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 0xSanei/darwinia --skill openclawgit clone --depth 1 https://github.com/0xSanei/darwiniaWrote 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/0xsanei/darwinia/openclaw)<a href="https://agentmods.dev/skills/0xsanei/darwinia/openclaw"><img src="https://agentmods.dev/badge/skills/0xsanei/darwinia/openclaw/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/0xsanei/darwinia/openclaw"><img src="https://agentmods.dev/badge/skills/0xsanei/darwinia/openclaw.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 | $0.00036 | $0.01372 |
| Opus 5 | $0.00018 | $0.00686 |
| Sonnet 5 | $0.00007 | $0.00274 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
darwinia scanned grade A with 0 findings 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 13d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Darwinia — The Self-Evolving Agent Ecosystem
Darwinia evolves trading strategies through natural selection. 50 agents compete on real BTC market data, the weak die, the strong breed. After 50 generations, survivors handle rug pulls, fake breakouts, and whipsaws — because agents that couldn't survive didn't reproduce.
When to use this skill
- User asks to "find a good trading strategy" or "optimize trading parameters"
- User wants to stress-test a strategy against adversarial market conditions
- User asks about genetic algorithms applied to trading
- User wants to discover market patterns automatically
- User says "evolve", "darwinia", "genetic trading", or "adversarial test"
Commands
Quick evolution (~30 seconds)
python -m darwinia evolve -g 10 --json
Full evolution with adversarial arena (~3 minutes)
python -m darwinia evolve -g 50 --json
Test champion against 6 attack types
python -m darwinia arena --json
System info
python -m darwinia info --json
Interactive dashboard
python -m darwinia dashboard
Always use --json when calling programmatically.
Interpreting results
Key JSON fields after evolve --json:
champion.fitness: Risk-adjusted score. >1.0 = outperforms buy-and-hold.champion.genes: 17 floats [0,1] encoding the full trading strategy.evolution_summary.patterns_discovered: Number of emergent trading rules found.patterns: Emergent trading rules discovered by agents (not pre-programmed).
How to explain results to user
- Fitness > 1.0 → Champion outperforms buy-and-hold on risk-adjusted basis
- High genetic diversity → Population hasn't converged yet, more generations may help
- Discovered patterns → Trading rules the agents found on their own
Installation
git clone https://github.com/0xSanei/darwinia.git
cd darwinia
pip install -e ".[dev]"
No API keys. No cloud. Pure Python + numpy. BTC/USDT 1h data (10,946 candles) included.
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.
- 13d ago First seen · 180 lines · 36 tokens per session scan A b831ce66793b
darwinia is a skill published in the GitHub repository 0xSanei/darwinia (87 stars, last pushed 5mo ago), licensed MIT. It adds 36 tokens to every session and 1,372 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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