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 juicer-pipeline-configuration-and-executiongit 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/juicer-pipeline-configuration-and-execution)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/juicer-pipeline-configuration-and-execution"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/juicer-pipeline-configuration-and-execution/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/juicer-pipeline-configuration-and-execution"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/juicer-pipeline-configuration-and-execution.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 | $0.00048 | $0.01712 |
| Opus 5 | $0.00024 | $0.00856 |
| Sonnet 5 | $0.00010 | $0.00342 |
| Haiku 4.5 | $0.00005 | $0.00171 |
Grade A, and why
juicer-pipeline-configuration-and-execution 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.
juicer-pipeline-configuration-and-execution
Summary
Configure and execute the Juicer pipeline to transform raw Hi-C FASTQ data into processed Hi-C contact maps (.hic files) through read alignment, contact matrix construction, and normalization. This skill is essential for generating kilobase-resolution Hi-C datasets from sequencing output.
When to use
You have raw Hi-C FASTQ files from a high-throughput chromatin conformation capture experiment and need to generate a normalized contact matrix (.hic file) for downstream genomic analysis. Use this skill when starting from FASTQ-format sequencing reads rather than pre-aligned BAM or existing contact matrices.
When NOT to use
- Input data is already in .hic format or pre-processed contact matrix form — use Juicer Tools for post-processing instead
- You have only single-end sequencing reads — Juicer requires paired-end Hi-C reads
- Your restriction enzyme is not pre-configured in the pipeline and you lack the ability to define custom restriction site coordinates
Inputs
- Hi-C FASTQ files (paired-end sequencing reads)
- Reference genome sequence (FASTA)
- Restriction enzyme site file (e.g., HindIII or MboI coordinates)
- Chromosome sizes file (.chrom.sizes)
Outputs
- .hic file (processed Hi-C contact map with normalized contacts)
- merged_nodups file (deduplicated alignments)
- Pipeline statistics and QC metrics
How to apply
Clone either Juicer 1.6 (stable) or Juicer 2 (development) from the aidenlab/juicer GitHub repository based on your deployment requirements. Obtain Hi-C FASTQ data from public repositories (GEO, SRA) or your own sequencing experiment. Configure the pipeline by specifying the reference genome ID (e.g., 'hg19', 'mm10'), restriction enzyme site (e.g., 'HindIII', 'MboI'), and computational resources (thread count, memory allocation ≥64 GB RAM recommended for cluster, minimum 16 GB). Select the appropriate execution environment script (SLURM or CPU recommended as most up-to-date; AWS, LSF, and UGER are deprecated). Execute juicer.sh with your parameters, which orchestrates read alignment via BWA, chimeric read handling, duplicate removal, and contact matrix normalization. Monitor completion and verify that a .hic output file is generated in the aligned directory.
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 · 48 tokens per session scan A 4aea0dc04324
juicer-pipeline-configuration-and-execution is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 1,712 once invoked, about $0.0002 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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