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/recombinator

Produce offspring genomes from parent pairs via meiotic recombination, mutation, and clinical evaluation

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clawbio
1.1k97 skills4 commands
Install
$ npx -y skills add ClawBio/ClawBio --skill recombinator --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/recombinator

Context preview

The summary Claude sees to decide when to auto-load this skill.

Produce offspring genomes from parent pairs via meiotic recombination, mutation, and clinical evaluation

SKILL.md

recombinator.SKILL.md
name: recombinator
description: Produce offspring genomes from parent pairs via meiotic recombination, mutation, and clinical evaluation
license: MIT
metadata:
  version: 0.1.0
  author: Manuel Corpas
  tags:
  - genomebook
  - recombination
  - meiosis
  - mutation
  - offspring
  - clinical-genetics
  openclaw:
    requires:
      bins:
      - python3
    always: false
    emoji: 🧪
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    trigger_keywords:
    - recombinator
    - recombination
    - offspring
    - breed
    - meiosis
    - genomebook breed
    - next generation

🧪 Recombinator

Purpose

Produce offspring genomes from selected parent pairs via simulated meiotic recombination. Models Mendelian segregation, de novo mutation, sex determination, and clinical evaluation against a disease registry.

How It Works

1. **Mendelian segregation**: one allele inherited from each parent per locus (random selection simulating independent assortment). 2. **De novo mutation**: configurable rate per locus (default 0.1%), with hotspot multipliers for cognitive, immune, and metabolic loci. Mutations are classified as disease-risk, protective, or neutral. 3. **Sex determination**: 50/50 coin flip (XY or XX). 4. **Trait inference**: reverse-map offspring genotype back to trait scores using the trait registry, accounting for dominance models. 5. **Clinical evaluation**: check offspring genotype against disease registry for penetrance, onset probability, and fitness cost. 6. **Health score**: computed from cumulative fitness costs of clinical conditions.

Input

  • Two parent `.genome.json` files (one Male, one Female)
  • `GENOMEBOOK/DATA/trait_registry.json`
  • `GENOMEBOOK/DATA/disease_registry.json`

Output

  • Offspring `.genome.json` with:
  • Inherited loci and alleles
  • Mutation log
  • Inferred trait scores
  • Clinical history
  • Health score (0.0 to 1.0)

CLI Usage

# Demo: breed Einstein x Anning, produce 3 offspring
python skills/recombinator/recombinator.py --demo

# Breed specific parents
python skills/recombinator/recombinator.py \
  --father einstein-g0 --mother anning-g0 --offspring 3

# Custom generation number
python skills/recombinator/recombinator.py \
  --father einstein-g0 --mother curie-g0 --offspring 2 --generation 1

Output Format

ID:     g1-001-a3f2c1
Sex:    Female (XX)
Health: 0.9500
Mutations: 1
  - COMT_Val158Met: G->A (neutral, from mother)
Conditions: 0
Top traits:
  - curiosity: 0.92
  - analytical_thinking: 0.88
  - persistence: 0.85
Read more
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