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-number-correctiongit 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-number-correction)<a href="https://agentmods.dev/skills/gptomics/bioskills/copy-number-correction"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/copy-number-correction/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-number-correction"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/copy-number-correction.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 | $0.00245 | $0.04591 |
| Opus 5 | $0.00122 | $0.02295 |
| Sonnet 5 | $0.00049 | $0.00918 |
| Haiku 4.5 | $0.00024 | $0.00459 |
Grade A, and why
bio-crispr-screens-copy-number-correction 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-crispr-screens-copy-number-correction — 95% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: CRISPRcleanR 3.0+ (R; github.com/francescojm/CRISPRcleanR), Chronos 2.0+ (https://github.com/broadinstitute/chronos), CERES (legacy, superseded by Chronos), pandas 2.2+, numpy 1.26+, scipy 1.12+.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('CRISPRcleanR');?ccr.GWclean - Python:
pip show crispr_chronos;python -c 'import chronos; print(chronos.__file__)'
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Copy-Number Bias Correction in CRISPR Screens
"Correct copy-number artifacts in my cancer-cell-line screen" -> Identify gene-independent depletion at amplified loci, apply CRISPRcleanR (pre-hoc, unsupervised, position-based) or Chronos (joint model, supervised with CN profile) to remove the artifact, then proceed to hit calling on corrected data.
- R:
CRISPRcleanR::ccr.GWclean()for unsupervised pre-hoc correction (no CN profile required) - Python: Chronos (
crispr_chronos) for joint cell-population dynamics + CN modeling - Python: CERES (legacy, superseded by Chronos)
The Copy-Number Artifact (Mechanism)
Aguirre AJ et al 2016 Cancer Discov 6:914 and Munoz DM et al 2016 Cancer Discov 6:900 demonstrated that focal amplification regions in cancer cell lines appear systematically "essential" in CRISPR-Cas9 screens, independent of the gene's actual biology. The mechanism:
- A focal amplification creates 4-50+ copies of a genomic region.
- Each sgRNA targeting a gene in that region cuts at all copies simultaneously.
- Multiple cuts trigger a DNA-damage response and G2 arrest, in both TP53-mutant and TP53-wild-type lines but with larger magnitude in wild-type (Aguirre 2016).
- Cells arrest in G2 phase; the sgRNA appears depleted because its bearer cells don't proliferate.
- The depletion is proportional to the number of simultaneous cuts, not the gene's essentiality.
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 · 299 lines · 245 tokens per session scan A 7e9cd469eb94
bio-crispr-screens-copy-number-correction is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 245 tokens to every session and 4,591 once invoked, about $0.0012 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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