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 fragment-length-estimation-from-read-pairsgit 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/fragment-length-estimation-from-read-pairs)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/fragment-length-estimation-from-read-pairs"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/fragment-length-estimation-from-read-pairs/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/fragment-length-estimation-from-read-pairs"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/fragment-length-estimation-from-read-pairs.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 | $0.00067 | $0.01451 |
| Opus 5 | $0.00034 | $0.00726 |
| Sonnet 5 | $0.00013 | $0.00290 |
| Haiku 4.5 | $0.00007 | $0.00145 |
Grade A, and why
fragment-length-estimation-from-read-pairs 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fragment-length-estimation-from-read-pairs
Summary
Estimate the average sequencing fragment length from paired-end ChIP-Seq reads using cross-correlation analysis of read pair coordinates. This is a critical preliminary step that informs downstream pileup extension and peak calling sensitivity in ChIP-Seq analysis.
When to use
Apply this skill when you have paired-end ChIP-Seq data (BEDPE format) and need to determine the empirical fragment length (insertion length) before peak calling. This is mandatory for paired-end ChIP-Seq workflows to ensure MACS3 correctly extends coverage tracks and calculates local bias; it is especially important when the biological fragment length is unknown or expected to deviate significantly from default assumptions.
When NOT to use
- Input is single-end ChIP-Seq data (SAM, BAM, or BED format without read-pair information); fragment length estimation requires paired-end coordinate information.
- Fragment length has already been determined by external methods (e.g., Illumina fragment analyzer) and is being supplied manually via
--extsizeparameter; predictd estimation is redundant. - Sample is a control/input library rather than the ChIP library; the article states predictd should be applied only to ChIP data, not controls.
Inputs
- BEDPE file (paired-end ChIP sample, e.g., CTCF_PE_ChIP_chr22_50k.bedpe.gz)
Outputs
- Fragment length estimate (scalar, reported in base pairs)
- Cross-correlation profile (internal, used to determine d)
How to apply
Run macs3 predictd with the -f BEDPE flag on the paired-end ChIP sample to estimate the average insertion length via cross-correlation of forward and reverse reads. The subcommand analyzes the distribution of distances between paired reads and outputs a single fragment length estimate (typically reported in base pairs, e.g., ~253 bp for the CTCF_PE_ChIP_chr22_50k.bedpe.gz dataset). Record this estimated fragment length for use in subsequent macs3 callpeak and macs3 pileup steps. The estimated value should be validated against known biology (e.g., typical ChIP sonication fragment sizes) and inspected for outliers that suggest read-pair artifacts or misalignment. When fragment length is successfully estimated, proceed to peak calling without manually specifying --extsize in callpeak mode, allowing the paired-end mode to automatically handle fragment extent during pileup.
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 · 94 lines · 67 tokens per session scan A 735dad54bc4a
fragment-length-estimation-from-read-pairs is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 2d ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,451 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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