Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill darwinian-evolvergit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/darwinian-evolver)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/darwinian-evolver"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/darwinian-evolver.svg" alt="Measured on agentmods" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 17 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Prompt Injection · line 164 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
- medium Rogue Agent · line 40 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Rogue Agent · line 176 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 4d 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.
Copies of this mod
5 near-identical copies found in the catalogue:
- darwinian-evolver — 100% identical, 0 lines differ
- darwinian-evolver — 100% identical, 2 lines differ
- darwinian-evolver — 100% identical, 2 lines differ
- darwinian-evolver — 100% identical, 0 lines differ
- darwinian-evolver — 100% identical, 0 lines differ
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.
- 4d ago First seen · 200 lines · 22 tokens per session scan B bb2fa909ced5
darwinian-evolver is a skill published in the GitHub repository NousResearch/hermes-agent (243,146 stars, last pushed today), 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). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude…
claude-api-in-prototypes
Call Claude from your HTML artifacts via window.claude.complete.
llm-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
llm-engineering-expert
Build reliable applications on large language models: prompt design, structured output, evaluation, guardrails, and cost and latency control. Use when the user mentions LLMs, prompts, prompt engineering, few-shot examples, structured or JSON output, function calling, hallucination, model evaluation, token costs…
recipe-eval-prompt
Compares original and optimized prompts through repeated blind paired execution in git worktrees. Use when evaluating prompt improvement effects or learning prompt engineering through concrete examples.
c-ai
Query LLMs from the CLI — pipe text for summarization, chat interactively, use local or cloud models with llm or aichat.