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 mageck-analysisgit 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/mageck-analysis)<a href="https://agentmods.dev/skills/gptomics/bioskills/mageck-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/mageck-analysis/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/mageck-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/mageck-analysis.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 | $0.00223 | $0.06193 |
| Opus 5 | $0.00112 | $0.03096 |
| Sonnet 5 | $0.00045 | $0.01239 |
| Haiku 4.5 | $0.00022 | $0.00619 |
Grade A, and why
bio-crispr-screens-mageck-analysis 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-mageck-analysis — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 383 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+, MAGeCKFlute 2.0+ (R/Bioconductor), MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
mageck --version,mageck count --help,mageck test --help,mageck mle --help - R:
packageVersion('MAGeCKFlute'),?FluteRRA,?FluteMLE
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
MAGeCK CRISPR Screen Analysis
"Run MAGeCK on my pooled CRISPR screen" -> Count sgRNAs from FASTQ, normalize across samples, and rank genes by enrichment or depletion using either the robust rank aggregation (RRA) test for two-condition designs or the maximum-likelihood (MLE) model with explicit design matrix for multi-condition / time-course / drug screens.
- CLI:
mageck count->mageck testfor two-condition RRA - CLI:
mageck mlefor multi-condition / time-course / multi-cell-line MLE - R:
MAGeCKFlute::FluteRRA()/FluteMLE()for downstream visualization and pathway analysis - Python:
mageck-visprfor interactive QC + result dashboard
RRA vs MLE Decision Tree
| Experimental design | Recommended | Why |
|---|---|---|
| Two conditions (e.g. drug vs vehicle, treated vs untreated), single cell line, no covariates | mageck test (RRA) |
RRA is more robust to outlier sgRNAs; faster; default for most published screens |
| Time series (Day 0 -> Day 7 -> Day 14 -> Day 21) | mageck mle |
RRA cannot model multiple timepoints jointly; MLE estimates per-condition beta scores |
| Multi-cell-line panel (e.g. DepMap-style 5-50 lines) | mageck mle with cell-line covariate, or Chronos |
MLE handles >2 conditions; Chronos preferred at DepMap scale |
| Paired samples (each replicate matched donor/cell prep) | mageck mle with paired design |
RRA does not support pairing |
| Combinatorial (treatment x cell line x time) | mageck mle with full factorial design |
RRA only handles 1 factor |
| Drug screen (vehicle vs drug, multiple doses) | mageck mle with dose covariate OR drugZ (preferred for chemogenomic) |
drugZ optimized for chemogenomic; see [[drugz-chemogenomic]] |
| Essentiality (Day 0 -> endpoint, single condition) | mageck test for simple dropout; mageck mle if multi-cell-line |
RRA suffices; or BAGEL2 for Bayesian essentiality; see [[bagel-essentiality]] |
| Multi-batch / multi-screen joint analysis | mageck mle with batch covariate, JACKS for guide efficacy, or Chronos |
See [[batch-correction]] |
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 · 383 lines · 223 tokens per session scan A b054d0e0892f
bio-crispr-screens-mageck-analysis is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 223 tokens to every session and 6,193 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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