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 eigenvector-digitization-into-compartment-binsgit 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/eigenvector-digitization-into-compartment-bins)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/eigenvector-digitization-into-compartment-bins"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/eigenvector-digitization-into-compartment-bins/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/eigenvector-digitization-into-compartment-bins"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/eigenvector-digitization-into-compartment-bins.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 | $0.00059 | $0.01466 |
| Opus 5 | $0.00030 | $0.00733 |
| Sonnet 5 | $0.00012 | $0.00293 |
| Haiku 4.5 | $0.00006 | $0.00147 |
Grade A, and why
eigenvector-digitization-into-compartment-bins 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
eigenvector-digitization-into-compartment-bins
Summary
Discretize continuous eigenvector values from Hi-C compartment analysis into categorical bins (typically 2–5 levels) representing A/B compartment states using quantile or fixed thresholds. This preprocessing step converts a continuous genomic track into a discrete compartment assignment required for downstream saddle plot analysis and compartment interaction quantification.
When to use
You have computed eigenvector values from a prior eigs_cis calculation on a cooler Hi-C matrix and need to classify genomic regions into discrete A/B compartment categories before performing saddle analysis or computing compartment-level contact asymmetry metrics.
When NOT to use
- Eigenvector values are missing or NaN for large fractions of the genome (no meaningful bins can be defined)
- The eigenvector track and cooler have mismatched bin resolutions or genomic coordinates
- You are performing continuous (non-binned) compartment strength analysis and do not need discrete categorical assignments
Inputs
- cooler Hi-C matrix file (h5 format)
- eigenvector track (1D numpy array or bedGraph, indexed by genomic bins)
- binning parameters (bin edges, quantile thresholds, or bin count)
Outputs
- digitized compartment track (1D array of integer bin labels, same length as input eigenvector)
- bin boundary definitions or threshold values used for digitization
How to apply
Load the eigenvector track (a 1D array indexed by genomic bins) alongside the cooler Hi-C object. Apply cooltools.digitize to partition eigenvector values according to quantile-based or fixed thresholds into 2–5 discrete bins, each representing a compartment strength class. The choice of binning strategy (quantile vs. fixed threshold) should reflect whether you prioritize balanced bin populations or absolute eigenvector magnitude cutoffs. Validate that the output digitized track has the correct length (matching the number of bins in the cooler) and that bin labels are numeric (typically 0–4). This discretized track is then passed to cooltools.saddle to aggregate contact frequencies by compartment pair.
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 · 99 lines · 59 tokens per session scan A e5f6c8b50170
eigenvector-digitization-into-compartment-bins is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 59 tokens to every session and 1,466 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.
Other skills, from other repositories
gsva-analysis-and-visualization
Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression…
medical-research-literature-reader-pro
A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title.…
adverse-event-narrative
Generates CIOMS I-compliant ICSR narratives from adverse event case data for FDA and EMA regulatory submission. Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.
anatomy-quiz-master
Generate interactive anatomy quizzes for medical education with multiple.
decision-curve-analysis
Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or…
elastic-net-feature-selection
Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification…