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Evolve prompts/regex/SQL/code with Imbue's evolution loop.
Passed 16 of 19 test cases with the skill, and 6 of 19 without it.
and 14 more in the full report
12 fixed6 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which environment variable is used to select the LLM model when running the custom OpenRouter driver script (`parrot_openrouter.py`) shipped with the skill?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies EVOLVER_MODEL as the environment variable used for specifying the OpenRouter model. | Missed | Passed |
The instructions your model reads when the skill loads.
Run Imbue's 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.
Do not use this when:
git, uv (or pip)OPENROUTER_API_KEY, ANTHROPIC_API_KEY, or OPENAI_API_KEYThe skill ships a small parrot_openrouter.py driver that uses OPENROUTER_API_KEY
via the OpenAI SDK, so any model on OpenRouter works. The upstream CLI itself
hardcodes Anthropic and needs ANTHROPIC_API_KEY.
test cases passed, vs 6 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.