Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cyborg-garden/hermes-agent-mt --skill darwinian-evolvergit clone --depth 1 https://github.com/cyborg-garden/hermes-agent-mtWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/darwinian-evolver)<a href="https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/darwinian-evolver"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/darwinian-evolver/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/darwinian-evolver"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/darwinian-evolver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.02373 |
| Opus 5 | $0.00011 | $0.01187 |
| Sonnet 5 | $0.00004 | $0.00475 |
| Haiku 4.5 | $0.00002 | $0.00237 |
Grade B, and why
darwinian-evolver scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
reject phrases like "ignore previous instructions" with HTTP 400. Wrap Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
This is a copy
100% identical to darwinian-evolver — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Darwinian Evolver
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.
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.
Prerequisites
- Python ≥3.11
git,uv(orpip)- One of:
OPENROUTER_API_KEY,ANTHROPIC_API_KEY, orOPENAI_API_KEY
The 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.
Install (One-Time)
Run via the terminal tool:
mkdir -p ~/.hermes/cache/darwinian-evolver && cd ~/.hermes/cache/darwinian-evolver
[ -d darwinian_evolver ] || git clone --depth 1 https://github.com/imbue-ai/darwinian_evolver.git
cd darwinian_evolver && uv sync
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 200 lines · 22 tokens per session scan B 962725ad3936
darwinian-evolver is a skill published in the GitHub repository cyborg-garden/hermes-agent-mt (13 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 2,373 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). It is 100% identical to darwinian-evolver, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
darwinian-evolver
Evolve prompts/regex/SQL/code with Imbue's evolution loop.
data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
dbt-transformation-patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
testing-llm
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.
deepeval
Use when discussing or working with DeepEval (the python AI evaluation framework).
hatch3r-ai-feature
Eval-driven development workflow for shipping AI features — write eval before prompt, measure, iterate, ship with caching + cost telemetry + model fallback + hallucination SLI.