darwinian-evolver

You can automate prompt optimization by using an LLM-driven Darwinian evolver to iteratively mutate and select artifacts based on fitness evaluation scores. This skill applies evolutionary algorithms to improve prompts, regex, SQL queries, or small code snippets automatically.

Evolutionary optimization is a process where artifacts like prompts or code snippets undergo fitness-based selection using trainable and holdout datasets. Lightweight evaluators and mutators guide iterative improvements, generating snapshots for reproducible analysis and rapid prototyping.

Yes, you need Python 3.11+ and access to either an OpenAI or OpenRouter API to execute the evolutionary loop. The included scripts, templates, and problem-driving code require this LLM access to generate mutations and evaluate fitness.

You optimize regex patterns by providing an initial artifact and running the evolver against a defined fitness function. The LLM-driven loop evaluates the regex patterns using trainable and holdout data sets, selecting the fittest variations for the next generation.

The best way to improve SQL queries through evolutionary algorithms is using a Darwinian evolver with lightweight evaluators to apply fitness-based selection. This approach enables rapid prototyping and reproducible experiments by guiding iterative improvements with feedback from evaluation results.

Yes, evolutionary optimization supports rapid prototyping and research by applying fitness-based selection to artifacts. By using trainable and holdout datasets, you can run reproducible experiments that generate snapshots for analysis and iterative improvement.