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 GPTomics/bioSkills --skill chromatin-state-segmentationgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/chromatin-state-segmentation)<a href="https://agentmods.dev/skills/gptomics/bioskills/chromatin-state-segmentation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/chromatin-state-segmentation/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/gptomics/bioskills/chromatin-state-segmentation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/chromatin-state-segmentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00190 | $0.04201 |
| Opus 5 | $0.00095 | $0.02100 |
| Sonnet 5 | $0.00038 | $0.00840 |
| Haiku 4.5 | $0.00019 | $0.00420 |
Grade A, and why
bio-chipseq-chromatin-state-segmentation 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-chipseq-chromatin-state-segmentation — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ChromHMM 1.27+, Segway 3.0+, EpiSegMix 1.0+, EpiLogos (Meuleman lab), IDEAS 1.20+, samtools 1.19+, bedtools 2.31+. ChromHMM requires Java 8+; runs as java -mx<MEMORY> -jar ChromHMM.jar <command>.
Chromatin State Segmentation
"Integrate multiple histone modification ChIP-seq tracks into chromatin states" -> Learn a small set of recurring combinatorial patterns of histone marks (active promoter, active enhancer, poised enhancer, polycomb-repressed, heterochromatic, transcribed, etc.) and segment the genome by which state each region belongs to. Output: per-state genomic intervals, state-by-mark emission matrix, and state-state transition matrix.
- CLI (canonical): ChromHMM
BinarizeBam->LearnModel->OverlapEnrichment/NeighborhoodEnrichment - CLI (continuous signal): Segway
train->posterior->annotate - CLI (flexible distributions): EpiSegMix (2024)
- Visualization across biosamples: EpiLogos (Meuleman lab)
- Cell-type-aware joint: IDEAS
Chromatin state segmentation requires a panel of histone marks; minimum 4-5 marks (e.g., H3K4me3, H3K27ac, H3K4me1, H3K36me3, H3K27me3) for meaningful states. With fewer marks, simpler peak-based annotation (chipseq/peak-annotation) is more appropriate.
Tool Taxonomy
| Tool | Method | Strength | Fails when |
|---|---|---|---|
| ChromHMM (Ernst & Kellis 2012; v1.27 current) | Multivariate HMM on binarized 200 bp bins | Canonical; widely used; integrated with Roadmap Epigenomics 15-state model; mature toolchain | Binarization throws away signal quantitation; default 200 bp bins may be too coarse for sharp boundaries |
| Segway (Hoffman 2012) | Dynamic Bayesian Network on continuous signal | Higher resolution; uses signal magnitudes not binarized | More complex setup; slower; less standardized output |
| EpiSegMix (Schmitz, Aggarwal, Laufer, Walter, Salhab, Rahmann 2024 Bioinformatics 40:btae178) | HMM with flexible read-count distributions + duration modeling | Modern; handles both narrow and broad mark distributions in one model | Newer; smaller user base |
| EpiLogos (Meuleman lab) | Multi-biosample visualization tool | Built on top of ChromHMM/Segway segmentations; compare ChromHMM states across 100s of biosamples | Visualization tool, not a segmentation method itself |
| IDEAS (Zhang 2016) | Cell-type-aware joint inference | Across-cell-type segmentation respecting cell-type identity | Slower; complex parameter tuning |
| EpiCSeg (Mammana 2015) | Negative binomial mixture | Read-count-based; doesn't need binarization | Less standardized output |
| GenoSTAN | HMM with various emission distributions | Flexible | Less actively developed |
| Roadmap 25-state model (Kundaje 2015) | ChromHMM 25-state precomputed model | Reference for cross-cell-type interpretation | Requires the Roadmap imputed 12-mark panel |
| Full-stack ChromHMM (Vu Ernst 2022) | 100-state segmentation across 1032 datasets / 127 reference epigenomes | Comprehensive cross-tissue annotation | Computationally intensive to retrain |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 292 lines · 190 tokens per session scan A c8dae5c57024
bio-chipseq-chromatin-state-segmentation is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 190 tokens to every session and 4,201 once invoked, about $0.0010 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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