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 contact-distance-binning-logarithmicgit 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/contact-distance-binning-logarithmic)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/contact-distance-binning-logarithmic"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/contact-distance-binning-logarithmic/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/contact-distance-binning-logarithmic"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/contact-distance-binning-logarithmic.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 | $0.00053 | $0.01309 |
| Opus 5 | $0.00026 | $0.00655 |
| Sonnet 5 | $0.00011 | $0.00262 |
| Haiku 4.5 | $0.00005 | $0.00131 |
Grade A, and why
contact-distance-binning-logarithmic 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 11d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
contact-distance-binning-logarithmic
Summary
Apply logarithmic binning to genomic distance values in a precomputed contact frequency table, grouping distances into log-spaced bins and smoothing contact frequencies within each bin to produce a log-binned P(s) contact probability curve. This skill is essential for compressing high-resolution Hi-C contact data into a manageable number of distance-dependent bins suitable for downstream analysis.
When to use
You have a precomputed expected contact frequency table (TSV format with columns: dist_bp, contact_frequency, n_valid) derived from cooler files and need to compress distance-dependent contact probabilities into log-spaced bins. This is necessary when you want to plot or analyze the P(s) curve (contact probability as a function of genomic distance) with reduced noise and improved interpretability, particularly when the raw frequency table spans multiple orders of magnitude in genomic distance.
When NOT to use
- The input is already a log-binned contact probability table or does not require smoothing.
- You need per-bin contact counts for individual genomic regions rather than genome-wide expected contact frequency.
- The precomputed expected table is in a non-standard format lacking the required dist_bp and contact_frequency columns.
Inputs
- precomputed expected_cis table (TSV format with columns: dist_bp, contact_frequency, n_valid)
Outputs
- log-binned and smoothed P(s) table (TSV with columns: dist_bp, count.avg.smoothed, and bin statistics)
How to apply
Load the precomputed expected_cis table (TSV) containing dist_bp, contact_frequency, and n_valid columns. Apply the cooltools logbin_expected function, which performs logarithmic binning by grouping distance values into log-spaced bins and applies an integrated smoothing algorithm to each bin's contact frequencies. The function computes the mean smoothed contact frequency for each log bin, outputting a reduced table with binned distance (dist_bp), mean smoothed contact frequency (count.avg.smoothed), and associated bin statistics. Save the output as a TSV file with appropriate numeric precision. The logarithmic binning is motivated by the observation that Hi-C contact frequencies follow a power-law decay with distance; log-spacing ensures equal representation across the range of distances while the smoothing algorithm reduces noise within each bin.
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.
- 11d ago First seen · 87 lines · 53 tokens per session scan A 9d1ea75a9466
contact-distance-binning-logarithmic is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed today), licensed Apache-2.0. It adds 53 tokens to every session and 1,309 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 2026-08-30.
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