deepagents

Provides architectural decision support and code review for Deep Agents multi-agent systems.

Updated Jul 18, 2026

Search Tags:#architecture #code review #middleware #multi-agent #langgraph #agentic

Ready to run

npx skills add https://github.com/arthrod/conejo-skills --skill deepagents

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deepagents
Source: https://github.com/arthrod/conejo-skills/tree/main/skills/deepagents
Command: npx skills add https://github.com/arthrod/conejo-skills --skill deepagents

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of designing and maintaining multi-agent systems, helping developers avoid architectural pitfalls and identify anti-patterns in agentic code.

Core Features & Use Cases

Architectural Guidance: Provides a decision framework for choosing backends, subagent strategies, and middleware stacks.
Code Review: Offers a comprehensive checklist to detect critical bugs like missing checkpointers, incorrect path handling, and state schema mismatches.
Use Case: Use this skill when you are building a new agentic workflow and need to decide between a FilesystemBackend or a StoreBackend, or when you need to audit your existing agent code for potential runtime failures.

Quick Start

Use the deepagents skill to review my current agent implementation for potential architectural anti-patterns and missing safety checks.

Frequently Asked Questions about deepagents

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

FAQPage Schema

How do I review multi-agent system code for architectural anti-patterns?▼

Reviewing multi-agent system code involves checking for missing checkpointers, incorrect path handling, and state schema mismatches. Use a comprehensive code review protocol to detect critical bugs and identify architectural anti-patterns in agentic workflows.

When do I need a FilesystemBackend versus a StoreBackend for LLM workflows?▼

You need a FilesystemBackend versus a StoreBackend for LLM workflows when selecting a persistence backend for your multi-agent system. Use an architectural decision framework to choose based on your state management and checkpointing requirements.

What is the best way to configure middleware for complex agentic workflows?▼

The best way to configure middleware for complex agentic workflows is using a decision framework that evaluates your subagent delegation patterns. This ensures technical compliance with state management and filesystem security requirements.

How do I ensure state management and checkpointing compliance in multi-agent systems?▼

To ensure state management and checkpointing compliance in multi-agent systems, audit your agent implementation for missing checkpointers and state schema mismatches. Architectural guidance helps maintain technical compliance for complex LLM workflows.

Can I use subagent delegation patterns with the Deep Agents framework?▼

Yes, you can use subagent delegation patterns with the Deep Agents framework. Architectural decision support facilitates the selection of appropriate delegation strategies and middleware configurations for complex LLM workflows.

Why does my multi-agent architecture fail with filesystem security errors?▼

Multi-agent architecture fails with filesystem security errors due to incorrect path handling and missing safety checks. Code review protocols detect these critical bugs and ensure technical compliance with filesystem security requirements.