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darwinian-evolver

Evolve prompt templates, regex patterns, and code snippets via LLM-driven search.

2|1|Updated Jul 14, 2026
Search Tags:#automation#research#optimization#llm#evolution#prompt-engineering
One-click install
npx skills add https://github.com/heysuhas/hermes_cli --skill darwinian-evolver-heysuhas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: darwinian-evolver
Source: https://github.com/heysuhas/hermes_cli/tree/main/optional-skills/research/darwinian-evolver
Command: npx skills add https://github.com/heysuhas/hermes_cli --skill darwinian-evolver-heysuhas

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, jinja2, and includes scripts (resource) components.

What problem does it solve?

This skill solves the challenge of manually tuning prompts, regex patterns, or code snippets by automating the search for optimal configurations using an LLM-driven evolutionary loop.

Core Features & Use Cases

  • Automated Optimization: Iteratively evolves artifacts like prompt templates or SQL queries against a defined fitness function.
  • Custom Problem Definition: Provides a flexible framework to define organisms, evaluators, and mutators for domain-specific tasks.
  • Use Case: If you have a prompt that is failing to consistently extract data from unstructured text, use this skill to evolve the prompt template until it achieves a high success rate across your test dataset.

Quick Start

Use the darwinian-evolver skill to optimize the prompt template in custom_problem_template.py by running the evolution loop with your specific fitness criteria.

Frequently Asked Questions about darwinian-evolver

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

FAQPage Schema
How do I automate prompt engineering to optimize LLM templates?▼

Automate prompt engineering by defining a fitness function and running an LLM-driven evolutionary search loop that mutates and evaluates prompt templates until achieving a high success rate.

Can I use an evolutionary search loop to refine regex patterns and SQL queries?▼

Yes, evolutionary search loops iteratively mutate and evaluate regex patterns and SQL queries against a custom fitness function to refine and optimize domain-specific artifacts for high performance.

What do I need to run an LLM-driven mutation and evaluation cycle?▼

To run mutation and evaluation cycles you need Python 3.11+, the uv package manager, and an LLM provider API key to execute the evolutionary search loop.

How does LLM-driven evolution work for optimizing code snippets?▼

LLM-driven evolution works by treating code snippets as organisms, applying mutations via the LLM, and evaluating them against a defined fitness function to iteratively select and optimize algorithmic configurations.

When should I use an evolutionary search to fix failing prompt templates?▼

Use evolutionary search when a prompt template fails to consistently extract data from unstructured text, allowing the automated loop to evolve the prompt until it succeeds across test datasets.

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