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Centralized Metrics Aggregation

aggregating-performance-metrics

jeremylongshore/claude-code-plugins-plus-skills

Aggregates performance metrics from applications, systems, databases, caches, queues, and external services into a central platform, helping teams define a consistent taxonomy, choose aggregation tools, and create dashboards and alerts for faster monitoring and troubleshooting.

Azure Service Bus .NET Messaging SDK

azure-servicebus-dotnet

sickn33/antigravity-awesome-skills

The comprehensive official SDK for implementing enterprise-grade messaging patterns in .NET applications. It provides reliable connectivity to Azure Service Bus, supporting queues, topics, subscriptions, and ordered sessions. Ideal for building microservices, asynchronous communication, and durable event-driven architectures.

Azure Service Bus Python Messaging Client

azure-servicebus-py

sickn33/antigravity-awesome-skills

This comprehensive SDK provides a robust Python interface to interact with Azure Service Bus. It enables developers to implement reliable, scalable enterprise messaging patterns, including point-to-point queues, publish/subscribe topics, and session-based communication. Use it for building resilient, decoupled microservices and distributed cloud architectures.

Azure Service Bus Messaging with TypeScript

azure-servicebus-ts

sickn33/antigravity-awesome-skills

A comprehensive guide on using the Azure Service Bus SDK in TypeScript for enterprise messaging. It demonstrates core asynchronous patterns, including sending/receiving messages from queues, publishing to topics with subscriptions, managing message sessions, and handling advanced features like dead-lettering and scheduled messages. Essential for building robust, scalable, event-driven microservices.

Mastering Job Queues With BullMQ

bullmq-specialist

sickn33/antigravity-awesome-skills

A comprehensive guide to implementing reliable, scalable, and resilient background job processing in Node.js/TypeScript using BullMQ and Redis. Covers advanced patterns such as delayed jobs, repeatable scheduling, job dependencies (flows), rate limiting, robust error handling, and graceful worker shutdown, ensuring production-grade async execution.

Self-Learning Correction System for Claude

jeremylongshore/claude-code-plugins-plus-skills

A sophisticated, two-stage system designed to capture and internalize user corrections and learnings during ongoing conversations. It automatically detects patterns (e.g., "use X instead of Y") and queues them. Users can manually review and apply these learnings to a persistent knowledge base (CLAUDE.md), ensuring the AI continuously improves its knowledge and adherence to specific user context for future sessions.

Scaling High-Volume Data Processing Pipelines

clay-load-scale

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide and code implementation for scaling data enrichment pipelines to handle high volumes (10K-100K+ leads monthly). It covers capacity planning, optimizing batch processing using queues (e.g., BullMQ/Redis), managing rate limits, and designing multi-table architectures for robust, large-scale data ingestion.

Implementing Fault-Tolerant Data Pipelines

clay-reliability-patterns

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to building resilient and fault-tolerant data enrichment pipelines. Learn to implement critical patterns like credit budget circuit breakers, Dead Letter Queues (DLQs) for failed records, and health monitoring to ensure stability and cost control when integrating with external services like Clay.

Cloud Tasks Queue Setup

cloud-tasks-queue-setup

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for setting up Cloud Tasks queues across GCP workloads, covering patterns, best practices, and vetted configs.

Implement Resilient Customer.io Integration Patterns

customerio-reliability-patterns

jeremylongshore/claude-code-plugins-plus-skills

This guide provides technical patterns for building fault-tolerant integrations with Customer.io. Learn to implement critical resilience techniques, including Circuit Breakers to prevent cascading failures, Retry with Jitter for handling transient errors, and Fallback Queues (using Redis) to ensure data continuity during service outages. Essential for robust, production-grade applications.

Go Durable Workflow Best Practices

sickn33/antigravity-awesome-skills

This guide provides essential best practices for building reliable, fault-tolerant applications in Go using DBOS durable workflows. It covers architectural patterns for integrating DBOS into existing Go code, defining complex workflows, managing steps, and utilizing queues for robust concurrency control. Essential reading for developers setting up, configuring, or testing mission-critical asynchronous processes.

Building Reliable Workflows with DBOS Python

sickn33/antigravity-awesome-skills

A comprehensive guide detailing best practices for building reliable, fault-tolerant Python applications utilizing DBOS durable workflows. This resource is essential when integrating DBOS into existing Python codebases, defining complex workflows and steps, managing concurrency via queues, or implementing robust workflow communication patterns.

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