afd-directclient

Execute commands between co-located AI agents within a Node.js process.

4|1|Updated Dec 30, 2025
One-click install
npx skills add https://github.com/lushly-dev/afd --skill afd-directclient
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: afd-directclient
Source: https://github.com/lushly-dev/afd/tree/main/.claude/skills/afd-directclient
Command: npx skills add https://github.com/lushly-dev/afd --skill afd-directclient

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the need for extremely fast, low-latency command execution between AI agents that are running within the same Node.js process, eliminating the overhead of traditional network communication.

Core Features & Use Cases

  • In-Process Communication: Enables direct, high-speed command calls between co-located agents.
  • Performance Optimization: Achieves minimal latency (~0.03ms) for rapid agentic loops.
  • Use Case: When an AI agent needs to make a rapid sequence of tool calls to perform a complex task within the same application, DirectClient ensures the fastest possible execution.

Quick Start

Use the afd-directclient skill to create a new todo item with the title 'Fast task' and priority 'high'.

Frequently Asked Questions about afd-directclient

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

FAQPage Schema
How do I optimize AI agent communication latency in a Node.js application?

Zero-overhead agent communication bypasses network transport to optimize AI agent communication latency in Node.js. This approach enables direct command execution between co-located agents, reducing latency to approximately 0.03ms for rapid agentic loops.

What is zero-overhead command execution for co-located AI agents?

Zero-overhead command execution is a mechanism that facilitates direct, high-speed command calls between AI agents running within the same Node.js process. It eliminates traditional network communication overhead, achieving minimal latency for complex task completion.

Can I use direct client communication for security hardening and error handling?

Direct client communication supports security hardening, error handling, and observability patterns for embedded agent integrations. These features ensure robust in-process command execution while maintaining high-speed performance between co-located AI agents.

Why does my AI agent loop experience high latency during rapid tool calls?

AI agent loops experience high latency during rapid tool calls when relying on traditional network transport for command execution. Bypassing network transport for direct inter-process communication eliminates this overhead, achieving approximately 0.03ms latency per call.

When should I bypass network transport for agent communication?

You should bypass network transport for agent communication when multiple AI agents are co-located within the same Node.js process and require rapid sequences of tool calls. Direct inter-process communication eliminates network overhead, optimizing agentic loops for complex tasks.

Does direct client communication work with embedded agent integrations?

Direct client communication works with embedded agent integrations by supporting security hardening, error handling, and observability patterns. It enables zero-overhead command execution for co-located AI agents within a single Node.js process.