What production best practices are included?

The skill emphasizes idempotency, robust error handling, retries, performance monitoring, and secure secret management using environment variables.

Does this skill support both no-code and custom code automation?

Yes, it provides optimized patterns for low-code platforms like n8n and Zapier, as well as full-code orchestration engines built with Python and TypeScript.

Does it support real-time data integration?

Yes, it provides standardized handlers for webhooks using FastAPI and background processing patterns using Celery and Redis.

Can I build multi-agent systems with this tool?

Absolutely. The skill includes specific architectural patterns for chaining multiple specialized agents together, such as researchers, writers, and reviewers.

AI Workflow Orchestrator

Name: AI Workflow Orchestrator
Author: Krosebrook

byKrosebrook

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생산성 및 워크플로

Orchestrates complex AI-powered workflows and multi-agent systems using n8n, Zapier, and custom integration patterns.

소개

The AI Workflow Orchestrator is a specialized skill designed to bridge the gap between Large Language Models and practical business automation. It provides developers with enterprise-grade patterns for building, deploying, and monitoring AI workflows across popular low-code platforms like n8n and Zapier, as well as custom-coded orchestration engines in Python and TypeScript. By implementing best practices for idempotency, error handling, and task queuing, this skill enables the creation of robust multi-agent systems that can handle complex research, content generation, and data processing tasks at scale.

주요 기능

Zapier custom code templates for advanced AI integrations
TypeScript multi-agent chain patterns for research and formatting
Pre-built n8n workflow patterns for AI agents and vector stores
Production-ready integration handlers for webhooks and task queues
Custom Python-based workflow engine with conditional logic
1 GitHub stars

사용 사례

Building automated AI-driven lead processing and customer support systems
Creating multi-stage research agents that gather, summarize, and format data
Orchestrating background data processing using FastAPI and Celery task queues

소개

주요 기능

Zapier custom code templates for advanced AI integrations
TypeScript multi-agent chain patterns for research and formatting
Pre-built n8n workflow patterns for AI agents and vector stores
Production-ready integration handlers for webhooks and task queues
Custom Python-based workflow engine with conditional logic
1 GitHub stars

사용 사례

Building automated AI-driven lead processing and customer support systems
Creating multi-stage research agents that gather, summarize, and format data
Orchestrating background data processing using FastAPI and Celery task queues