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 recurrent-cnvgit 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/recurrent-cnv)<a href="https://agentmods.dev/skills/gptomics/bioskills/recurrent-cnv"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/recurrent-cnv/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/recurrent-cnv"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/recurrent-cnv.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 | $0.02970 |
| Opus 5 | $0.00069 | $0.01485 |
| Sonnet 5 | $0.00028 | $0.00594 |
| Haiku 4.5 | $0.00014 | $0.00297 |
Grade A, and why
bio-copy-number-recurrent-cnv 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-recurrent-cnv — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: GISTIC 2.0.23, R 4.3+ with CINSignatureQuantification 1.2+; Python 3.10+ with SigProfilerAssignment 0.1+ (optional, COSMIC CN signatures).
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
gistic2 --help(GISTIC 2.0 is a MATLAB-compiled binary; needs the MCR runtime) - R:
packageVersion('CINSignatureQuantification') - Python:
pip show SigProfilerAssignment
GISTIC 2.0 has had no substantive release since ~2017; it is effectively frozen. It runs as a compiled binary against the MATLAB Compiler Runtime — there is no R or Python package. Verify the reference (-refgene) .mat file matches the genome build.
Recurrent and Driver Copy Number Alteration
"Which copy number changes recur across my cohort, and which gene is the driver" -> A CNV in one tumor is an observation; a CNV recurring across many tumors beyond chance is evidence of selection. GISTIC2 separates recurrent driver events from passengers by modeling a background rate and scoring each locus by how often, and how strongly, it is altered. Copy-number signatures decompose the genome-wide pattern of alterations into the mutational processes that generated them.
- CLI:
gistic2— cohort-level recurrence, focal vs broad, driver localization - R:
CINSignatureQuantification(Drews 2022); PythonSigProfilerAssignment(Steele 2022 COSMIC)
How GISTIC2 Works — and Its Limits
GISTIC2 scores each genomic marker with a G-score = frequency of alteration x mean amplitude, separately for amplifications and deletions. Significance (q-value) comes from permuting events along the genome under the null that all are passengers. Ziggurat deconstruction decomposes each sample's profile into the additive arm-level and focal events that produced it, so the background rate is estimated separately for broad and focal alterations — without this, ubiquitous arm-level events swamp the focal signal. A peel-off procedure removes the contribution of each significant peak before testing the next, so one strong driver does not mask its neighbors.
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 · 185 lines · 138 tokens per session scan A cc81804c4e86
bio-copy-number-recurrent-cnv is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 138 tokens to every session and 2,970 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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