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darwinian-evolver

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

2|Updated Jun 8, 2026
Search Tags:#SQL#prompt engineering#optimization#evolution#regex#code optimization
One-click install
npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill darwinian-evolver-vikrant-project
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: darwinian-evolver
Source: https://github.com/vikrant-project/devil-agent-ai-platform/tree/main/agent_core/optional-skills/research/darwinian-evolver
Command: npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill darwinian-evolver-vikrant-project

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires darwinian_evolver, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables users to evolve prompts, regex patterns, SQL queries, and small code snippets using AI-driven evolutionary search, optimizing them against a fitness function.

Core Features & Use Cases

  • AI-Driven Evolution: Uses LLM-driven evolutionary search loops to optimize inputs.
  • Prompt and Regex Evolution: Enhance the effectiveness of prompts and regex patterns.
  • SQL and Code Optimization: Refine SQL queries and code snippets for better performance.
  • Use Case: A user may have a SQL query that doesn't perform well on a particular dataset. Using this Skill, they can evolve the query to improve its performance.

Quick Start

Run the 'darwinian-evolver' skill with a specific problem definition and let it optimize your prompt/regex/SQL/query.

Frequently Asked Questions about darwinian-evolver

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

FAQPage Schema
What is the best way to optimize SQL queries for a specific dataset?▼

AI-driven evolutionary search optimizes SQL queries by generating variations, testing them against a fitness function, and selecting the best performers. This refines your query for better performance on specific datasets.

Can I use LLM-driven evolutionary search to improve code performance?▼

You can evolve small code snippets by defining a problem and letting an LLM-driven evolutionary search loop optimize them. The system iteratively mutates and evaluates your code against a target fitness function.

What are the prerequisites for setting up an evolutionary search loop for code optimization?▼

Yes, you need an API key for OpenRouter, Anthropic, or OpenAI to run the AI-driven evolution. The environment also requires Python 3.11 and git installed to execute the evolutionary search loops.

When should I avoid using AI-driven evolution for regex and SQL tasks?▼

The limitations of evolutionary search include its dependency on external LLM API keys and its design for small code snippets or specific queries rather than large-scale codebases. It requires a clearly defined fitness function to evaluate optimizations accurately.

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