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-fastq-read-preprocessinggit 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-fastq-read-preprocessing)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/hi-c-fastq-read-preprocessing"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/hi-c-fastq-read-preprocessing/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-fastq-read-preprocessing"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/hi-c-fastq-read-preprocessing.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 | $0.00068 | $0.01775 |
| Opus 5 | $0.00034 | $0.00888 |
| Sonnet 5 | $0.00014 | $0.00355 |
| Haiku 4.5 | $0.00007 | $0.00178 |
Grade A, and why
hi-c-fastq-read-preprocessing 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
hi-c-fastq-read-preprocessing
Summary
Preprocess raw Hi-C FASTQ files through the Juicer pipeline to generate normalized Hi-C contact maps at kilobase resolution. This skill transforms raw sequencing reads into structured Hi-C interaction matrices suitable for downstream 3D genomics analysis.
When to use
Apply this skill when you have raw Hi-C FASTQ files from a Hi-C wet-lab protocol and need to convert them into processed Hi-C contact maps (.hic files) for loop detection, TAD identification, or 3D structure inference. Use when starting from deposited public Hi-C datasets (e.g., from GEO or SRA) or newly sequenced Hi-C libraries.
When NOT to use
- Input FASTQ files are from non-Hi-C protocols (e.g., RNA-seq, ChIP-seq, single-cell RNA-seq) — use appropriate pipelines for those modalities.
- Contact matrices are already in processed .hic or matrix format — skip directly to feature annotation or downstream analysis tools.
- Restriction enzyme used is not pre-configured in Juicer — manual enzyme coordinate file creation may be required, which is outside standard preprocessing scope.
Inputs
- Hi-C raw FASTQ files (paired-end sequencing reads)
- Reference genome FASTA file
- Restriction enzyme site coordinates file
- Chromosome sizes file (chrom.sizes)
Outputs
- .hic contact map file (normalized Hi-C interaction matrix)
- Merged alignment file (merged_nodups)
- Pipeline statistics and QC metrics
How to apply
Clone the Juicer repository (selecting either stable release 1.6 or development version Juicer 2 based on your requirements) and configure it with the appropriate reference genome, restriction enzyme used in the Hi-C protocol, and computational resources (thread count and memory allocation matching your cluster capabilities). Place raw FASTQ files in the designated input directory and execute the Juicer pipeline via juicer.sh with the selected genome ID and restriction site parameters. The pipeline performs sequential read alignment (via BWA), contact matrix construction, and normalization to produce a final .hic output file. Verify completion by checking that the .hic file was generated successfully and contains valid contact frequency data at the expected resolution.
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 · 104 lines · 68 tokens per session scan A 82b7d51a06ab
hi-c-fastq-read-preprocessing is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 68 tokens to every session and 1,775 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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