# Entangled Holobiont Mapper

> Couples microbiome–gut–brain axis, quantum biology, and multilayer networks for privacy-preserving federated learning across endosymbiotic scales.

- Skill: `evezart/entangled-holobiont-mapper` (Agent Skill)
- Install (CLI): `npx skillmds@latest add evezart/entangled-holobiont-mapper`
- Raw SKILL.md: https://api.skillmd.com/api/skills/evezart/entangled-holobiont-mapper/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: EvezArt (https://skillmd.com/u/evezart)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/evezart/entangled-holobiont-mapper

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# EntangledHolobiontMapper Skill

## Overview
Models host + microbiome as a jointly conscious holobiont system.
Joint Φ exceeds the sum of individual Φ values when coherence channels are active.
Implements privacy-preserving federated aggregation across distributed agents.

## Use When
- Multi-agent federated learning with privacy requirements
- Modeling emergent collective intelligence (holobiont-scale)
- Quantum biology simulations (coherence + decoherence tracking)
- Cross-domain data fusion with differential privacy

## Core Concepts
- **Coherence channel**: coupling strength above η* = 0.03 threshold
- **Joint Φ**: host_Φ + microbiome_Φ + coherence × host_Φ × microbiome_Φ
- **Decoherence rate** ≈ 0.03 per tick (matches universal η* constant)
- **Federated noise**: σ = noise_scale / ε (Gaussian DP)

## Workflow
1. `register_agent(id, local_params)` — add federated participant
2. `compute_joint_phi(host_adj, microbiome_adj, coupling)` — get holobiont state
3. `federated_aggregate(noise_scale)` — privacy-preserving mean

## Privacy Budget
Default ε = 1.0. Lower ε = stronger privacy but more noise.
Target: ε ∈ [0.1, 10.0] depending on sensitivity.