hermes-agent-self-evolution

Run evolutionary search with DSPy and GEPA to improve Hermes Agent skills and prompts.

2|1|Updated May 16, 2026

Search Tags:#optimization #agent #evolution #prompts #DSPy #Hermes #GEPA

Ready to run

npx skills add https://github.com/Aradotso/ai-agent-skills --skill hermes-agent-self-evolution

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hermes-agent-self-evolution
Source: https://github.com/Aradotso/ai-agent-skills/tree/main/skills/hermes-agent-self-evolution
Command: npx skills add https://github.com/Aradotso/ai-agent-skills --skill hermes-agent-self-evolution

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Hermes Agent Self-Evolution addresses the need for automated, data-driven improvement of Hermes Agent capabilities by applying evolutionary search to skills, tool descriptions, and prompts without requiring GPU training or manual re-implementation.

Core Features & Use Cases

Evolutionary optimization pipeline (DSPy + GEPA) that mutates and evaluates skill artifacts against synthetic or real sessions.
Automated improvement of tool descriptions, system prompts, and code with trace-driven feedback and guardrails.
Use cases include refining a GitHub code-review skill, enhancing web-scraping agents, and continuous improvement of agent behaviors in Hermes Agent repos.

Quick Start

Run the evolution pipeline to optimize Hermes Agent skills using the included datasets.

Frequently Asked Questions about hermes-agent-self-evolution

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

FAQPage Schema

How do I automate agent prompt optimization without GPU training?▼

You can automate agent prompt optimization using evolutionary search with DSPy and GEPA. This pipeline mutates and evaluates skill artifacts against datasets to iteratively improve agent prompts and tool descriptions without requiring GPU training or manual re-implementation.

What is evolutionary search for agent skills?▼

Evolutionary search for agent skills is an automated, data-driven improvement process using DSPy and GEPA. It applies mutation and crossover to system prompts and tool descriptions, evaluating them against synthetic or real sessions to continuously enhance agent capabilities.

Can I use DSPy and GEPA to evolve tool descriptions and system prompts?▼

Yes, you can use DSPy and GEPA to evolve tool descriptions and system prompts. The pipeline applies mutation and crossover operations to these artifacts, evaluating their performance against session datasets to drive automated, trace-driven improvements.

How do I integrate optimized agent prompts back into my repository?▼

You integrate optimized agent prompts back into your repository using PR-based integration. The evolution pipeline generates improvements through mutation and evaluation, then submits these optimized skill artifacts via pull requests for seamless integration into Hermes Agent repos.

Does the Hermes Agent evolution pipeline require manual code re-implementation?▼

No, the Hermes Agent evolution pipeline does not require manual code re-implementation. It applies evolutionary search to automatically mutate, evaluate, and improve tool descriptions, system prompts, and code using trace-driven feedback and guardrails.

What are the limitations of using evolutionary search for agent optimization?▼

A limitation of using evolutionary search for agent optimization is the reliance on evaluation datasets, requiring synthetic or real sessions for accurate trace-driven feedback. The pipeline also depends on PR-based integration to merge improvements into Hermes Agent repos.