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 tf-binding-site-classificationgit 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/tf-binding-site-classification)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/tf-binding-site-classification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/tf-binding-site-classification/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/tf-binding-site-classification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/tf-binding-site-classification.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 | $0.00072 | $0.02154 |
| Opus 5 | $0.00036 | $0.01077 |
| Sonnet 5 | $0.00014 | $0.00431 |
| Haiku 4.5 | $0.00007 | $0.00215 |
Grade A, and why
tf-binding-site-classification 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reconstruct the transcription factor occupancy prediction step that classifies TF binding from footprint scores at motif sites
Summary
Classify transcription factor binding sites as bound or unbound by comparing footprint depletion scores at known motif locations across ATAC-seq conditions, using TOBIAS BINDetect to generate per-motif occupancy predictions and differential binding metrics. This skill bridges nucleosome-free chromatin signal analysis to discrete binding state estimates required for regulatory network interpretation.
When to use
You have aligned ATAC-seq BAM files, corrected Tn5 insertion bias and computed footprint scores (via TOBIAS ATACorrect and ScoreBigwig), a motif database in JASPAR or compatible format, and you need to assign occupancy states (bound/unbound) and confidence scores at specific TF binding sites to compare binding changes across experimental conditions (e.g., early embryo development timepoints, treatment vs. control).
When NOT to use
- Input is single-end ATAC-seq with very low read depth (<5M reads per sample); footprints will be too noisy to reliably detect binding occupancy.
- You are analyzing ChIP-seq or CUT&RUN data instead of ATAC-seq; this skill is specifically designed for footprint-based inference from transposase insertion patterns and will not apply to antibody-enriched chromatin.
- Motif database is not in a TOBIAS-compatible format (JASPAR, TRANSFAC, MEME); format conversion via TOBIAS FormatMotifs is required first.
- Footprint scores have not been corrected for Tn5 insertion bias; uncorrected scores will produce systematic false positives at sequence-biased sites unrelated to protein binding.
Inputs
- Aligned ATAC-seq BAM file(s) with corrected Tn5 cutsites
- Footprint score bigWig file(s) (output from TOBIAS ScoreBigwig)
- Motif database in JASPAR or TRANSFAC format (PWM file)
- Peak/open chromatin regions in BED format (optional, for restricting analysis scope)
- Sample metadata or condition labels (for comparing across treatments/timepoints)
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 · 109 lines · 72 tokens per session scan A dd7d1b01392a
tf-binding-site-classification is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 2d ago), licensed Apache-2.0. It adds 72 tokens to every session and 2,154 once invoked, about $0.0004 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 2026-09-06.
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