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-matrix-normalization-pipeline-setupgit 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-matrix-normalization-pipeline-setup)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/hi-c-matrix-normalization-pipeline-setup"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/hi-c-matrix-normalization-pipeline-setup/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-matrix-normalization-pipeline-setup"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/hi-c-matrix-normalization-pipeline-setup.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 | $0.00062 | $0.01738 |
| Opus 5 | $0.00031 | $0.00869 |
| Sonnet 5 | $0.00012 | $0.00348 |
| Haiku 4.5 | $0.00006 | $0.00174 |
Grade A, and why
hi-c-matrix-normalization-pipeline-setup 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
hi-c-matrix-normalization-pipeline-setup
Summary
Install and configure the iced Python module and its dependencies to enable iterative correction and eigenvalue decomposition (ICE) normalization of raw Hi-C contact matrices within the HiC-Pro pipeline. This skill ensures that the normalization stage can correct biases and balance contact frequency matrices after read alignment and filtering.
When to use
Before running HiC-Pro's normalization stage on aligned Hi-C BAM files. Specifically, when you have SAM/BAM-formatted aligned Hi-C reads that need bias correction and matrix balancing to produce normalized contact maps suitable for downstream chromatin structure analysis. The iced module is a required dependency that no longer ships with HiC-Pro source code and must be independently installed.
When NOT to use
- If you are using a pre-built HiC-Pro container (Docker, Singularity, or conda environment) that already includes iced; the dependency is already resolved.
- If your downstream analysis does not require contact matrix normalization or uses an alternative normalization method (e.g., external normalization tools or raw contact frequencies).
- If Python < 3.7 is your only available interpreter; iced requires Python >3.7 and will not function on Python 2.x.
Inputs
- System Python (>3.7) installation
- iced module source code (from GitHub repository)
- System package manager or pip environment
Outputs
- Installed iced Python module with accessible API
- Installed numpy (≥1.18.1) and scipy (≥1.4.1) dependencies
- Configuration file documenting PYTHONPATH and iced installation location
How to apply
Verify that Python (>3.7) is available on your system. Clone or download the iced module from https://github.com/hiclib/iced and install it along with transitive dependencies (numpy ≥1.18.1, scipy ≥1.4.1) using pip or the iced setup.py installer. Test the installation by importing the module in a Python interpreter and confirming version/API availability. Document the iced installation path and set PYTHONPATH environment variables in a configuration file that will be sourced by downstream HiC-Pro normalization steps. This ensures that when HiC-Pro calls the iced normalization functions, the module is discoverable and its dependencies are met.
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 · 109 lines · 62 tokens per session scan A 7c1caa1e45d5
hi-c-matrix-normalization-pipeline-setup is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 62 tokens to every session and 1,738 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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