write-swarm-client

Name: write-swarm-client
Availability: InStock
Author: ModelScope

Scaffold AgentJet Swarm agent loop templates with episode management.

233|26|Updated Jan 7, 2026

Search Tags:#template #swarm #workflows #copilot #agent-loop #rl-training #agentjet

Ready to run

npx skills add https://github.com/modelscope/AgentJet --skill write-swarm-client

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: write-swarm-client
Source: https://github.com/modelscope/AgentJet/tree/main/ajet/copilot/write-swarm-client
Command: npx skills add https://github.com/modelscope/AgentJet --skill write-swarm-client

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the creation of a trainable agent loop and swarm client workflow for AgentJet, reducing boilerplate and accelerating RL experiments.

Core Features & Use Cases

Scaffold agent loop templates and run scripts to integrate with Ajetswarm workflows.
Provide guidance and templates for begin_episode, end_episode, and multi-agent coordination in distributed training.
Enable rapid prototyping of agent-driven experiments and reward calculation pipelines.

Quick Start

Create the agent workspace and launch an ajet-swarm client to begin training.

Frequently Asked Questions about write-swarm-client

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

FAQPage Schema

How do I set up a swarm client for distributed RL training?▼

To set up a swarm client for distributed RL training, you can scaffold agent loop templates and run scripts to integrate with AgentJet Swarm workflows, reducing boilerplate for multi-agent coordination.

What is an agent loop template and when do I need one for RL workflows?▼

An agent loop template provides a standardized structure for trainable agent loops, needed when prototyping RL workflows to manage episodes and integrate with the AgentJet API for reward calculation.

Can I manage individual training episodes using the AgentJet API?▼

Yes, you can manage individual training episodes using the AgentJet API through provided guidance and templates for begin_episode and end_episode functions, ensuring reproducible experiments.

Does this skill support multi-agent coordination across single-server and distributed setups?▼

Yes, this skill supports multi-agent coordination across both single-server and distributed setups by providing templates and safe defaults for scalable agent-driven experiments.

What's the best way to prototype agent-driven experiments and reward calculation pipelines?▼

The best way to prototype agent-driven experiments and reward pipelines is by automating the creation of trainable agent loops and swarm client workflows, enabling rapid prototyping with clear guidance.

Why should I use a scaffolded template for swarm-based agent training?▼

You should use scaffolded templates for swarm-based agent training to reduce boilerplate, ensure safe defaults, and maintain clear guidance for reproducible RL experiments across distributed setups.