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 PKU-YuanGroup/OpenAI4S --skill bio-crispr-screens-batch-correctiongit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-crispr-screens-batch-correction)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-crispr-screens-batch-correction"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-batch-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/pku-yuangroup/openai4s/bio-crispr-screens-batch-correction"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-crispr-screens-batch-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 | $0.00185 | $0.03969 |
| Opus 5 | $0.00093 | $0.01985 |
| Sonnet 5 | $0.00037 | $0.00794 |
| Haiku 4.5 | $0.00018 | $0.00397 |
Grade A, and why
bio-crispr-screens-batch-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 8d 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.
This is a copy
98% identical to bio-crispr-screens-batch-correction — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: pyComBat 0.3.3+ (epigenelabs/pyComBat), MAGeCK 0.5.9+, R/limma 3.58+, sva 3.50+, RUVSeq 1.36+, pandas 2.2+, numpy 1.26+, scikit-learn 1.4+, scipy 1.12+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show combat;from combat.pycombat import pycombat - R:
packageVersion('sva');?ComBat;packageVersion('RUVSeq');?RUVg
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Batch Correction for CRISPR Screens
"Correct batch effects in my CRISPR screens" -> Diagnose the batch source, decide whether to remove via empirical-Bayes (ComBat), explicit covariate modeling (MAGeCK MLE / Chronos design matrix), control-guide-anchored normalization, or unwanted-variation decomposition (RUV, SVA), then apply only the correction that preserves biological condition signal.
- Python:
pyComBat.pycombatfor empirical-Bayes correction - Python: explicit batch covariates in
mageck mle --design-matrix - R:
sva::ComBat,RUVSeq::RUVg,limma::removeBatchEffect - Python: Chronos (
crispr_chronos) natively handles screen-batch covariates
Batch Sources in CRISPR Screens
| Source | Mechanism | Detectable by |
|---|---|---|
| Library lot | Different aliquots or PCR amplifications | Gini shift; plasmid-pool sequencing |
| Cell passage cohort | Cells passaged through different periods | PCA Day-0 samples clustering by passage |
| Infection day | Lentivirus titer drifts; FBS lot changes | PCA Day-0 samples cluster by day |
| Cas9 enzyme lot | Cas9 expression heterogeneity | PR-AUC drift across screens |
| Sequencing run | Lane bias, flowcell variant, machine | Per-sample read-count distribution |
| FBS / culture lot | Fetal bovine serum lot variations confound proliferation | Day-0 vs endpoint differential not present in vehicle |
| Tissue-prep batch | In-vivo: animal cohort, surgical day, organ-prep tech | In-vivo screens (see [[in-vivo-screens]]) |
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.
- 8d ago First seen · 290 lines · 185 tokens per session scan A 756e0483b703
bio-crispr-screens-batch-correction is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed today), licensed MIT. It adds 185 tokens to every session and 3,969 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to bio-crispr-screens-batch-correction, differing in 12 lines, and is treated as a copy.
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