NutriGx Advisor — Personalised Nutrition from Genetic Data

To generate a personalised nutrition report from 23andMe raw data, you can use this Skill to automatically extract genotypes, normalize strand calls, and compute weighted SNP risk scores. It processes the file locally and outputs a structured Markdown report.

Yes, you can use VCF files for nutrigenomics risk scoring. The Skill accepts VCF inputs alongside 23andMe and AncestryDNA formats, automatically detecting the format to extract genotypes and apply forward-strand normalization.

The best way to map consumer genetic data to dietary recommendations is by using a curated nutrigenomics SNP panel with per-SNP weights. This approach computes domain risk scores and translates them into actionable, evidence-linked guidance.

Yes, local-only genetic data analysis keeps your raw consumer DNA files entirely on your computer. This Skill satisfies local-only execution without external data transmission, ensuring privacy while generating reports.

Your consumer genetic file analysis lacks reproducibility artifacts if the tool does not generate provenance bundles. This Skill resolves that by outputting commands.sh, environment.yml, and checksums for deterministic reproducibility.

Yes, you can generate radar and heatmap figures from AncestryDNA genotype lists. The Skill uses optional matplotlib and seaborn to produce gene × nutrient heatmaps and radar charts from the computed risk scores.