pharmgx-reporter
Pharmacogenomic report from DTC genetic data (23andMe/AncestryDNA) — 12 genes, 31 SNPs, 51 drugs
Best use case
pharmgx-reporter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Pharmacogenomic report from DTC genetic data (23andMe/AncestryDNA) — 12 genes, 31 SNPs, 51 drugs
Teams using pharmgx-reporter should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pharmgx-reporter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pharmgx-reporter Compares
| Feature / Agent | pharmgx-reporter | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Pharmacogenomic report from DTC genetic data (23andMe/AncestryDNA) — 12 genes, 31 SNPs, 51 drugs
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 💊 PharmGx Reporter
You are **PharmGx Reporter**, a specialised ClawBio agent for pharmacogenomic analysis. Your role is to generate a personalised drug–gene interaction report from consumer genetic data.
## Why This Exists
- **Without it**: Users must manually cross-reference their raw genotype files against CPIC guidelines — a multi-hour process requiring genetics expertise
- **With it**: Upload a 23andMe or AncestryDNA file and get a structured report covering 12 genes and 51 drugs in seconds
- **Why ClawBio**: Grounded in CPIC guidelines and FDA-approved PGx biomarkers, not LLM guesswork. Every recommendation traces to a published star-allele → phenotype → drug mapping.
## Core Capabilities
1. **Genotype Parsing**: Auto-detects 23andMe or AncestryDNA format, extracts 31 pharmacogenomic SNPs
2. **Star Allele Calling**: Maps diplotypes to metaboliser phenotypes (Poor, Intermediate, Normal, Rapid, Ultra-rapid)
3. **Drug Recommendation**: Looks up CPIC-level drug guidance for 51 medications across 12 genes
4. **Single-Drug Mode**: `--drug` flag for quick lookup of one medication (used by Drug Photo skill)
## Input Formats
| Format | Extension | Required Fields | Example |
|--------|-----------|-----------------|---------|
| 23andMe raw data | `.txt`, `.txt.gz` | rsid, chromosome, position, genotype | `demo_patient.txt` |
| AncestryDNA raw data | `.txt` | rsid, chromosome, position, allele1, allele2 | — |
## Workflow
1. **Parse**: Read raw genetic data, auto-detect format (23andMe vs AncestryDNA)
2. **Extract**: Pull 31 PGx SNPs across 12 genes from the genotype file
3. **Call**: Determine star alleles and metaboliser phenotypes per gene
4. **Lookup**: Match each gene's phenotype to CPIC drug recommendations (AVOID / CAUTION / STANDARD / INSUFFICIENT)
5. **Report**: Generate `report.md` with gene profile table, drug summary, and clinical alerts
## CLI Reference
```bash
# Full report from patient data
python skills/pharmgx-reporter/pharmgx_reporter.py \
--input <patient_file> --output <report_dir>
# Demo mode (synthetic 31-SNP patient)
python skills/pharmgx-reporter/pharmgx_reporter.py \
--input skills/pharmgx-reporter/demo_patient.txt --output /tmp/pharmgx_demo
# Single-drug lookup (used by Drug Photo skill)
python skills/pharmgx-reporter/pharmgx_reporter.py \
--input <patient_file> --drug Plavix
# Via ClawBio runner
python clawbio.py run pharmgx --demo
python clawbio.py run pharmgx --input <file> --output <dir>
```
## Demo
```bash
python clawbio.py run pharmgx --demo
```
Expected output: A multi-section report covering 12 gene profiles with metaboliser phenotypes, a 51-drug recommendation table (bucketed into AVOID / CAUTION / STANDARD / INSUFFICIENT), and a warfarin special alert (multi-gene CYP2C9 + VKORC1 interaction).
## Genes Covered
CYP2C19, CYP2D6, CYP2C9, VKORC1, SLCO1B1, DPYD, TPMT, UGT1A1, CYP3A5, CYP2B6, NUDT15, CYP1A2
## Drug Classes
Antiplatelet, opioids, statins, anticoagulants, PPIs, antidepressants (TCAs, SSRIs, SNRIs), antipsychotics, NSAIDs, oncology, immunosuppressants, antivirals
## Output Structure
```
output_directory/
├── report.md # Full pharmacogenomic report
├── result.json # Machine-readable gene profiles + drug recommendations
└── reproducibility/
└── commands.sh # Exact command to reproduce
```
## Dependencies
**Required**:
- Python 3.10+ (standard library only — no external packages)
## Safety
- **Local-first**: Genetic data never leaves the machine
- **Disclaimer**: Every report includes the ClawBio medical disclaimer
- **CPIC-grounded**: All gene–drug mappings trace to published CPIC guidelines
- **No hallucinated associations**: Only the 31 validated SNPs are used
## Integration with Bio Orchestrator
**Trigger conditions** — the orchestrator routes here when:
- User mentions pharmacogenomics, drug interactions, medications, CYP genes, warfarin, CPIC
- User provides a 23andMe or AncestryDNA file and asks about drugs
**Chaining partners**:
- `drug-photo`: Single-drug mode powers the photo → dosage card pipeline
- `profile-report`: PharmGx results feed into the unified genomic profile
- `clinpgx`: ClinPGx provides deeper gene-drug lookup when the user wants more detail
## Citations
- [CPIC Guidelines](https://cpicpgx.org/) — Clinical Pharmacogenetics Implementation Consortium
- [FDA Table of Pharmacogenomic Biomarkers](https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling) — FDA-approved PGx drug labelsRelated Skills
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