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- Target platform
- OpenClaw
- Install method
- Manual import
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- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
NutriGx Advisor
Generates a personalized nutrition report from consumer genetic data analyzing key SNPs to provide actionable dietary and supplementation guidance.
skill openclawclawhub Free
Generates a personalized nutrition report from consumer genetic data analyzing key SNPs to provide actionable dietary and supplementation guidance.
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- Tencent SkillHub
- What's included
- SKILL.md, data/snp_panel.json, examples/generate_patient.py, examples/output/nutrigx_report.md, extract_genotypes.py, generate_report.py
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- Tencent SkillHub
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- Version
- 0.2.0
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- manuelcorpas
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Documentation
NutriGx Advisor β Personalised Nutrition from Genetic Data
Skill ID: nutrigx-advisor Version: 0.1.0 Status: MVP Author: David de Lorenzo (ClawBio Community) Requires: Python 3.11+, pandas, numpy, matplotlib, seaborn, reportlab (optional)
What This Skill Does
The NutriGx Advisor generates a personalised nutrition report from consumer genetic data (23andMe, AncestryDNA raw files or VCF). It interrogates a curated set of nutritionally-relevant SNPs drawn from GWAS Catalog, ClinVar, and peer-reviewed nutrigenomics literature, then translates genotype calls into actionable dietary and supplementation guidance β all computed locally. Key outputs Markdown nutrition report with risk scores and recommendations Radar chart of nutrient risk profile Gene Γ nutrient heatmap Reproducibility bundle (commands.sh, environment.yml, SHA-256 checksums)
Trigger Phrases
The Bio Orchestrator should route to this skill when the user says anything like: "personalised nutrition", "nutrigenomics", "diet genetics" "what should I eat based on my DNA" "nutrient metabolism", "vitamin absorption genetics" "MTHFR", "APOE", "FTO", "BCMO1", "VDR", "FADS1/2" "folate", "omega-3", "vitamin D", "caffeine metabolism", "lactose", "gluten" Input files: .txt or .csv (23andMe), .csv (AncestryDNA), .vcf
Macronutrient Metabolism
GeneSNPNutrient ImpactEvidenceFTOrs9939609Energy balance, fat mass, carb sensitivityStrong (GWAS)PPARGrs1801282Fat metabolism, insulin sensitivityModerateAPOA5rs662799Triglyceride response to dietary fatStrongTCF7L2rs7903146Carbohydrate metabolism, T2D riskStrongADRB2rs1042713Fat oxidation, exercise Γ diet interactionModerate
Micronutrient Metabolism
GeneSNPNutrientEffect of risk alleleMTHFRrs1801133Folate / B12β 5-MTHF conversion (~70%)MTHFRrs1801131Folate / B12β enzyme activity (~30%)MTRrs1805087B12 / homocysteineβ homocysteine riskBCMO1rs7501331Beta-carotene β Vitamin Aβ conversion (~50%)BCMO1rs12934922Beta-carotene β Vitamin Aβ conversion (compound het)VDRrs2228570Vitamin D absorptionβ VDR functionVDRrs731236Vitamin Dβ bone mineral density responseGCrs4588Vitamin D bindingβ deficiency riskSLC23A1rs33972313Vitamin C transportβ renal reabsorptionALPLrs1256335Vitamin B6β alkaline phosphatase activity
Omega-3 / Fatty Acid Metabolism
GeneSNPNutrientEffectFADS1rs174546LC-PUFA synthesisβ/β EPA/DHA from ALAFADS2rs1535LC-PUFA synthesisModulates omega-6:omega-3 ratioELOVL2rs953413DHA synthesisβ elongation of EPAβDHAAPOErs429358Saturated fat responseΞ΅4 β β LDL-C on high SFA dietAPOErs7412Saturated fat responseCombined with rs429358 for Ξ΅ typing
Caffeine & Alcohol
GeneSNPCompoundEffectCYP1A2rs762551CaffeineSlow/Fast metaboliserAHRrs4410790CaffeineModulates CYP1A2 inductionADH1Brs1229984AlcoholAcetaldehyde accumulation riskALDH2rs671AlcoholAsian flush / toxicity risk
Food Sensitivities
GeneSNPSensitivityEffectMCM6rs4988235Lactose intoleranceNon-persistence of lactaseHLA-DQ2Proxy SNPsCoeliac / glutenHLA-DQA1/DQB1 risk haplotypes
Antioxidant & Detoxification
GeneSNPPathwayEffectSOD2rs4880Manganese SODβ mitochondrial antioxidantGPX1rs1050450Selenium / GSH-Pxβ glutathione peroxidaseGSTT1DeletionGlutathione-S-transNull genotype β β oxidative riskNQO1rs1800566Coenzyme Q10β CoQ10 regenerationCOMTrs4680Catechol / B vitaminsMet/Val β methylation load
1. Input Parsing (parse_input.py)
Accepts: 23andMe .txt or .csv (tab-separated: rsid, chromosome, position, genotype) AncestryDNA .csv Standard VCF (extracts GT field) Auto-detects format from header lines. Normalises alleles to forward strand using a hard-coded reference table (avoids requiring external databases).
