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 boundary-detection-thresholdinggit 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/boundary-detection-thresholding)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/boundary-detection-thresholding"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/boundary-detection-thresholding/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/boundary-detection-thresholding"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/boundary-detection-thresholding.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 | $0.00041 | $0.01475 |
| Opus 5 | $0.00020 | $0.00737 |
| Sonnet 5 | $0.00008 | $0.00295 |
| Haiku 4.5 | $0.00004 | $0.00147 |
Grade A, and why
boundary-detection-thresholding 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
boundary-detection-thresholding
Summary
Automated detection of topologically associating domain (TAD) boundaries from Hi-C insulation scores using statistical thresholding (Li or Otsu methods). This skill converts continuous insulation profiles into discrete boundary annotations for characterizing genome compartmentalization.
When to use
When you have computed per-bin insulation scores from a Hi-C cooler file using cooltools.insulation and need to identify discrete genomic boundaries that separate topological domains. Use this skill when insulation scores are available as a numeric array with values corresponding to window-specific measures of local domain insulation, and you need boolean boundary annotations for downstream visualization or structural analysis.
When NOT to use
- Input contact matrix is low-resolution (<10 kb bins) or sparse; insulation scores become unreliable below ~5 kb resolution.
- Hi-C data lacks sufficient sequencing depth or has extreme bias artifacts; thresholding will fail to separate signal from noise.
- Boundaries have already been called by another method and you need only to intersect or validate them; use boundary comparison/overlap tools instead.
Inputs
- cooler file (.cool or .mcool) containing Hi-C contact matrix and bin table
- window size parameter (integer, in base pairs or as number of bins)
- genomic region coordinates (optional, for subsetting analysis)
Outputs
- pandas DataFrame with columns: region1, region2, insulation_score (numeric), is_boundary_{window} (boolean)
- BED format file of detected boundaries (chrom, start, end, is_boundary flag)
- insulation score table exportable as TSV or CSV
How to apply
Apply cooltools.insulation with a specified window size parameter (e.g., 50 kb or 100 kb) to compute insulation scores for each genomic bin. The function simultaneously performs thresholding to detect insulating boundaries using either Li or Otsu automated threshold selection. Li thresholding assumes a bimodal distribution of insulation scores (signal vs. background), while Otsu maximizes between-class variance. The output includes per-bin insulation score values and boolean is_boundary_{window} columns indicating which bins cross the computed threshold. Choose the thresholding method based on your domain's expected boundary prominence: Otsu is more conservative and works well for clear TAD structure, while Li may be more sensitive to subtle boundaries. Validate that output scores fall within expected numeric ranges and boundary annotations are strictly boolean; verify row count matches input bin count.
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 · 98 lines · 41 tokens per session scan A 537ff52aa2bc
boundary-detection-thresholding is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 4d ago), licensed Apache-2.0. It adds 41 tokens to every session and 1,475 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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