Methylation Cycle Analysis as an Agent Skill

Where this fits

A clinician receives raw genotype data from a patient who suspects MTHFR-related issues but has no lab panel. Manually mapping dozens of SNPs to methylation enzymes and calculating Net Methylation Capacity is error-prone and slow. This skill turns raw 23andMe, ADNTRO, or Ancestry files into an interpreted enzymatic profile, including BH4 axis estimates and compound heterozygosity flags.

Why agents benefit

• Parses common DTC genotype formats directly, so an agent can accept a raw file and skip format-cleaning work.
• Computes Net Methylation Capacity from SNP data, giving a practical single output rather than a table of variants.
• Detects compound heterozygosity in genes like MTHFR and MTRR, which is easy to miss when reading variants independently.
• Reports BH4 axis estimates relevant to neurotransmitter synthesis, connecting genotype to clinical hypotheses.
• Emits both Markdown and JSON, so agents can produce a patient-facing summary and a machine-readable payload for downstream pipelines.

Practical scenarios

Supporting a clinician during MTHFR counseling

A patient with borderline homocysteine has a 23andMe export. The agent runs this skill and returns an enzymatic activity profile, flagging a compound heterozygous MTHFR pattern. The clinician can then counsel folate versus methylfolate supplementation with a concrete basis.

Genomic research on neurotransmitter pathways

A researcher investigating BH4-dependent neurotransmitter synthesis needs to filter cohorts by methylation capacity. The skill provides BH4 axis estimates from raw genotype files, allowing the agent to batch-analyze multiple samples and rank them by predicted enzyme activity.

Building a consumer-facing methylation report

A small DTC genomics company wants to generate a "methylation cycle" section for customers. Instead of maintaining internal pipelines, they wire this skill into their agent workflow to produce Markdown summaries from raw files, then review and export to final reports.

Add it to your agent workflow

Install the skill with:

npx skills add ClawBio/ClawBio --skill claw-methylation-cycle

Then prompt the agent with something like:

Run claw-methylation-cycle on demo_input.txt. Summarize Net Methylation Capacity, BH4 axis estimates, and any compound heterozygosity calls.

The skill handles file parsing and returns a structured markdown/json report. The repo includes demo data, so you can validate output before using real genomes.

Compare before adopting

Before using it, compare it against other methylation/PGx tools on several dimensions. Quality signals: it cites guideline DOI 10.3390/nu13030768 and carries validation_tier: community, so verify clinical interpretation against established protocols. Maintenance freshness: check commits and version 0.1.3 for ongoing updates. Alternatives: general PGx interpretation services or manual CSV processing may cover similar SNPs but often lack Net Methylation Capacity or BH4 estimates. Workflow fit: confirm your agent can satisfy the python3 requirement and that the accepted input formats (23andme, adntro, ancestry) match your data sources.

Why it is worth tracking

The skill has 1,125 GitHub stars and an active ClawBio repository, which signals meaningful community interest. It is tagged for genomics, methylation, and neurodevelopment, so it will likely evolve with clearer clinical guidance and broader format support. If you work with raw consumer genomics data and need a reproducible methylation analysis step, it is worth evaluating now. OpenAgentSkill maintains a curated index of agent skills; see https://www.openagentskill.com/skills/clawbio-claw-methylation-cycle. Track it again when the validation tier moves from community to a reviewed tier or when new genes are added.