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genome-match

Score genetic compatibility across all male-female pairings in a Genomebook generation

Published by ClawBio ↗ClawBio/ClawBio

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genome-match

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Use genome-match to generate a research-only genetic-compatibility report for the genomes and disease registry in [GENOMEBOOK path], listing the top [N] pairings without treating it as a reproductive decision.

Required inputs

  • [GENOMEBOOK path]
  • [N]

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SKILL.md

name: genome-matchdescription: Score genetic compatibility across all male-female pairings in a Genomebook generationlicense: MITmetadata:   version: 0.1.0  author: Manuel Corpas  tags:   - genomebook  - compatibility  - heterozygosity  - disease-risk  - mating-selection  openclaw:     requires:       bins:       - python3    always: false    emoji: 💞    homepage: https://github.com/ClawBio/ClawBio    os:     - darwin    - linux    trigger_keywords:     - genome match    - compatibility    - mating pairs    - genomebook match    - heterozygosity score    - breeding pairs

💞 GenomeMatch

Purpose

Score genetic compatibility between all male-female pairings in a Genomebook generation. The engine evaluates heterozygosity advantage, disease carrier risk, and trait complementarity to rank optimal mating pairs for the next generation.

How It Works

  1. Load genomes for a target generation from GENOMEBOOK/DATA/GENOMES/.
  2. Compute pairwise compatibility for every M x F combination:
    • Heterozygosity score (40%): fraction of loci where offspring would be heterozygous (genetic diversity advantage).
    • Trait complementarity (40%): reward balanced trait combinations and high average trait values across the pair.
    • Disease risk penalty (20%): flag pairs where both parents carry recessive disease alleles (25% affected offspring risk per flagged condition).
  3. Rank all pairings by composite score (0.0 to 1.0).
  4. Select non-overlapping mating pairs via greedy selection from the top of the ranked list (each individual mates at most once per generation).

Input

  • GENOMEBOOK/DATA/GENOMES/*.genome.json
  • GENOMEBOOK/DATA/disease_registry.json

Output

  • Ranked compatibility table (all M x F pairings)
  • Selected mating pairs for the next generation

CLI Usage

# Score all pairings for generation 0
python skills/genome-match/genome_match.py

# Score a specific generation
python skills/genome-match/genome_match.py --generation 1

# Demo mode
python skills/genome-match/genome_match.py --demo

# Limit output to top N pairings
python skills/genome-match/genome_match.py --top 10

Output Format

Rank          Male x Female              Score   Het   Comp   Risk  Flags
   1      einstein-g0 x curie-g0         0.8234  0.650  0.821  0.000  --
   2      darwin-g0   x franklin-g0      0.7891  0.600  0.790  0.000  --
...

SELECTED MATING PAIRS (generation 0 -> 1):
  Albert Einstein x Marie Curie  (compat: 0.8234)
  Charles Darwin x Rosalind Franklin  (compat: 0.7891)