methylation-clock
Compute epigenetic age from DNA methylation arrays using PyAging clocks from GEO accessions or local files.
Best use case
methylation-clock is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Compute epigenetic age from DNA methylation arrays using PyAging clocks from GEO accessions or local files.
Teams using methylation-clock 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/methylation-clock/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How methylation-clock Compares
| Feature / Agent | methylation-clock | 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?
Compute epigenetic age from DNA methylation arrays using PyAging clocks from GEO accessions or local files.
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
# Methylation Clock
## Domain Decisions
Epigenetic age workflows are difficult to reproduce because preprocessing and clock inputs differ across tools and publications.
This skill standardizes a PyAging-first pipeline from ingestion to report generation, with explicit reproducibility outputs.
### Core Capabilities
1. Accepts exactly one input source: GEO accession (`--geo-id`) or local methylation file (`--input`).
2. Applies notebook-aligned preprocessing (female derivation and EPICv2 aggregation by default).
3. Converts tabular data to AnnData and runs one or more methylation clocks.
4. Exports predictions, missing-feature diagnostics, metadata, figures, and reproducibility artifacts.
### Input Contract
- Exactly one input source:
- GEO accession with `--geo-id` (example: `GSE139307`)
- Local file with `--input` (`.pkl`, `.pickle`, `.csv`, `.tsv`, `.csv.gz`, `.tsv.gz`)
- Required output directory via `--output`
- Optional clock list via `--clocks`
### Demo And Usage
Demo fixture provenance and checksum are documented in `skills/methylation-clock/data/PROVENANCE.md`.
Install optional methylation-clock dependency (not part of the global base requirements):
```bash
pip install pyaging>=0.1
```
```bash
# Demo
python skills/methylation-clock/methylation_clock.py \
--input skills/methylation-clock/data/GSE139307_small.csv.gz \
--output /tmp/methylation_clock_demo
# GEO input
python skills/methylation-clock/methylation_clock.py \
--geo-id GSE139307 \
--output /tmp/methylation_clock_geo
# Local methylation file
python skills/methylation-clock/methylation_clock.py \
--input my_methylation.pkl \
--clocks Horvath2013,AltumAge,PCGrimAge,GrimAge2,DunedinPACE \
--output /tmp/methylation_clock_local
```
### Output Structure
```
methylation_clock_report/
├── report.md
├── figures/
│ ├── clock_distributions.png
│ └── clock_correlation.png
├── tables/
│ ├── predictions.csv
│ ├── prediction_summary.csv
│ ├── missing_features.csv
│ └── clock_metadata.json
└── reproducibility/
├── commands.sh
├── environment.yml
└── checksums.sha256
```
## Safety Rules
1. ClawBio is local-first: user methylation data must remain on-device.
2. The skill refuses non-empty output directories to avoid silent overwrite.
3. Reports must include this disclaimer: "ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions."
## Agent Boundary
1. Route methylation clock requests to `skills/methylation-clock/methylation_clock.py`.
2. Do not infer clinical diagnosis or treatment from clock estimates.
3. Trigger terms include: epigenetic age, methylation clock, Horvath, GrimAge, DunedinPACE, GEO, GSE.
4. Valid downstream chaining: `rnaseq-de` for transcriptomic-aging contrasts and `equity-scorer` for cohort context.Related Skills
wes-clinical-report-es
Generates professional clinical PDF reports in Spanish from WES (Whole Exome Sequencing) data with clinical interpretation, pharmacogenomic alerts, and follow-up recommendations.
wes-clinical-report-en
Generates professional clinical PDF reports in English from WES (Whole Exome Sequencing) data with clinical interpretation summary, pharmacogenomic alerts, and follow-up recommendations.
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.
variant-annotation
Annotate VCF variants with Ensembl VEP REST, ClinVar significance, gnomAD/population frequency context, and prioritized variant ranking.
ukb-navigator
Semantic search across UK Biobank's 12,000+ data fields and publications — find the right variables for your research question.
target-validation-scorer
Evidence-grounded target validation scoring with GO/NO-GO decisions for drug discovery campaigns
struct-predictor
Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs boltz predict, extracts per-residue pLDDT and PAE confidence, and writes a markdown report with figures.
soul2dna
Compile SOUL.md character profiles into synthetic diploid genomes (.genome.json) via trait-to-allele mapping
seq-wrangler
Sequence QC, alignment, and BAM processing. Wraps FastQC, BWA/Bowtie2, SAMtools for automated read-to-BAM pipelines.
scrna-orchestrator
Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional CellTypist annotation, optional latent downstream mode from integrated.h5ad/X_scvi, and optional dataset-level plus within-cluster contrastive marker analysis from raw-count .h5ad or 10x Matrix Market input.
scrna-embedding
Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
rnaseq-de
Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.