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 MilkyWay008/Hermes-OTG --skill darwinian-evolvergit clone --depth 1 https://github.com/MilkyWay008/Hermes-OTGWrote 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/milkyway008/hermes-otg/darwinian-evolver)<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/darwinian-evolver"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/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/milkyway008/hermes-otg/darwinian-evolver"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/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 | $0.02372 |
| Opus 5 | $0.00011 | $0.01186 |
| Sonnet 5 | $0.00004 | $0.00474 |
| 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 7d 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 — 0 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.
- 7d ago First seen · 200 lines · 22 tokens per session scan B bb2fa909ced5
darwinian-evolver is a skill published in the GitHub repository MilkyWay008/Hermes-OTG (15 stars, last pushed 27d ago), licensed MIT. It adds 22 tokens to every session and 2,372 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 0 lines, and is treated as a copy.
Other skills, from other repositories
agentforge-protocol
Use when doing non-trivial coding with Hermes, OpenClaw, Claude Code, Codex CLI, or similar autonomous coding agents. Orchestrates Karpathy-style minimal-change discipline, grill-plan intake, TDD, systematic debugging, subagent-driven implementation, spikes, and pre-commit review into one end-to-end workflow.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code.
python
Python development with ruff, mypy, pytest - TDD and type safety.
conductor-implement
Execute tasks from a track's implementation plan following TDD workflow.