Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Personal Genomics
Analyze raw DNA data from consumer genetics services (23andMe, AncestryDNA, etc.). Extract health markers, pharmacogenomics, traits, ancestry composition, ancient DNA comparisons, and generate comprehensive reports. Uses open-source bioinformatics tools locally — no data leaves your machine.
skill openclawclawhub Free
Analyze raw DNA data from consumer genetics services (23andMe, AncestryDNA, etc.). Extract health markers, pharmacogenomics, traits, ancestry composition, ancient DNA comparisons, and generate comprehensive reports. Uses open-source bioinformatics tools locally — no data leaves your machine.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- CHANGELOG.md, README.md, SECURITY.md, SKILL.md, advanced_analysis.py, analyze_dna.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
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. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 4.2.0
Provenance
- Publisher
- wkyleg
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Personal Genomics Skill v4.2.0
Comprehensive local DNA analysis with 1600+ markers across 30 categories. Privacy-first genetic analysis for AI agents.
Quick Start
python comprehensive_analysis.py /path/to/dna_file.txt
Triggers
Activate this skill when user mentions: DNA analysis, genetic analysis, genome analysis 23andMe, AncestryDNA, MyHeritage results Pharmacogenomics, drug-gene interactions Medication interactions, drug safety Genetic risk, disease risk, health risk Carrier status, carrier testing VCF file analysis APOE, MTHFR, CYP2D6, BRCA, or other gene names Polygenic risk scores Haplogroups, maternal lineage, paternal lineage Ancestry composition, ethnicity Hereditary cancer, Lynch syndrome Autoimmune genetics, HLA, celiac Pain sensitivity, opioid response Sleep optimization, chronotype, caffeine metabolism Dietary genetics, lactose intolerance, celiac Athletic genetics, sports performance UV sensitivity, skin type, melanoma risk Telomere length, longevity genetics
Supported Files
23andMe, AncestryDNA, MyHeritage, FTDNA VCF files (whole genome/exome, .vcf or .vcf.gz) Any tab-delimited rsid format
Output Location
~/dna-analysis/reports/ agent_summary.json - AI-optimized, priority-sorted full_analysis.json - Complete data report.txt - Human-readable genetic_report.pdf - Professional PDF report
Haplogroup Analysis
Mitochondrial DNA (mtDNA) - maternal lineage Y-chromosome - paternal lineage (males only) Migration history context PhyloTree/ISOGG standards
Ancestry Composition
Population comparisons (EUR, AFR, EAS, SAS, AMR) Admixture detection Ancestry informative markers
Hereditary Cancer Panel
BRCA1/BRCA2 comprehensive Lynch syndrome (MLH1, MSH2, MSH6, PMS2) Other genes (APC, TP53, CHEK2, PALB2, ATM) ACMG-style classification
Autoimmune HLA
Celiac (DQ2/DQ8) - can rule out if negative Type 1 Diabetes Ankylosing spondylitis (HLA-B27) Rheumatoid arthritis, lupus, MS
Pain Sensitivity
COMT Val158Met OPRM1 opioid receptor SCN9A pain signaling TRPV1 capsaicin sensitivity Migraine susceptibility
PDF Reports
Professional format Physician-shareable Executive summary Detailed findings Disclaimers included
Medication Interaction Checker
from markers.medication_interactions import check_medication_interactions result = check_medication_interactions( medications=["warfarin", "clopidogrel", "omeprazole"], genotypes=user_genotypes ) # Returns critical/serious/moderate interactions with alternatives Accepts brand or generic names CPIC guidelines integrated PubMed citations included FDA warning flags
Sleep Optimization Profile
from markers.sleep_optimization import generate_sleep_profile profile = generate_sleep_profile(genotypes) # Returns ideal wake/sleep times, coffee cutoff, etc. Chronotype (morning/evening preference) Caffeine metabolism speed Personalized timing recommendations
Dietary Interaction Matrix
from markers.dietary_interactions import analyze_dietary_interactions diet = analyze_dietary_interactions(genotypes) # Returns food-specific guidance Caffeine, alcohol, saturated fat, lactose, gluten APOE-specific diet recommendations Bitter taste perception
Athletic Performance Profile
from markers.athletic_profile import calculate_athletic_profile profile = calculate_athletic_profile(genotypes) # Returns power/endurance type, recovery profile, injury risk Sport suitability scoring Training recommendations Injury prevention guidance
UV Sensitivity Calculator
from markers.uv_sensitivity import generate_uv_sensitivity_report uv = generate_uv_sensitivity_report(genotypes) # Returns skin type, SPF recommendation, melanoma risk Fitzpatrick skin type estimation Vitamin D synthesis capacity Melanoma risk factors
