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.

Security Scan

The skill's code and runtime instructions are consistent with its stated purpose (local DNA analysis producing reports), but verify the source and dependency installation before running because of sensitive data and omitted files.

Purpose & Capability

The name/description (personal genomics, local analysis) align with the included Python analysis modules, marker databases, report generation, dashboard, and supported file formats. Required env vars, binaries, and config paths in the registry are empty — consistent with a pure-Python local tool. The README references a GitHub repository (https://github.com/wkyleg/personal-genomics.git) while the registry entry lists source/homepage as unknown/none; that's a minor metadata mismatch but does not contradict the functionality.

Instruction Scope

SKILL.md and code instruct the agent to load local DNA files and write reports to ~/dna-analysis/reports/. The instructions and visible code focus on parsing genotyping files, marker lookup, PRS, haplogroups, and PDF/dashboard generation. I saw no instructions to read unrelated system files or to send data externally. The code references optional local data like AADR (ancient-dna) with explicit instructions for the user to download it separately rather than auto-download.

Install Mechanism

The registry contains no install specification (instruction-only), but README suggests cloning a GitHub repo and pip installing requirements.txt (or using 'clawhub install'). That implies fetching code and Python packages from the network during installation. The skill claims 'zero network requests' at runtime; that claim appears to refer to analysis execution, not to installation. Confirm requirements.txt and pip packages before installing to ensure no unexpected post-install steps or network activity.

Credentials

The skill requests no environment variables or external credentials. It creates and writes reports to a directory under the user's home (~/dna-analysis/reports/) which is expected for this functionality. Because outputs contain highly sensitive genetic/health information, this filesystem access is appropriate but privacy-sensitive — the behavior is proportionate to the stated purpose.

Persistence & Privilege

The skill is not marked 'always: true' and uses default model invocation settings. It does create files under the user's home (reports, dashboard, agent_summary.json) but does not request persistent platform-level privileges or modify other skills. Autonomous invocation is allowed by default on the platform; combine that with sensitive-data access only if the agent is permitted to read user DNA files. No evidence the skill attempts to persist credentials or change other skills' configurations.

Assessment

This package appears to implement local DNA analysis as described, but take these precautions before installing or running it: - Verify the source: README points to a GitHub repo but the registry lists no homepage. If you plan to run it, inspect the full repository (requirements.txt and all source files) yourself or from a trusted reviewer. - Check requirements.txt and installed packages: pip installs will fetch code from the network. Review dependency list for anything unexpected (native binaries, telemtry libraries, or post-install hooks). - Search the code for network/system calls you didn't expect (look for imports or uses of requests, urllib, socket, subprocess, os.system, ftplib, smtplib, or any hardcoded URLs). The truncated files shown do not include network calls, but 67 files were omitted; review those too. - Run in an isolated environment: use a throwaway VM or container and a dedicated Python virtualenv when first executing, especially before feeding real DNA files. - Protect outputs: reports contain extremely sensitive genetic and health data. Ensure the output directory is encrypted or access-controlled, and avoid uploading reports to third-party services. - Consider professional guidance: results are explicitly non-clinical. For medical decisions, consult a clinician/genetic counselor. If you want higher confidence, provide the omitted files or the contents of requirements.txt so I can re-scan for hidden network calls or executables.

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

New v4.0 Features

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

New v4.1.0 Features

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)

1. Pharmacogenomics (159) - Drug metabolism
2. Polygenic Risk Scores (277) - Disease risk
3. Carrier Status (181) - Recessive carriers
4. Health Risks (233) - Disease susceptibility
5. Traits (163) - Physical/behavioral
6. Haplogroups (44) - Lineage markers
7. Ancestry (124) - Population informative
8. Hereditary Cancer (41) - BRCA, Lynch, etc.
9. Autoimmune HLA (31) - HLA associations
10. Pain Sensitivity (20) - Pain/opioid response
11. Rare Diseases (29) - Rare conditions
12. Mental Health (25) - Psychiatric genetics
13. Dermatology (37) - Skin and hair
14. Vision & Hearing (33) - Sensory genetics
15. Fertility (31) - Reproductive health
16. Nutrition (34) - Nutrigenomics
17. Fitness (30) - Athletic performance
18. Neurogenetics (28) - Cognition/behavior
19. Longevity (30) - Aging markers
20. Immunity (43) - HLA and immune
21. 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": {}
}

Critical Findings (Always Alert User)

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)

Usage Examples

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