Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HolobiomicsLab/asb-skill-collections --skill dna-methylation-file-format-handlinggit clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collectionsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/dna-methylation-file-format-handling)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/dna-methylation-file-format-handling"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-file-format-handling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/dna-methylation-file-format-handling"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-file-format-handling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.01679 |
| Opus 5 | $0.00030 | $0.00839 |
| Sonnet 5 | $0.00012 | $0.00336 |
| Haiku 4.5 | $0.00006 | $0.00168 |
Grade A, and why
dna-methylation-file-format-handling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DNA methylation file format handling
Summary
Configure methylKit's dbtype parameter and tabix-backed storage to load bisulfite sequencing methylation calls into memory-efficient methylRawListDB objects backed by bgzipped indexed files on disk. This skill enables scalable handling of large methylation datasets without loading all data into RAM.
When to use
You have CpG methylation call files (from Bismark or MethylDackel) and need to load them into R for differential methylation analysis, but anticipate memory constraints or want to avoid loading the entire dataset into memory. Use this skill when dataset size makes in-memory storage impractical or when you need fast random access to specific genomic regions without decompressing the entire file.
When NOT to use
- Input files are already in HDF5 or other non-tabix disk-backed format (use appropriate dbtype instead)
- Your workflow requires in-memory operations only and dataset size is manageable in RAM (use default in-memory loading without dbtype parameter)
- Methylation calls are not from bisulfite sequencing or lack standard genomic coordinate format (e.g., from base-calling algorithms with non-standard output)
Inputs
- CpG methylation call files (bedGraph, cov, or Bismark coverage format)
- Sample metadata (group/phenotype assignments for methylation samples)
- Reference genome or annotation (optional, for downstream analysis)
Outputs
- methylRawListDB object with dbpath slots referencing bgzipped tabix files
- Disk-backed methylRaw objects within the list, each pointing to a tabix-indexed file
- Validated tabix file structure with methylKit metadata headers
How to apply
Call the methRead() function with the dbtype='tabix' parameter to create a methylRawListDB object that stores methylation data in bgzipped tabix-indexed files on disk rather than in memory. The resulting methylRawListDB object will have its dbpath slot populated with file paths to the bgzipped tabix files, enabling retrieval from external files. Verify that the tabix file headers contain methylKit metadata and version information (≥ 1.13.1), and inspect the object structure to confirm the dbpath slots reference valid bgzipped tabix file paths. By default, methRead() applies a minimum coverage threshold of 10 reads per base to ensure data quality, which can be adjusted as needed. This configuration allows downstream operations like unite(), calculateDiffMeth(), and getMethylDiff() to work on the database-backed object while accessing data from disk on demand.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 98 lines · 59 tokens per session scan A 3837d95de305
dna-methylation-file-format-handling is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed today), licensed Apache-2.0. It adds 59 tokens to every session and 1,679 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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