# Send NutriGx Advisor to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "nutrigx-advisor", "name": "NutriGx Advisor", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/manuelcorpas/nutrigx-advisor", "canonicalUrl": "https://clawhub.ai/manuelcorpas/nutrigx-advisor", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/nutrigx-advisor", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=nutrigx-advisor", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "data/snp_panel.json", "examples/generate_patient.py", "examples/output/nutrigx_report.md", "extract_genotypes.py", "generate_report.py" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "nutrigx-advisor", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-02T19:37:46.004Z", "expiresAt": "2026-05-09T19:37:46.004Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=nutrigx-advisor", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=nutrigx-advisor", "contentDisposition": "attachment; filename=\"nutrigx-advisor-0.2.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "nutrigx-advisor" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/nutrigx-advisor" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/nutrigx-advisor", "downloadUrl": "https://openagent3.xyz/downloads/nutrigx-advisor", "agentUrl": "https://openagent3.xyz/skills/nutrigx-advisor/agent", "manifestUrl": "https://openagent3.xyz/skills/nutrigx-advisor/agent.json", "briefUrl": "https://openagent3.xyz/skills/nutrigx-advisor/agent.md" } } ``` ## Documentation ### NutriGx Advisor — 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 — all computed locally. Key outputs Markdown nutrition report with risk scores and recommendations Radar chart of nutrient risk profile Gene × 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 × diet interactionModerate ### Micronutrient Metabolism GeneSNPNutrientEffect of risk alleleMTHFRrs1801133Folate / B12↓ 5-MTHF conversion (~70%)MTHFRrs1801131Folate / B12↓ enzyme activity (~30%)MTRrs1805087B12 / homocysteine↑ homocysteine riskBCMO1rs7501331Beta-carotene → Vitamin A↓ conversion (~50%)BCMO1rs12934922Beta-carotene → Vitamin A↓ conversion (compound het)VDRrs2228570Vitamin D absorption↓ VDR functionVDRrs731236Vitamin D↓ bone mineral density responseGCrs4588Vitamin D binding↑ deficiency riskSLC23A1rs33972313Vitamin C transport↓ renal reabsorptionALPLrs1256335Vitamin B6↓ alkaline phosphatase activity ### Omega-3 / Fatty Acid Metabolism GeneSNPNutrientEffectFADS1rs174546LC-PUFA synthesis↑/↓ EPA/DHA from ALAFADS2rs1535LC-PUFA synthesisModulates omega-6:omega-3 ratioELOVL2rs953413DHA synthesis↓ elongation of EPA→DHAAPOErs429358Saturated fat responseε4 → ↑ LDL-C on high SFA dietAPOErs7412Saturated fat responseCombined with rs429358 for ε 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 SOD↓ mitochondrial antioxidantGPX1rs1050450Selenium / GSH-Px↓ glutathione peroxidaseGSTT1DeletionGlutathione-S-transNull genotype → ↑ oxidative riskNQO1rs1800566Coenzyme Q10↓ CoQ10 regenerationCOMTrs4680Catechol / B vitaminsMet/Val → 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 — homozygous reference (lowest risk) 0.5 — heterozygous 1.0 — homozygous risk allele Composite Nutrient Risk Scores (0–10) 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: 0–3: Low risk — standard dietary advice applies 3–6: Moderate risk — dietary optimisation recommended 6–10: Elevated risk — 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 → interpretation → recommendation Radar chart (matplotlib) of nutrient risk scores Gene × 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 — full CLI to reproduce analysis environment.yml — pinned conda environment checksums.txt — SHA-256 checksums of input and output files provenance.json — 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/ ├── SKILL.md ← this file (agent instructions) ├── nutrigx_advisor.py ← main entry point ├── parse_input.py ← multi-format parser ├── extract_genotypes.py ← SNP lookup engine ├── score_variants.py ← risk scoring algorithm ├── generate_report.py ← Markdown + figures ├── repro_bundle.py ← reproducibility export ├── .gitignore ├── data/ │ └── snp_panel.json ← curated SNP definitions ├── tests/ │ ├── synthetic_patient.csv ← fixed 23andMe-format test data (for pytest) │ └── test_nutrigx.py ← pytest suite └── examples/ ├── generate_patient.py ← random patient generator (demo use) ├── data/ ← generated patient files land here (gitignored) └── output/ ├── nutrigx_report.md ← pre-rendered demo report ├── nutrigx_radar.png ← demo radar chart (nutrient risk profile) └── nutrigx_heatmap.png ← demo gene × 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. Gene–environment 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 — 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 × genotype interaction module (16S rRNA input) v0.3: Longitudinal tracking — 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. ## Trust - Source: tencent - Verification: Indexed source record - Publisher: manuelcorpas - Version: 0.2.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-02T19:37:46.004Z - Expires at: 2026-05-09T19:37:46.004Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/nutrigx-advisor) - [Send to Agent page](https://openagent3.xyz/skills/nutrigx-advisor/agent) - [JSON manifest](https://openagent3.xyz/skills/nutrigx-advisor/agent.json) - [Markdown brief](https://openagent3.xyz/skills/nutrigx-advisor/agent.md) - [Download page](https://openagent3.xyz/downloads/nutrigx-advisor)