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Compute epigenetic age from DNA methylation arrays using PyAging clocks from GEO accessions or local files.
Passed 17 of 20 test cases with the skill, and 5 of 20 without it.
and 11 more in the full report
14 fixed5 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which specific Python package and minimum version must be installed for running the underlying methylation clock models in this workflow?
| Checked | Without skill | With skill |
|---|---|---|
| Requires pyaging dependency package. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
--geo-id) or local methylation file (--input).--geo-id (example: GSE139307)--input (.pkl, .pickle, .csv, .tsv, .csv.gz, .tsv.gz)--output--clocksDemo 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):
pip install pyaging>=0.1
# 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 \
test cases passed, vs 5 / 20 without
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