly workspace. It gathers critical metrics including campaign state, account health, warmup analytics, webhooks, and background job status. The resulting structured JSON report is ideal for creating detailed support tickets, performing technical troubleshooting, or conducting internal system audits.

A comprehensive guide covering 33+ essential JavaScript concepts, from fundamental types and type coercion to advanced topics like closures, event loop mechanics, hoisting, and asynchronous programming (Promises). Ideal for developers who need to deepen their understanding of JavaScript's internal workings, debug complex behavior, or teach robust JS fundamentals.

This skill specializes in writing, debugging, and refactoring modern JavaScript code (ES2023+). It covers advanced patterns like async/await, Promise handling, ESM modules, and optimal performance for both browser and Node.js environments. Use it for vanilla applications, complex async flows, and performance optimization.

This bundle collects comprehensive diagnostic evidence from the Juicebox platform. It gathers API connectivity status, dataset health reports, analysis quota usage, and rate limit information into a single archive. Use it to systematically troubleshoot complex issues such as failed dataset uploads, stalled AI analysis runs, API authentication failures, or quota exhaustion.

A comprehensive guide applying Kaizen (continuous improvement) and Poka-Yoke (error proofing) principles to software development. Use this skill when you need to enhance code quality, refactor large systems incrementally, validate designs, or optimize development processes by focusing on prevention rather than fixing bugs.

This guide provides a comprehensive reference for diagnosing and resolving common errors encountered when interacting with the Kling AI API. It covers various HTTP status codes (e.g., 400, 401, 429), task-level failures (e.g., content policy violations, low image quality), proper JWT token management, and implementing rate limit handling with exponential backoff. Use this resource when troubleshooting failed video generation or API integration issues.

A comprehensive utility for structured logging, request tracing, and diagnostic purposes when integrating with the Kling AI API. It automatically captures request/response pairs, task lifecycle events, and detailed timing metrics for every API call, making troubleshooting video generation pipelines efficient and systematic. Ideal for developers building robust, observable AI applications.

This skill provides comprehensive monitoring and tracking for asynchronous video generation tasks using the Kling AI API. It supports polling single tasks, managing large batches of jobs, and detecting stalled or failed processes across multiple video endpoints. Ideal for developers building robust workflow dashboards, implementing reliable batch processing, or debugging long-running media generation pipelines.