Natural Language Explanations
from markers.explanations import generate_plain_english_explanation explanation = generate_plain_english_explanation( rsid="rs3892097", gene="CYP2D6", genotype="GA", trait="Drug metabolism", finding="Poor metabolizer carrier" ) Plain-English summaries Research variant flagging PubMed links
Telomere & Longevity
from markers.advanced_genetics import estimate_telomere_length telomere = estimate_telomere_length(genotypes) # Returns relative estimate with appropriate caveats TERT, TERC, OBFC1 variants Longevity associations (FOXO3, APOE)
Data Quality
Call rate analysis Platform detection Confidence scoring Quality warnings
Export Formats
Genetic counselor clinical export Apple Health compatible API-ready JSON Integration hooks
Marker Categories (21 total)
Pharmacogenomics (159) - Drug metabolism Polygenic Risk Scores (277) - Disease risk Carrier Status (181) - Recessive carriers Health Risks (233) - Disease susceptibility Traits (163) - Physical/behavioral Haplogroups (44) - Lineage markers Ancestry (124) - Population informative Hereditary Cancer (41) - BRCA, Lynch, etc. Autoimmune HLA (31) - HLA associations Pain Sensitivity (20) - Pain/opioid response Rare Diseases (29) - Rare conditions Mental Health (25) - Psychiatric genetics Dermatology (37) - Skin and hair Vision & Hearing (33) - Sensory genetics Fertility (31) - Reproductive health Nutrition (34) - Nutrigenomics Fitness (30) - Athletic performance Neurogenetics (28) - Cognition/behavior Longevity (30) - Aging markers Immunity (43) - HLA and immune Ancestry AIMs (24) - Admixture markers
Agent Integration
The agent_summary.json provides: { "critical_alerts": [], "high_priority": [], "medium_priority": [], "pharmacogenomics_alerts": [], "apoe_status": {}, "polygenic_risk_scores": {}, "haplogroups": { "mtDNA": {"haplogroup": "H", "lineage": "maternal"}, "Y_DNA": {"haplogroup": "R1b", "lineage": "paternal"} }, "ancestry": { "composition": {}, "admixture": {} }, "hereditary_cancer": {}, "autoimmune_risk": {}, "pain_sensitivity": {}, "lifestyle_recommendations": { "diet": [], "exercise": [], "supplements": [], "avoid": [] }, "drug_interaction_matrix": {}, "data_quality": {} }
Pharmacogenomics
DPYD variants - 5-FU/capecitabine FATAL toxicity risk HLA-B*5701 - Abacavir hypersensitivity HLA-B*1502 - Carbamazepine SJS (certain populations) MT-RNR1 - Aminoglycoside-induced deafness
Hereditary Cancer
BRCA1/BRCA2 pathogenic - Breast/ovarian cancer syndrome Lynch syndrome genes - Colorectal/endometrial cancer TP53 pathogenic - Li-Fraumeni syndrome (multi-cancer)
Disease Risk
APOE ε4/ε4 - ~12x Alzheimer's risk Factor V Leiden - Thrombosis risk, contraceptive implications HLA-B27 - Ankylosing spondylitis susceptibility (OR ~70)
Carrier Status
CFTR - Cystic fibrosis (1 in 25 Europeans) HBB - Sickle cell (1 in 12 African Americans) HEXA - Tay-Sachs (1 in 30 Ashkenazi Jews)
Basic Analysis
from comprehensive_analysis import main main() # Uses command line args
Haplogroup Analysis
from markers.haplogroups import analyze_haplogroups result = analyze_haplogroups(genotypes) print(result["mtDNA"]["haplogroup"]) # e.g., "H"
Ancestry
from markers.ancestry_composition import get_ancestry_summary ancestry = get_ancestry_summary(genotypes)
Cancer Panel
from markers.cancer_panel import analyze_cancer_panel cancer = analyze_cancer_panel(genotypes) if cancer["pathogenic_variants"]: print("ALERT: Pathogenic variants detected")
Generate PDF
from pdf_report import generate_pdf_report pdf_path = generate_pdf_report(analysis_results)
Export for Genetic Counselor
from exports import generate_genetic_counselor_export clinical = generate_genetic_counselor_export(results, "clinical.json")
Privacy
All analysis runs locally Zero network requests No data leaves the machine
Limitations
Consumer arrays miss rare variants (~0.1% of genome) Results are probabilistic, not deterministic Not a medical diagnosis Most conditions 50-80% non-genetic Consult healthcare providers for medical decisions Negative hereditary cancer result does NOT rule out cancer syndrome Haplogroup resolution limited without WGS
When to Recommend Genetic Counseling
Any pathogenic hereditary cancer variant APOE ε4/ε4 genotype Multiple critical pharmacogenomic findings Carrier status with reproduction implications High-risk autoimmune HLA types with symptoms Results causing significant user distress
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- CHANGELOG.md
- README.md
- SECURITY.md
- advanced_analysis.py
- analyze_dna.py