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 agentmods add skills/gptomics/bioskills/motif-deviationnpx skills add GPTomics/bioSkills --skill motif-deviationgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/motif-deviation)<a href="https://agentmods.dev/skills/gptomics/bioskills/motif-deviation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/motif-deviation.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00070 | $0.05079 |
| Opus 5 | $0.00035 | $0.02540 |
| Sonnet 5 | $0.00014 | $0.01016 |
| Haiku 4.5 | $0.00007 | $0.00508 |
Grade A, and why
bio-atac-seq-motif-deviation 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-atac-seq-motif-deviation — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: chromVAR 1.24+, motifmatchr 1.24+, JASPAR2024 0.99+, TFBSTools 1.40+, BSgenome.Hsapiens.UCSC.hg38 1.4+, SummarizedExperiment 1.32+, limma 3.58+, ggplot2 3.5+, Matrix 1.6+, ArchR 1.0.2+, Signac 1.13+.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
Motif Deviation (chromVAR)
"Which TF motifs explain accessibility variation across my samples or cells?" -> Compute per-sample (or per-cell) deviation z-scores: how many standard deviations above expectation each TF motif's accessibility falls, controlling for GC content and overall accessibility via matched background peak sets.
- R:
chromVAR::computeDeviations(counts, motifs)-> per-sample z-scores - R:
chromVAR::computeVariability(dev)-> per-motif variance ranking - Single-cell alternative:
Signac::RunChromVAR()(wrapper with matched defaults) orArchR::addDeviationsMatrix()
chromVAR answers a different question than footprinting: footprinting asks "is this specific motif site bound?", chromVAR asks "do peaks containing this motif have systematically more or less accessibility than expected?" The two are complementary.
What chromVAR Computes
For each (motif, sample) pair:
- Raw deviation = Sum of accessibility at peaks containing the motif - expected from a matched-GC, matched-accessibility background.
- Bias-corrected deviation = Raw deviation / SD of background deviations.
- Z-score = (corrected deviation - mean across cells) / SD across cells. Reported as the principal output.
Z-scores are signed: positive = motif more accessible in this sample than population average; negative = less. Magnitudes 2-5 are typical for biologically interesting motifs; >5 indicates strong covariation with sample state.
Algorithmic Taxonomy
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 333 lines · 70 tokens per session scan A de292fdd7cfc
bio-atac-seq-motif-deviation is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 21d ago), licensed MIT. It adds 70 tokens to every session and 5,079 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 2026-08-30.
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