claudegoodies
Skill

darwinian-evolver

From NousResearch

Evolve prompts/regex/SQL/code with Imbue's evolution loop.

Facts

Repository
NousResearch/hermes-agent
Status
Actively maintained
Last commit
Source file
optional-skills/research/darwinian-evolver/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# Darwinian Evolver

Run Imbue's [darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) — an
LLM-driven evolutionary search loop — to optimize a **prompt, regex, SQL query,
or small code snippet** against a fitness function.

Status: thin wrapper around the upstream tool. The skill installs it, walks the
agent through writing a `Problem` definition (organism + evaluator + mutator),
and drives the loop via the upstream CLI or a small custom Python driver.

**License:** the upstream tool is **AGPL-3.0**. The skill ONLY ever invokes it
via the upstream CLI or a `subprocess`/`uv run` call (mere aggregation). Do NOT
import upstream classes into Hermes itself.

## When to Use

- User says "optimize this prompt", "evolve a regex for X", "auto-improve this
  code/SQL", "search for a better instruction".
- You have a scorer (exact match, regex pass-rate, unit test, LLM-judge, runtime
  metric) AND a starting candidate (organism). If you don't have a scorer, stop
  and define one first — that's the hard part.
- Cost is OK: a typical run is 50–500 LLM calls. On gpt-4o-mini that's pennies;
  on Claude Sonnet it can be a few dollars.

Do **not** use this when:
- The optimization target is differentiable (use gradient descent / DSPy).
- You only need to try 2–3 variants — just write them by hand.
- The fitness signal is purely subjective with no measurable criterion.

## Prerequi
View full source on GitHub →

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