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docs: update README with validation/explainability section, 30 tests badge
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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README.md

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<a href="https://claude.ai"><img src="https://img.shields.io/badge/Claude_Code-compatible-blueviolet?style=flat-square" alt="Claude Code"></a>
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<img src="https://img.shields.io/badge/genes-17-gold?style=flat-square" alt="17 Genes">
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<img src="https://img.shields.io/badge/attacks-6_types-red?style=flat-square" alt="6 Attack Types">
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<img src="https://img.shields.io/badge/tests-22_passing-brightgreen?style=flat-square" alt="22 Tests">
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<img src="https://img.shields.io/badge/tests-30_passing-brightgreen?style=flat-square" alt="30 Tests">
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<a href="https://colab.research.google.com/github/0xSanei/darwinia/blob/main/notebooks/quickstart.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"></a>
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</p>
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python -m darwinia evolve --json # JSON output for agents
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python -m darwinia arena -c output/champions/champion_gen_0049.json
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python -m darwinia arena -r 10 --json # 10 rounds, JSON output
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python -m darwinia validate -w 3 -g 20 # Walk-forward overfitting check
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python -m darwinia explain -c champion.json # Gene ablation analysis
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python -m darwinia dashboard # Web UI
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python -m darwinia info --json # System info as JSON
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```
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scripts/ # Competitor monitoring, utilities
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```
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### Validation & Explainability
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**Walk-Forward Validation** splits data chronologically (train on past, test on future). If fitness degrades out-of-sample, the strategy is overfitting.
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```bash
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python -m darwinia validate -w 3 --json
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```
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**Gene Ablation** zeroes each gene one at a time and measures fitness drop. Genes that cause the biggest drop are the most important — proving the agent found real signal, not noise.
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```bash
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python -m darwinia explain -c champion.json --json
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```
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### Three Layers
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| Layer | Status | What It Does |

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