🧬 Soul2DNA Compiler

Purpose

Compile SOUL.md character profiles into synthetic diploid genomes. Each soul file describes a historical or fictional figure with trait scores (0.0 to 1.0). The compiler maps these scores to alleles at defined loci using additive, dominant, or recessive inheritance models, producing a .genome.json file per character.

How It Works

1. Parse SOUL.md files from GENOMEBOOK/DATA/SOULS/ extracting identity

metadata (name, sex, ancestry, domain, era) and trait scores.

1. Load trait registry (GENOMEBOOK/DATA/trait_registry.json) which defines

loci, alleles, chromosomal positions, dominance models, and effect sizes for each trait.

1. Assign genotypes at each locus based on trait score thresholds:

• Additive: <0.33 ref/ref, 0.33-0.66 ref/alt, >0.66 alt/alt
• Dominant: <0.40 ref/ref, 0.40-0.75 ref/alt, >0.75 alt/alt
• Recessive: <0.50 ref/ref, 0.50-0.80 ref/alt, >0.80 alt/alt

1. Write genome as JSON with full locus detail, trait scores, and metadata.

Input

• GENOMEBOOK/DATA/SOULS/*.soul.md (20 historical figures)
• GENOMEBOOK/DATA/trait_registry.json

Output

• GENOMEBOOK/DATA/GENOMES/<name>-g0.genome.json per character

CLI Usage

bash
# Compile all souls to genomes
python skills/soul2dna/soul2dna.py

# Demo mode (shows summary without writing files)
python skills/soul2dna/soul2dna.py --demo

Output Format

Each .genome.json contains:

json
{
  "id": "einstein-g0",
  "name": "Albert Einstein",
  "sex": "Male",
  "sex_chromosomes": "XY",
  "ancestry": "...",
  "generation": 0,
  "parents": [null, null],
  "loci": { "<locus_id>": { "chromosome": "...", "alleles": ["A","G"], ... } },
  "trait_scores": { "curiosity": 0.95, ... }
}