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 kilobase-resolution-contact-map-analysisgit 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/kilobase-resolution-contact-map-analysis)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/kilobase-resolution-contact-map-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/kilobase-resolution-contact-map-analysis/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/kilobase-resolution-contact-map-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/kilobase-resolution-contact-map-analysis.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 | $0.00066 | $0.02043 |
| Opus 5 | $0.00033 | $0.01022 |
| Sonnet 5 | $0.00013 | $0.00409 |
| Haiku 4.5 | $0.00007 | $0.00204 |
Grade A, and why
kilobase-resolution-contact-map-analysis 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
kilobase-resolution-contact-map-analysis
Summary
Generate and validate Hi-C contact maps at kilobase resolution from FASTQ raw sequencing data using the Juicer pipeline and ENCODE's uniform processing workflow. This skill enables reproducible construction of three-dimensional genome contact matrices with format validation and output integrity verification.
When to use
You have paired-end Hi-C FASTQ files from a public repository (NCBI SRA, GEO, or ENCODE-deposited) and need to produce standardized .hic binary contact maps that conform to ENCODE reference formats and integrity standards for downstream 3D genome analysis.
When NOT to use
- Input is not raw FASTQ data but already an aligned BAM or SAM file — use the 'merge' or 'dedup' stage restart flags instead of running the full pipeline.
- You require sub-kilobase or nucleosome-resolution contact mapping — Juicer is optimized for kilobase resolution and may not provide sufficient granularity for finer scales.
- Your cluster does not support SLURM, LSF, GridEngine, or cloud execution; CPU-only mode requires >= 4 cores and >= 64 GB RAM minimum, and is not recommended for large datasets.
Inputs
- Hi-C paired-end FASTQ files from public repository (NCBI SRA, GEO) or ENCODE accession
- Genome identifier (e.g., hg19, mm10) or reference genome file in FASTA format
- Restriction enzyme site file (e.g., HindIII, MboI)
- Chromosome sizes file (.chrom.sizes)
Outputs
- .hic binary contact map file (Hi-C format)
- File checksum or hash for integrity verification
- Alignment statistics and deduplication metrics
- Feature-annotated Hi-C maps (via postprocessing command-line tools)
How to apply
Clone the ENCODE Hi-C uniform processing pipeline (ENCODE-DCC/hic-pipeline) and invoke the Juicer-based encode_hic_pipeline wrapper on your FASTQ input files to align reads and construct the Hi-C contact map in .hic binary format. The pipeline performs chimeric read handling, deduplication, and final Hi-C file generation across configurable cluster environments (SLURM, CPU, or cloud via Caper/Cromwell). Validate output by computing file checksums or hashes and comparing against ENCODE reference outputs to confirm pipeline reproducibility. For cloud execution, use the dockerized ENCODE pipeline with Caper (Python wrapper for Cromwell) rather than the deprecated AWS scripts; ensure Java >= 1.8 and Python >= 3.6 are installed.
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 · 106 lines · 66 tokens per session scan A d768b2d73d70
kilobase-resolution-contact-map-analysis is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 66 tokens to every session and 2,043 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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