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darwinian-evolver

Evolve prompts, regex patterns, SQL queries, and code snippets via LLM-driven search.

Updated May 4, 2026
Search Tags:#automation#research#optimization#llm#evolution#prompt-engineering
One-click install
npx skills add https://github.com/InverterNetwork/hermes-agent --skill darwinian-evolver-inverternetwork
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: darwinian-evolver
Source: https://github.com/InverterNetwork/hermes-agent/tree/main/optional-skills/research/darwinian-evolver
Command: npx skills add https://github.com/InverterNetwork/hermes-agent --skill darwinian-evolver-inverternetwork

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill solves the challenge of manually tuning prompts, regex patterns, SQL queries, or code snippets by automating the search for optimal versions using an evolutionary loop.

Core Features & Use Cases

  • Automated Optimization: Uses an LLM-driven evolutionary loop to iteratively improve artifacts against a defined fitness function.
  • Custom Problem Definition: Provides templates to define organisms, evaluators, and mutators for domain-specific optimization tasks.
  • Use Case: If you have a prompt that is failing to produce consistent results, this skill can evolve the prompt template by testing it against a set of failure cases until it achieves a higher success rate.

Quick Start

Run the darwinian-evolver skill to optimize your prompt template by executing the parrot_openrouter script with your specific iteration and concurrency settings.

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 optimization for consistent LLM outputs?▼

Automate prompt optimization by running an LLM-driven evolutionary search loop that iteratively mutates and evaluates prompt templates against defined fitness criteria until success rates improve.

What is an evolutionary search loop for tuning regex patterns and SQL queries?▼

An evolutionary search loop for regex and SQL optimization applies automated mutation, evaluation, and population management to iteratively refine code snippets against measurable fitness criteria.

How do I evolve failing prompt templates using a fitness function?▼

Evolve failing prompt templates by defining custom organisms, evaluators, and mutators that test the template against failure cases, driving iterative refinement through the evolution framework.

Can I use OpenAI with the darwinian-evolver to optimize code snippets?▼

Yes, OpenAI integration supports the LLM-driven evolutionary loop to optimize code snippets, requiring Jinja2 and Pydantic dependencies to manage templates and validate structured data.

What are the limitations of using LLM-driven evolution for engineering workflows?▼

The main limitation of LLM-driven evolution is its reliance on explicitly defined, measurable fitness criteria; without accurate evaluation functions, the automated mutation and population management cannot effectively optimize artifacts.

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