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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.

Learning Rate Scheduler

learning-rate-scheduler

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for configuring learning rate schedulers during ML training, covering best practices, setup patterns, and framework-ready code snippets for PyTorch, TensorFlow, and Scikit-learn while validating against common standards.

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.

Lindy AI Integration Deployment Guide

lindy-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for deploying the application layer that interacts with Lindy AI agents. This includes setting up webhook receivers, managing callback handlers, and ensuring production readiness across multiple platforms like Docker, Vercel, and AWS. It covers prerequisites, multi-step deployment instructions, post-deployment verification scripts, and rollback plans for robust MLOps practices.

Implementing Enterprise RBAC for Lindy AI

lindy-enterprise-rbac

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive guide details how to configure robust Role-Based Access Control (RBAC) for Lindy AI workspaces. It is essential for setting up team permissions, managing complex workspace access, and enforcing enterprise security policies. Learn how to implement advanced features like SSO, SCIM synchronization, and detailed audit logging to ensure data security and separation of duties across large organizations.

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.

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.

Multi-Environment AI System Deployment

lindy-multi-env-setup

jeremylongshore/claude-code-plugins-plus-skills

This guide details the best practices for configuring and isolating AI agents across development, staging, and production environments. It covers the setup of separate workspaces, secure secret management using tools like Vault/AWS/GCP, and integrating environment-aware logic into CI/CD pipelines to ensure data integrity and smooth promotion from dev to prod.

AI Agent Observability and Metrics Monitoring

lindy-observability

jeremylongshore/claude-code-plugins-plus-skills

This guide details how to implement comprehensive observability for AI agents. It covers monitoring key metrics such as task success rates, step failures, execution duration, and credit consumption. Use this when setting up performance dashboards, configuring real-time alerts (via webhooks/Prometheus), or tracking agent reliability over time.

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