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KarlinskyS
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hermes-atropos-environments

Build and debug Atropos RL environments for the Hermes Agent.

Updated Apr 10, 2026
Search Tags:#tooling#environment#training#rl#reward#hermes#atropos
One-click install
npx skills add https://github.com/KarlinskyS/hermesSkills --skill hermes-atropos-environments-karlinskys
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hermes-atropos-environments
Source: https://github.com/KarlinskyS/hermesSkills/tree/main/mlops/training/hermes-atropos-environments
Command: npx skills add https://github.com/KarlinskyS/hermesSkills --skill hermes-atropos-environments-karlinskys

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It streamlines the creation, testing, and debugging of custom RL environments for the Hermes Agent within the Atropos training framework, removing manual boilerplate and integration errors.

Core Features & Use Cases

  • Base Environment Integration: Provides a HermesAgentBaseEnv class that manages the multi‑turn agent loop with tool calls.
  • Reward Function Templates: Guides implementation of compute_reward using LLM judges, sandbox verification, or heuristic scoring.
  • CLI Modes: Offers serve, process, and evaluate commands to run full training loops, offline data generation, or evaluation runs.
  • Use Case Example: Developers building a new environment MyEnv can focus on task‑specific logic while relying on the built‑in infrastructure for dataset handling, wandb logging, and Atropos server communication.

Quick Start

Ask the assistant to create a new Hermes Atropos RL environment named MyEnv in the hermes-agent repository.

Frequently Asked Questions about hermes-atropos-environments

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

FAQPage Schema
How do I build a custom RL environment for the Hermes Agent?▼

To build a custom RL environment, use the HermesAgentBaseEnv class to manage the multi-turn agent loop with tool calls, letting you focus on task-specific logic while relying on built-in infrastructure for dataset handling and logging.

What's the best way to debug reward functions in Atropos training environments?▼

Debug Atropos reward functions by using the CLI evaluate command to run evaluation modes, guiding your implementation of compute_reward through LLM judges, sandbox verification, or heuristic scoring to validate logic.

Do I need an Atropos server endpoint to test my Hermes Agent RL training loop?▼

Yes, testing an RL training loop requires an active Atropos server endpoint for inference, alongside Python and the hermes-agent repository to run the serve, process, and evaluate CLI commands.

Can I generate offline training data with Atropos without running a full training loop?▼

Yes, you can generate offline training data by utilizing the CLI process command, which handles dataset generation independently without needing to execute the full serve training loop.

What is the HermesAgentBaseEnv class used for in Atropos?▼

The HermesAgentBaseEnv class provides base environment integration that manages the multi-turn agent loop with tool calls, streamlining the creation of custom Atropos-compatible environments by removing manual boilerplate.

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