Skill UI

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.

A comprehensive utility designed to gather all necessary diagnostic evidence for troubleshooting complex LangChain failures. It captures environment snapshots, dependency versions, detailed execution traces (including LLM calls and tool usage), and error stack traces. Use this bundle when preparing detailed GitHub bug reports or conducting LangSmith tracing.

A comprehensive diagnostic guide for resolving the 10 most common Langfuse integration errors. This resource covers root causes, specific error codes (like 401 Unauthorized or connection timeouts), and provides tested solutions for debugging missing traces, authentication failures, and general LLM observability issues.

This bundle collects comprehensive diagnostic evidence for Langfuse support tickets and troubleshooting. It gathers system environment details (Node.js, Python, OS), installed SDK versions, API connectivity status, and redacted application logs. Use this when encountering persistent or complex Langfuse issues to provide full context to support teams.

This comprehensive guide details the installation and configuration of the Langfuse SDK for LLM observability. It covers setup instructions for both JavaScript/TypeScript and Python environments. Users will learn how to retrieve necessary API keys, configure environment variables, and initialize tracing using OpenTelemetry, enabling full monitoring and debugging of AI application calls.

This guide provides a comprehensive local development setup for LLM applications using Langfuse. It enables fast iteration cycles, real-time trace debugging, and hot reloading, making it ideal for developers building and testing complex AI workflows locally. Instructions cover environment setup, advanced SDK usage, and optional self-hosted Docker deployment.

A comprehensive guide for setting up a local development loop to test AI agent integrations. This skill teaches developers how to build and debug webhook receivers, callback handlers, and associated application code that interacts with a cloud-hosted AI agent (like Lindy). It requires using tunneling services (e.g., ngrok) to expose local endpoints for receiving webhooks and simulating the entire agent-to-application data flow.

A comprehensive, production-ready toolkit for robustly debugging and monitoring Linear API integrations. This bundle includes features like an instrumented client wrapper for detailed request/response logging, a performance request tracer with summary statistics (avg, p95, max latency), a health check utility, and an environment validator. Ideal for developers setting up logging, performance monitoring, or debugging utilities in a live SaaS environment.

Implement robust, structured logging for API requests, capturing performance metrics, correlation IDs, and detailed security audit trails. This skill ensures compliance by handling PII redaction and providing comprehensive, JSON-formatted logs suitable for ELK Stack or Loki. Essential for debugging, performance analysis, and maintaining SOC 2 compliance in distributed microservices.

A comprehensive guide for diagnosing and fixing common Lokalise API errors and exceptions. This skill details the error response format and provides diagnostic cURL commands for specific HTTP status codes (401, 400, 404, 429, 413, 500, 503). Use it when encountering failed requests, debugging integration issues, or needing to verify API token validity and rate limit status.

A comprehensive tool designed to collect all necessary diagnostic evidence for troubleshooting Lokalise integration issues or filing support tickets. It gathers environment details (Node, Python, OS), tests API connectivity, lists project metrics, checks upload processes, and collects redacted logs, bundling everything into a single archive.

A comprehensive debugging toolkit designed for diagnosing complex issues within MaintainX integrations. It includes environment diagnostics, detailed request/response logging (using interceptors), and robust health checks for multiple core API endpoints. Use this when troubleshooting connectivity, data integrity, or unexpected API behavior.