agent-workflow

Design AI agent workflows with scoped architectures, tool integrations, and structured outputs.

Updated Mar 23, 2026
One-click install
npx skills add https://github.com/luyangliuable/claude-setup --skill agent-workflow-luyangliuable
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-workflow
Source: https://github.com/luyangliuable/claude-setup/tree/main/.agents/skills/agent-workflow
Command: npx skills add https://github.com/luyangliuable/claude-setup --skill agent-workflow-luyangliuable

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you turn vague or complex problems into clear AI agent workflows, so you can define scope, tool use, orchestration, memory, and delivery without over-engineering.

Core Features & Use Cases

  • Agent Design Guidance: Breaks down a problem into purpose, inputs/outputs, system instructions, tools, memory, and interface design.
  • Architecture Planning: Recommends when to use a single agent versus a multi-agent system and maps needs across an 8-layer framework.
  • Practical Templates and Examples: Provides reusable prompt templates, orchestration patterns, tool integration patterns, and case studies for workflows like customer support, sales qualification, content creation, and code review.
  • Use Case: A product or engineering team can use this Skill to design an AI assistant that researches data, makes decisions, calls tools safely, and produces structured outputs with guardrails.

Quick Start

Ask the skill to help design an AI agent for a specific workflow, including the goal, constraints, available tools, and desired output format.

Frequently Asked Questions about agent-workflow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a multi-agent workflow for complex business problems?

Design multi-agent workflows by translating business problems into scoped agent architectures, mapping needs across an 8-layer framework that covers inputs, prompts, memory, and tool integrations. It provides reusable orchestration patterns to structure your system reliably.

When should I use a single agent versus a multi-agent system?

Choose a single agent for simpler, scoped tasks and a multi-agent system when your workflow requires complex orchestration, diverse tool integrations, and distributed memory. Architecture planning recommends the optimal setup by evaluating your specific inputs, error recovery needs, and quality control requirements.

How do I integrate MCP and external APIs into an AI agent stack?

Integrate MCP and external APIs by applying structured tool integration patterns that ensure safe tool calling and reliable data retrieval. The design process maps tool use within the agent architecture, handling inputs, error recovery, and structured outputs.

What is the best way to structure prompts and memory for AI agents?

Structure prompts and memory by breaking down the problem into purpose, system instructions, and memory design within an 8-layer framework. Practical templates guide configuring memory and interface design to maintain context and produce structured outputs.

How do I add error recovery and quality control to agent workflows?

Add error recovery and quality control across the agent stack by applying structured workflow architecture patterns. The design framework requires clear handling of error states and delivery guardrails to ensure agents produce reliable outputs.

Can I use this to improve an existing AI agent instead of building a new one?

Yes, you can improve existing agents by applying the architecture planning framework to audit and refine current tool integrations, memory, and system instructions. It helps identify scope issues and restructure workflows for better quality control without over-engineering.