Borrowing it
Nothing to install: this file belongs to 0xSanei/darwinia. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/0xSanei/darwinia/main/.claude/SKILL.mdgit 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/claude)<a href="https://agentmods.dev/skills/0xsanei/darwinia/claude"><img src="https://agentmods.dev/badge/skills/0xsanei/darwinia/claude/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/claude"><img src="https://agentmods.dev/badge/skills/0xsanei/darwinia/claude.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 | $0.00028 | $0.00827 |
| Opus 5 | $0.00014 | $0.00413 |
| Sonnet 5 | $0.00006 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00083 |
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 11d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Darwinia — Self-Evolving Trading Agent Ecosystem
Evolves trading strategies through natural selection. 50 agents with random DNA compete on BTC data. Weak die, strong breed. Survivors handle rug pulls, fake breakouts, whipsaws.
When to use
- User asks to find, optimize, or evolve a trading strategy
- User wants adversarial stress-testing of trading logic
- User mentions "darwinia", "evolve strategy", or "adversarial test"
Setup
git clone https://github.com/0xSanei/darwinia.git && cd darwinia && pip install -e ".[dev]"
Commands
| Command | Time | Purpose |
|---|---|---|
python -m darwinia evolve -g 10 --json |
~30s | Quick demo |
python -m darwinia evolve -g 50 --json |
~3min | Full evolution + adversarial arena |
python -m darwinia arena --json |
~30s | Test champion against 6 attacks |
python -m darwinia info --json |
instant | Version and capabilities |
python -m darwinia dashboard |
— | Streamlit interactive dashboard |
Always use --json for programmatic calls.
Key output fields
champion.fitness: >1.0 = outperforms buy-and-holdchampion.genes: 17 floats [0,1] encoding full strategyevolution_summary.patterns_discovered: count of emergent rulespatterns: Emergent rules discovered by agents (not pre-programmed)
17-gene DNA
Signal (5): momentum, volume, volatility, mean_reversion, trend Threshold (4): entry, exit, stop_loss, take_profit Personality (5): risk_appetite, time_horizon, contrarian_bias, patience, position_sizing Adaptation (3): regime_sensitivity, memory_length, noise_filter
6 adversarial attacks
rug_pull, fake_breakout, slow_bleed, whipsaw, volume_mirage, pump_and_dump Arena reads agent DNA to find weaknesses and generates targeted scenarios.
Composability
Darwinia provides a two-way composability interface for cross-skill interop.
Inbound: other skills call Darwinia (SkillBridge)
from darwinia.integrations import SkillBridge
bridge = SkillBridge()
result = bridge.evolve({"generations": 20, "population_size": 30, "data_path": "data/btc_1h.csv"})
champion = bridge.get_champion()
score = bridge.evaluate_strategy([0.5] * 17)
regime = bridge.get_market_regime()
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.
- 11d ago First seen · 90 lines · 28 tokens per session scan A 43efbac44186
darwinia is a skill published in the GitHub repository 0xSanei/darwinia (87 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 827 once invoked, about $0.0001 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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