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 differential-tf-occupancy-analysisgit 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/differential-tf-occupancy-analysis)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/differential-tf-occupancy-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/differential-tf-occupancy-analysis/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/differential-tf-occupancy-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/differential-tf-occupancy-analysis.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 | $0.00033 | $0.01874 |
| Opus 5 | $0.00016 | $0.00937 |
| Sonnet 5 | $0.00007 | $0.00375 |
| Haiku 4.5 | $0.00003 | $0.00187 |
Grade A, and why
differential-tf-occupancy-analysis 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
differential-tf-occupancy-analysis
Summary
Identify transcription factors with significantly altered binding occupancy between two ATAC-seq conditions by correcting for Tn5 insertion bias, computing footprint enrichment scores, and performing differential binding detection at known transcription factor binding sites (TFBS). This skill leverages the visible depletion of Tn5 insertions around protein-bound sites (footprints) to quantify condition-specific changes in TF occupancy.
When to use
You have aligned ATAC-seq BAM files and peak annotations from two or more experimental conditions (e.g., treated vs. control, different timepoints, or different cell states) and want to discover which transcription factors show statistically significant changes in chromatin binding occupancy between those conditions, not just differences in open chromatin accessibility.
When NOT to use
- Input is single-condition ATAC-seq data with no biological replicate or comparison group — differential analysis requires at least two conditions.
- The ATAC-seq peaks were generated from single-cell clusters without adequate pseudobulk aggregation; TOBIAS requires sufficient sequencing depth per condition to detect footprints reliably.
- You are interested only in differences in open chromatin peaks, not in transcription factor binding occupancy; standard peak-calling and differential accessibility tools (e.g., DESeq2 on peak counts) are more appropriate.
Inputs
- BAM files (aligned ATAC-seq reads, one per condition)
- Peak annotations in BED format (open chromatin regions)
- Reference genome FASTA file
- Motif database (JASPAR, HOCOMOCO, or similar format supported by TOBIAS)
Outputs
- Uncorrected and bias-corrected BigWig files (.bw) of Tn5 insertion signal per condition
- Footprint score BigWig files (.bw) per condition
- BINDetect differential occupancy results table (.tsv) with TF names, footprint scores, fold-changes, and statistical significance metrics (p-values, adjusted p-values)
- PDF or PNG summary visualizations showing top differential TFs and representative footprint patterns
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 · 103 lines · 33 tokens per session scan A b1b0c73a11a1
differential-tf-occupancy-analysis is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 1,874 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
external-model-validation
Use when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk distribution plots, heatmap, and time-dependent ROC curves. NOT for: model training, feature selection, nomogram construction, calibration analysis…
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…