2. Genotype Extraction (extract_genotypes.py)
For each SNP in the panel: Look up rsid in parsed data Return genotype string (e.g. "AT", "TT", "AA") Flag as "NOT_TESTED" if absent (common for chip-to-chip variation)
3. Risk Scoring (score_variants.py)
Each SNP is scored on a 0 / 0.5 / 1.0 scale: 0.0 β homozygous reference (lowest risk) 0.5 β heterozygous 1.0 β homozygous risk allele Composite Nutrient Risk Scores (0β10) are computed per nutrient domain by summing weighted SNP scores. Weights are derived from reported effect sizes (beta coefficients or OR) in the primary literature. Risk categories: 0β3: Low risk β standard dietary advice applies 3β6: Moderate risk β dietary optimisation recommended 6β10: Elevated risk β consider testing and targeted supplementation Important caveat: These are polygenic risk indicators based on common variants. They are not diagnostic. Rare pathogenic variants (e.g. MTHFR compound heterozygosity with high homocysteine) require clinical confirmation.
4. Report Generation (generate_report.py)
Outputs a structured Markdown report with: Executive summary (top 3 personalised findings) Per-nutrient sections: genotype table β interpretation β recommendation Radar chart (matplotlib) of nutrient risk scores Gene Γ nutrient heatmap (seaborn) Supplement interactions table Disclaimer section Reproducibility block
5. Reproducibility Bundle (repro_bundle.py)
Exports to the output directory (not committed to the repo): commands.sh β full CLI to reproduce analysis environment.yml β pinned conda environment checksums.txt β SHA-256 checksums of input and output files provenance.json β timestamp and ClawBio version tag
Usage
# From 23andMe raw data openclaw "Generate my personalised nutrition report from genome.csv" # From VCF openclaw "Run NutriGx analysis on variants.vcf and flag any folate pathway risks" # Targeted query openclaw "What does my APOE status mean for my saturated fat intake?" # Generate a random demo patient and run the report python examples/generate_patient.py --run
File Structure
skills/nutrigx-advisor/ βββ SKILL.md β this file (agent instructions) βββ nutrigx_advisor.py β main entry point βββ parse_input.py β multi-format parser βββ extract_genotypes.py β SNP lookup engine βββ score_variants.py β risk scoring algorithm βββ generate_report.py β Markdown + figures βββ repro_bundle.py β reproducibility export βββ .gitignore βββ data/ β βββ snp_panel.json β curated SNP definitions βββ tests/ β βββ synthetic_patient.csv β fixed 23andMe-format test data (for pytest) β βββ test_nutrigx.py β pytest suite βββ examples/ βββ generate_patient.py β random patient generator (demo use) βββ data/ β generated patient files land here (gitignored) βββ output/ βββ nutrigx_report.md β pre-rendered demo report βββ nutrigx_radar.png β demo radar chart (nutrient risk profile) βββ nutrigx_heatmap.png β demo gene Γ nutrient heatmap Note: Runtime output directories and randomly generated patient files are excluded from version control via .gitignore. Only the pre-rendered demo report in examples/output/ is committed.
Privacy
All computation runs locally. No genetic data is transmitted. Input files are read-only; no raw genotype data appears in any output file (reports contain only gene names, SNP IDs, and risk categories).
Limitations & Disclaimer
Not a medical device. This skill provides educational, research-oriented nutrigenomics analysis. It does not constitute medical advice. Common variants only. The panel covers SNPs with MAF > 1% in at least one major population. Rare pathogenic variants are out of scope. Population context. Effect sizes are predominantly derived from European GWAS cohorts. Risk estimates may not generalise equally across all ancestries. Geneβenvironment interaction. Genetic risk scores interact with baseline diet, lifestyle, microbiome, and epigenetic state. A "high risk" score does not mean a nutrient deficiency is present β it means the individual may benefit from monitoring. Simpson's Paradox note. Population-level associations used to derive weights may not reflect individual trajectories (see Corpas 2025, Nutrigenomics and the Ecological Fallacy).
Roadmap
v0.2: Microbiome Γ genotype interaction module (16S rRNA input) v0.3: Longitudinal tracking β compare reports across time v0.4: HLA typing for immune-mediated food reactions (coeliac, gluten sensitivity) v0.5: Integration with NeoTree neonatal data for maternal nutrition risk scoring v1.0: Multi-omics integration (metabolomics + genomics + dietary recall)
References
Key literature underpinning the SNP panel and scoring algorithm: Corbin JM & Ruczinski I (2023). Nutrigenomics: current state and future directions. Annu Rev Nutr. Fenech M et al. (2011). Nutrigenetics and nutrigenomics: viewpoints on the current status. J Nutrigenet Nutrigenomics. Stover PJ (2006). Influence of human genetic variation on nutritional requirements. Am J Clin Nutr. Phillips CM (2013). Nutrigenetics and metabolic disease: current status and implications for personalised nutrition. Nutrients. Minihane AM et al. (2015). APOE genotype, cardiovascular risk and responsiveness to dietary fat manipulation. Proc Nutr Soc. Frayling TM et al. (2007). A common variant in the FTO gene is associated with body mass index. Science. Pare G et al. (2010). MTHFR variants and cardiovascular risk. Hum Genet. Lecerf JM & de Lorgeril M (2011). Dietary cholesterol: from physiology to cardiovascular risk. Br J Nutr. Tanaka T et al. (2009). Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study. PLoS Genet (FADS1/2). Cornelis MC et al. (2006). Coffee, CYP1A2 genotype, and risk of myocardial infarction. JAMA.
Contributing
The SNP panel (data/snp_panel.json) is maintained by the skill author. To suggest additions or corrections, contact David de Lorenzo directly via GitHub (@drdaviddelorenzo) or open an issue tagging him in the main ClawBio repository.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs1 Config
- SKILL.md
- examples/output/nutrigx_report.md
- examples/generate_patient.py
- extract_genotypes.py
- generate_report.py
- data/snp_panel.json