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 in-vivo-screensgit 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/in-vivo-screens)<a href="https://agentmods.dev/skills/gptomics/bioskills/in-vivo-screens"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/in-vivo-screens/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/in-vivo-screens"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/in-vivo-screens.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 | $0.00228 | $0.03769 |
| Opus 5 | $0.00114 | $0.01885 |
| Sonnet 5 | $0.00046 | $0.00754 |
| Haiku 4.5 | $0.00023 | $0.00377 |
Grade A, and why
bio-crispr-screens-in-vivo-screens 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-crispr-screens-in-vivo-screens — 100% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: MAGeCK 0.5.9+, MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
mageck --version - Reference focused libraries: Manguso 2017, Chen 2015, public Addgene aliquots
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
In Vivo CRISPR Screen Analysis
"Design or analyze an in vivo CRISPR screen" -> Account for the dramatic bottleneck during animal implantation and tumor growth; use focused libraries; recover DNA from tumor explants; analyze with bottleneck-adjusted hit calling.
- CLI:
mageck count+mageck testfor standard analysis - Special handling: bottleneck-adjusted coverage thresholds; per-tissue per-animal replicate structure
The In Vivo Bottleneck Problem
Why in vivo screens differ from in vitro:
| Constraint | In vitro | In vivo |
|---|---|---|
| Cells per condition | 10M-100M (unlimited) | Limited by injection volume (1-5M cells typical) |
| Implant -> early tumor cell count | N/A | 10-100x drop typical |
| Late tumor cell count | N/A | Further 5-10x reduction; ~4 sgRNAs/gene retained in late tumors (Scheidmann 2022) |
| Bottleneck per animal | None | Tens of millions of cells fail to engraft |
| Library coverage achievable | 500-1000x | Often 50-100x effective at endpoint |
| sgRNAs survivable | Full library | 66-97% in early (14 d) tumors, strongly cell-line dependent (Lee 2023); by 38-43 d most reads come from the top 1% of guides |
Math: A 70,000-sgRNA library at 500x coverage requires 35M cells in pool. Most syngeneic models can implant 1-5M cells. Result: real coverage is 70x at best; effective coverage at endpoint is even lower after bottleneck.
Solution: Use focused libraries (500-3,000 genes; ~3,000-15,000 sgRNAs) to maintain reasonable coverage despite the bottleneck.
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 · 243 lines · 228 tokens per session scan A 6b23efbcf2f1
bio-crispr-screens-in-vivo-screens is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 228 tokens to every session and 3,769 once invoked, about $0.0011 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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