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Monitoring Kling AI Video Generation Jobs

klingai-job-monitoring

jeremylongshore/claude-code-plugins-plus-skills

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.

Collect LangChain Debug Evidence

langchain-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

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.

LangChain Observability And Monitoring Dashboard

langchain-observability

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

Debugging Common Langfuse Errors and Tracing

langfuse-common-errors

jeremylongshore/claude-code-plugins-plus-skills

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.

Langfuse LLM Tracing and Observability

langfuse-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Implement comprehensive end-to-end tracing for Large Language Model (LLM) calls, chains, and complex agents using Langfuse. This workflow demonstrates techniques like OpenAI drop-in wrappers, manual span creation for RAG pipelines, tracking streamed responses, and integrating with various LLM providers (OpenAI, Anthropic). Use this to monitor latency, track token usage, and debug complex AI features in production.

Collect Langfuse Diagnostic Evidence Bundle

langfuse-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

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.

Install And Configure Langfuse SDK

langfuse-install-auth

jeremylongshore/claude-code-plugins-plus-skills

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.

Langfuse Local Dev Workflow Setup

langfuse-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

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.

LangSmith Trace Debugger

langsmith-fetch

ComposioHQ/awesome-claude-skills

Fetch execution traces from LangSmith Studio to inspect LangChain/LangGraph agent runs, analyze errors, tool calls, memory usage, and performance, and export sessions for deeper diagnostics using the langsmith-fetch CLI.

LangSmith LLM Observability

langsmith-observability

Orchestra-Research/AI-Research-SKILLs

LangSmith centralizes tracing, evaluation, and monitoring for LLM applications, letting teams debug prompts, run regression tests, collect latency/cost metrics, and integrate with OpenAI, Anthropic, LangChain, or LlamaIndex to keep production systems observable.

Lindy AI Agent Debugging Bundle

lindy-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

Local Webhook Testing for AI Agents

lindy-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

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.

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