write-swarm-client

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.

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.

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.

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.

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.

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.