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 encode-hic-pipeline-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/encode-hic-pipeline-execution)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/encode-hic-pipeline-execution"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/encode-hic-pipeline-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/encode-hic-pipeline-execution"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/encode-hic-pipeline-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 | $0.00071 | $0.01898 |
| Opus 5 | $0.00036 | $0.00949 |
| Sonnet 5 | $0.00014 | $0.00380 |
| Haiku 4.5 | $0.00007 | $0.00190 |
Grade A, and why
encode-hic-pipeline-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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
encode-hic-pipeline-execution
Summary
Execute the ENCODE Hi-C uniform processing pipeline (based on Juicer) to generate aligned Hi-C contact maps and .hic binary files from raw FASTQ sequencing data, with output validation against reference checksums to ensure reproducibility and format correctness.
When to use
You have raw Hi-C FASTQ files from a public repository (NCBI SRA, GEO, or ENCODE-deposited accession) and need to reproduce or validate Hi-C map generation following the ENCODE uniform processing standard, or you need to verify that your pipeline output conforms to reference format and integrity standards expected for Hi-C maps.
When NOT to use
- Input is already a .hic file or pre-processed Hi-C contact map (skip directly to feature annotation or downstream analysis).
- You require GPU-accelerated HiCCUPS peak calling and do not have access to an NVIDIA GPU or CPU fallback alternative.
- Your cluster does not support any of Juicer's supported resource managers (SLURM, LSF, GridEngine, OpenLava) and cloud infrastructure is not available.
Inputs
- Hi-C FASTQ dataset (paired-end sequencing reads)
- Reference genome FASTA file
- Restriction site file (e.g., for HindIII or MboI)
- Chromosome sizes file (chrom.sizes)
Outputs
- Aligned reads file (BAM or intermediate SAM format)
- Hi-C contact map (.hic binary format)
- Merged and deduplicated alignments (merged_nodups)
- Pipeline statistics and QC metrics
- File checksum/hash for output validation
How to apply
Clone the ENCODE Hi-C uniform processing pipeline from ENCODE-DCC/hic-pipeline and install Caper (Python wrapper for Cromwell) with Java >= 1.8 and Python >= 3.6. Configure your Caper configuration file (~/.caper/default.conf) for your platform (local, cloud, or cluster). Run the encode_hic_pipeline wrapper via Caper on your FASTQ input files to execute the Juicer-based pipeline, which performs alignment, duplicate removal, and constructs the Hi-C contact map in .hic binary format. After pipeline completion, validate the output .hic file by computing its checksum (e.g., MD5 hash) and compare it against the ENCODE reference output checksum to confirm reproducibility and detect any corruption or deviation in the processing pipeline.
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 · 107 lines · 71 tokens per session scan A 9e93ff6adc73
encode-hic-pipeline-execution is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 71 tokens to every session and 1,898 once invoked, about $0.0004 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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