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.