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 hi-c-insulation-score-calculationgit 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/hi-c-insulation-score-calculation)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/hi-c-insulation-score-calculation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/hi-c-insulation-score-calculation/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/hi-c-insulation-score-calculation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/hi-c-insulation-score-calculation.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 | $0.00061 | $0.01544 |
| Opus 5 | $0.00030 | $0.00772 |
| Sonnet 5 | $0.00012 | $0.00309 |
| Haiku 4.5 | $0.00006 | $0.00154 |
Grade A, and why
hi-c-insulation-score-calculation 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
hi-c-insulation-score-calculation
Summary
Compute per-bin insulation scores and detect topologically associating domain (TAD) boundaries from high-resolution Hi-C cooler files using cooltools.insulation with a specified window size parameter. This quantitative measure identifies genomic regions of local sequence isolation based on contact frequency asymmetry.
When to use
You have a cooler-format Hi-C contact matrix and need to identify TAD boundaries and insulation strength along the genome. Use this skill when your research question requires quantifying local chromatin compartmentalization or annotating structural domain edges for downstream analysis (e.g., correlation with epigenetic marks, structural variant detection, or 3D model parameterization).
When NOT to use
- Input contact matrix is sparse, low-resolution (>100 kb bins), or lacks sufficient read depth; insulation scores become unreliable when bin-pair contact counts are near zero.
- You need to preserve raw contact values unchanged; insulation score computation requires access to off-diagonal contact patterns and may require preliminary normalization or filtering of bad bins.
- Your Hi-C data are already annotated with validated TAD calls from orthogonal methods (e.g., directionality index, chromatin immunoprecipitation); recomputing insulation scores may introduce inconsistent boundary definitions.
Inputs
- cooler file (.cool or .mcool format) containing normalized Hi-C contact matrix
- window size parameter (numeric; typical range 2–10 Mb depending on resolution and biology)
Outputs
- pandas DataFrame with columns: region1, region2, insulation_score (numeric), is_boundary_{window} (boolean)
- BED-format file of called TAD boundaries with genomic coordinates
How to apply
Load a cooler file using the cooler Python API, then apply cooltools.insulation with a window size parameter (e.g., 2–10 Mb) to compute per-bin insulation scores measuring contact asymmetry across diagonal bands. The function outputs a pandas DataFrame with bin coordinates and numeric insulation_score values. Apply either Li or Otsu thresholding to convert scores into boolean boundary annotations (is_boundary_* columns). Validate that output scores fall within expected numeric ranges (typically 0–1 or unbounded depending on normalization) and that boundary flags are strictly boolean. Convert the annotated boundaries to BED format for integration with external visualization and genomic feature annotation tools.
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 · 96 lines · 61 tokens per session scan A 8c516b71cf65
hi-c-insulation-score-calculation is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 61 tokens to every session and 1,544 once invoked, about $0.0003 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.
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