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 methylkit-database-mode-configurationgit 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/methylkit-database-mode-configuration)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/methylkit-database-mode-configuration"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/methylkit-database-mode-configuration/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/methylkit-database-mode-configuration"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/methylkit-database-mode-configuration.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 | $0.00063 | $0.01731 |
| Opus 5 | $0.00032 | $0.00865 |
| Sonnet 5 | $0.00013 | $0.00346 |
| Haiku 4.5 | $0.00006 | $0.00173 |
Grade A, and why
methylkit-database-mode-configuration 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 6d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
methylkit-database-mode-configuration
Summary
Configure methylKit to store methylation data in tabix-indexed bgzipped files on disk rather than in memory, enabling scalable analysis of large bisulfite sequencing datasets. This skill uses the methRead() function with dbtype='tabix' parameter to create memory-efficient methylRawListDB objects backed by external indexed files.
When to use
When analyzing DNA methylation data from bisulfite sequencing (RRBS, target-capture, or whole-genome) and the dataset is too large to fit comfortably in memory, or when you need to process multiple large samples sequentially without reloading data. Specifically, use this skill when you have CpG methylation call files and want to avoid the memory overhead of storing all methylation data in RAM while preserving fast random access to per-base methylation information.
When NOT to use
- Input data is already loaded as an in-memory methylRawList or methylBase object — direct conversion or in-memory analysis is more efficient.
- Dataset is small enough to fit comfortably in RAM and analysis speed is critical over memory efficiency.
- Bismark or methylation call files are not in a format recognized by methRead() or lack required metadata headers for tabix indexing.
Inputs
- CpG methylation call files (typically in Bismark or similar format)
- dbtype parameter specification ('tabix')
- bgzipped tabix-indexed methylation data files on disk
Outputs
- methylRawListDB object with disk-backed storage
- methylRaw objects with populated dbpath slots referencing external tabix files
- Validated tabix file index structures with methylKit metadata headers
How to apply
Load CpG methylation call files using the methRead() function with the dbtype='tabix' parameter to create a methylRawListDB object where each methylRaw object maintains a dbpath slot referencing bgzipped tabix-indexed files on disk. The tabix files must contain methylKit metadata headers (version ≥ 1.13.1) and be properly indexed. Verify that the resulting object's structure matches methylKit database-mode standards by inspecting the dbpath slot to confirm valid file paths and examining tabix file headers for metadata integrity. This configuration enables data retrieval from external files rather than memory storage, making it suitable for large-scale methylation studies where coverage statistics and methylation percentages are queried selectively rather than loaded entirely into RAM.
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.
- 6d ago First seen · 101 lines · 63 tokens per session scan A 99e4253ea3c2
methylkit-database-mode-configuration is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 4d ago), licensed Apache-2.0. It adds 63 tokens to every session and 1,731 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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