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LangChain Production Patterns Guide

langchain-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.

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.

Langfuse Tracing: Hello World Example

langfuse-hello-world

jeremylongshore/claude-code-plugins-plus-skills

A minimal example demonstrating how to implement basic tracing using the Langfuse SDK. It illustrates the hierarchy of traces, spans, and generations (LLM calls), providing code examples in TypeScript and Python for easy setup and integration testing.

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.

Optimizing LLM Observability Rate Limiting

langfuse-rate-limits

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide on implementing robust strategies for high-volume LLM observability workloads using Langfuse. Learn to manage API rate limits by implementing exponential backoff with jitter, optimizing SDK batching settings, controlling concurrency with queues, and utilizing configurable sampling to prevent data loss during periods of high traffic.

Langfuse LLM Observability Architecture Patterns

langfuse-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

This guide provides production-grade architecture patterns for implementing robust LLM observability using Langfuse. It covers singleton SDK setup, context propagation across asynchronous boundaries, Express middleware for automatic tracing, and cross-service trace correlation using OpenTelemetry. Ideal for designing enterprise-level, scalable tracing infrastructure for LLM applications.

Langfuse SDK Tracing Patterns and Best Practices

langfuse-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide covering production-grade patterns for using the Langfuse SDK. Learn how to implement robust LLM observability, including singleton client setup, using the `observe` wrapper for automatic span creation, and utilizing `startActiveObservation` for fine-grained trace control. Essential for maintaining high standards of observability in AI-powered applications.

Guide for Langfuse SDK Migration and Upgrade

langfuse-upgrade-migration

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide providing step-by-step instructions for upgrading Langfuse SDK versions (v3 to v5) and migrating between major API changes. It covers best practices for both TypeScript/JavaScript and Python implementations, focusing on adopting OpenTelemetry standards to ensure seamless LLM observability integration.

Configure Lindy AI API and Webhooks

lindy-install-auth

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive guide details the end-to-end setup process for integrating Lindy AI into external applications. Learn how to obtain and manage API keys, install and initialize SDKs (Python/Node.js), and securely configure webhook authentication endpoints to enable programmatic access and trigger AI agents.

Lindy AI Integration Patterns Guide

lindy-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

This skill provides comprehensive patterns for integrating external applications with the Lindy AI platform. It covers three primary channels: Webhook triggers for receiving inbound data, HTTP actions for making outbound API calls, and executing custom Python/JavaScript code (Run Code). Use this when building complex, multi-step automation workflows or connecting internal systems to Lindy agents.

Linear API Error Troubleshooting Guide

linear-common-errors

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for diagnosing and fixing common errors when interacting with the Linear API or SDK. Covers critical topics such as authentication failures (401), rate limiting (429), excessive query complexity, and handling null references. Essential for building robust and reliable software integrations.

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