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 atac-seq-clustering-performance-interpretationgit 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/atac-seq-clustering-performance-interpretation)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/atac-seq-clustering-performance-interpretation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/atac-seq-clustering-performance-interpretation/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/atac-seq-clustering-performance-interpretation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/atac-seq-clustering-performance-interpretation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00045 | $0.01724 |
| Opus 5 | $0.00023 | $0.00862 |
| Sonnet 5 | $0.00009 | $0.00345 |
| Haiku 4.5 | $0.00005 | $0.00172 |
Grade A, and why
atac-seq-clustering-performance-interpretation 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 10d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
atac-seq-clustering-performance-interpretation
Summary
Systematically extract, tabulate, and interpret clustering performance metrics (NMI, ARI, purity) from single-cell ATAC-seq benchmark studies to compare method performance across datasets. This skill enables practitioners to contextualize the relative strengths of dimensionality reduction + clustering pipelines (e.g., kmers+PCA vs. SnapATAC) against published benchmarks.
When to use
When you need to assess whether a given ATAC-seq clustering method (or variant) is competitive on your data or when evaluating which published method to adopt. Specifically, apply this skill when you have access to published benchmark results (preprint or paper) that report clustering accuracy scores (NMI, ARI, purity) across multiple datasets, and you want to extract, standardize, and rank those metrics to determine which method or configuration (e.g., motif type, dimensionality reduction strategy) achieves best clustering performance.
When NOT to use
- Your goal is motif discovery or TF binding annotation rather than cell clustering—chromVAR is complementary to clustering and better suited for annotating TF motif usage in cells and clusters.
- You are comparing methods on a dataset not included in the published benchmark—extrapolation beyond reported datasets requires additional validation.
- Clustering metrics are unavailable or unreported in your source literature—this skill requires access to published quantitative benchmarks, not qualitative claims.
Inputs
- Published benchmark tables or supplementary data reporting clustering accuracy metrics (NMI, ARI, purity) from single-cell ATAC-seq methods
- Method names and variant specifications (e.g., 'chromVAR kmers + PCA', 'chromVAR motifs + PCA', 'SnapATAC')
- Dataset identifiers and their associated accuracy scores
Outputs
- TSV or CSV table with methods as rows, datasets as columns, and clustering accuracy scores as cell values
- Summary statistics table (mean, median, rank per method across datasets)
- Comparative analysis document stating which method/variant achieves best clustering performance and quantifying performance gaps
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.
- 10d ago First seen · 95 lines · 45 tokens per session scan A 0738e25f9c17
atac-seq-clustering-performance-interpretation is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 5d ago), licensed Apache-2.0. It adds 45 tokens to every session and 1,724 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 2026-08-30.
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