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LangChain Security Best Practices Guide

langchain-security-basics

jeremylongshore/claude-code-plugins-plus-skills

This guide outlines essential security practices for deploying production-grade LLM applications using LangChain. It covers secure secrets management, defending against prompt injection, implementing sandboxed tool execution, validating model outputs for sensitive data, and setting up comprehensive audit logging, ensuring robust and secure AI deployment.

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.

Langfuse Data Migration Deep Dive

langfuse-migration-deep-dive

jeremylongshore/claude-code-plugins-plus-skills

Provides a deep dive and step-by-step scripts for complex Langfuse migrations. Covers comprehensive data export (traces, scores, prompts, datasets) and import processes. This guide is essential for platform transitions, moving between different Langfuse instances, or migrating from observability tools like LangSmith, supporting zero-downtime dual-write patterns.

Configure Multi-Environment Langfuse Setup

langfuse-multi-env-setup

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive guide details the best practices for configuring Langfuse across development, staging, and production environments. It covers essential steps including environment-specific API key management, secret integration (AWS/GCP), and implementing reliable CI/CD pipelines to ensure data isolation and prevent cross-environment leakage, maintaining observability throughout the software lifecycle.

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.

LibFuzzer Coverage Fuzzing

trailofbits/skills

LibFuzzer is LLVM’s in-process coverage-guided fuzzer for C/C++ projects compiled with Clang, providing a quick harness-based setup that can later transition to more advanced fuzzers while maintaining reproducible, high-throughput testing.

Automating Lindy Webhook CI/CD Testing

lindy-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

This guide provides the framework for configuring Continuous Integration and Continuous Deployment (CI/CD) pipelines specifically for Lindy AI agent integrations. It covers setting up automated tests for webhook receivers, validating payload processing, and checking authentication signatures using GitHub Actions and Node.js, ensuring robust and reliable deployment of agent services.

Advanced Workflow Automation and Delegation

lindy-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Master the creation of complex, event-driven, and scheduled automation workflows. This guide covers setting up various triggers (email, chat, webhook, calendar), implementing conditional filters, managing multi-step processes, and orchestrating specialized AI agents in a multi-agent 'Society' for advanced delegation and collaboration.

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