darwinia: The Self-Evolving Agent Ecosystem — Trading agents that evolve through Darwinian selection and adversarial

by 0xSanei · Codex Skill · ★ 86

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install darwinia

git clone https://github.com/0xSanei/darwinia.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 65/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

adversarial-testingai-agentsautonomous-agentsbacktestingbitcoindarwinian-evolutionevolutionary-computinggenetic-algorithmmulti-agent-systemopenclaw

Quick Facts

Stars86
Forks5
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score65/100
Last Updated2026-04-13
Created2026-04-03
Platformspython
Est. Tokens~71k

About darwinia

🧬 Darwinia Give your AI agent the power to evolve its own trading strategies. Trading agents that discover strategies through Darwinian selection and adversarial self-play — not human-written rules. <img src="https://colab.research.goog

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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