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 nucleotide-footprint-pattern-recognitiongit 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/nucleotide-footprint-pattern-recognition)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/nucleotide-footprint-pattern-recognition"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/nucleotide-footprint-pattern-recognition/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/nucleotide-footprint-pattern-recognition"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/nucleotide-footprint-pattern-recognition.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 | $0.00045 | $0.02092 |
| Opus 5 | $0.00023 | $0.01046 |
| Sonnet 5 | $0.00009 | $0.00418 |
| Haiku 4.5 | $0.00005 | $0.00209 |
Grade A, and why
nucleotide-footprint-pattern-recognition 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 9d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nucleotide-footprint-pattern-recognition
Summary
Identify and quantify transcription factor occupancy by detecting the characteristic depletion of Tn5 insertion signals around protein-bound DNA motifs in ATAC-seq data. This skill reveals footprints—localized regions of accessibility reduction caused by protein binding—which distinguish occupied from unoccupied transcription factor binding sites.
When to use
Apply this skill when you have aligned ATAC-seq BAM files and want to discriminate between transcription factor binding sites that are actually occupied by protein versus sites with matching sequence motifs that are unbound. Use it when your research question requires quantifying the extent of transcription factor occupancy genome-wide or identifying condition-specific changes in binding kinetics across regulatory regions.
When NOT to use
- Input ATAC-seq data is not from bulk chromatin or has insufficient sequencing depth (<10 million reads); low coverage compromises the statistical power to detect footprints. Single-cell data requires aggregation into pseudobulk BAM files per cell cluster first.
- Transcription factor motif coordinates are unavailable or of poor quality; footprinting requires known or predicted binding site locations to anchor the analysis window.
- Your primary goal is to identify novel transcription factor binding sites de novo rather than to quantify occupancy at known motif locations; footprinting detects occupancy signal but does not perform motif discovery.
Inputs
- ATAC-seq aligned reads (BAM format)
- Reference genome sequence (FASTA)
- Transcription factor motif coordinate annotations (BED format with classified bound/unbound status or accessibility signal values for classification)
- Open chromatin peak coordinates (BED format, optional but recommended for ATACorrect)
- Transcription factor position weight matrices or motif annotations
Outputs
- Bias-corrected ATAC-seq cut-site signal (BigWig format)
- Footprint score matrix (insertion counts by genomic position × site class)
- Positional insertion distribution statistics (mean and standard deviation per bin)
- Aggregate footprint visualization (plot showing insertion profiles for bound vs. unbound sites)
- Bound/unbound classification confidence scores or differential binding estimates
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.
- 9d ago First seen · 110 lines · 45 tokens per session scan A 7c5166276e77
nucleotide-footprint-pattern-recognition is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 45 tokens to every session and 2,092 once invoked, about $0.0002 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.
Other skills, from other repositories
gsva-analysis-and-visualization
Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression…
medical-research-literature-reader-pro
A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title.…
adverse-event-narrative
Generates CIOMS I-compliant ICSR narratives from adverse event case data for FDA and EMA regulatory submission. Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.
anatomy-quiz-master
Generate interactive anatomy quizzes for medical education with multiple.
decision-curve-analysis
Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or…
elastic-net-feature-selection
Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification…