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 copy-ratio-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/copy-ratio-segmentation)<a href="https://agentmods.dev/skills/gptomics/bioskills/copy-ratio-segmentation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/copy-ratio-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/copy-ratio-segmentation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/copy-ratio-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 | $0.00138 | $0.03383 |
| Opus 5 | $0.00069 | $0.01691 |
| Sonnet 5 | $0.00028 | $0.00677 |
| Haiku 4.5 | $0.00014 | $0.00338 |
Grade A, and why
bio-copy-number-copy-ratio-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 7d 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-copy-number-copy-ratio-segmentation — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: R 4.3+ with DNAcopy 1.76+, Python 3.10+ with numpy 1.26+, pandas 2.2+; QDNAseq 1.38+ (optional, GC/mappability normalization).
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('DNAcopy')then?segmentto confirm arguments - Python:
pip show numpy pandas
If code throws an error, introspect the installed package and adapt the example. CBS lives in Bioconductor DNAcopy; HMM segmentation is provided by caller-specific backends (CNVkit uses pomegranate; HaarSeg has its own R/Python packages).
Copy-Ratio Segmentation
"Turn noisy per-bin depth into clean copy-number segments" -> Two stages, both error-prone. First, normalize the depth profile so the only remaining variation is copy number (not GC, mappability, or replication timing). Second, partition the normalized profile into segments of constant copy number. The segmentation algorithm choice has a predictable bias signature, and the diploid-baseline choice can invert every call.
- R:
DNAcopy::segment(CBS, the reference implementation) - Python: HMM via
pomegranate; HaarSeg viahaarseg - The output feeds every CNV caller (cnvkit-analysis, gatk-cnv, allele-specific-copy-number)
Stage 1: Why Depth Is Biased Before It Is Copy Number
Raw read depth confounds copy number with three systematic biases:
| Bias | Cause | Correction |
|---|---|---|
| GC content | PCR efficiency and probe hybridization vary with GC | Loess fit of depth vs GC (QDNAseq), or matched normal |
| Mappability | Multi-mapping reads under-counted in repetitive regions | Mappability track filter/weight; exclude low-mappability bins |
| Replication timing | Late-replicating DNA is under-represented — the "wave artifact" | Matched normal or PoN; GC correction alone does NOT remove it |
| Capture efficiency | Per-probe hybridization varies 10-100x (hybrid capture) | Panel of normals — the dominant bias for exomes/panels |
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.
- 7d ago First seen · 208 lines · 138 tokens per session scan A 3cfb914c3378
bio-copy-number-copy-ratio-segmentation is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 26d ago), licensed MIT. It adds 138 tokens to every session and 3,383 once invoked, about $0.0007 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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