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 screen-qcgit 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/screen-qc)<a href="https://agentmods.dev/skills/gptomics/bioskills/screen-qc"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/screen-qc/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/screen-qc"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/screen-qc.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 | $0.00173 | $0.06433 |
| Opus 5 | $0.00086 | $0.03216 |
| Sonnet 5 | $0.00035 | $0.01287 |
| Haiku 4.5 | $0.00017 | $0.00643 |
Grade A, and why
bio-crispr-screens-screen-qc 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-screen-qc — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 363 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+ (count + VISPR), MAGeCKFlute 2.0+ (R), pandas 2.2+, numpy 1.26+, scikit-learn 1.4+, matplotlib 3.8+, seaborn 0.13+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
mageck --versionthenmageck count --help; R:packageVersion('MAGeCKFlute') - R:
packageVersion('MAGeCKFlute')then?BatchRemove/?FluteRRA
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
CRISPR Screen Quality Control
"Audit my CRISPR screen quality before hit calling" -> Assess library representation, replicate concordance, depth, drift, and biological signal recovery using DepMap-grade metrics, then decide whether the screen is usable, salvageable, or must be repeated.
- Python:
pandas+scikit-learnfor Gini, AUC, PCA;MAGeCKFlute(R) for one-shot QC dashboard - CLI:
mageck countwrites Gini and mapping stats unconditionally to<prefix>.countsummary.txt(GiniIndex,Reads,Mapped,Percentage); MAGeCK-VISPR for an interactive dashboard
QC Stage Hierarchy
A pooled screen has six distinct bottlenecks where complexity can collapse. Audit each:
| Stage | Metric | Acceptable threshold | Failure consequence |
|---|---|---|---|
| Plasmid pool | Gini, skew, % zero-count guides | Gini <0.1, skew <2 (Joung 2017 states <10), zero <0.5% | Missing guides cannot be screened; dropout indistinguishable from non-coverage |
| Day-0 infection | Library coverage, MOI verification | ≥99% guide detection at 500x cells/sgRNA; MOI 0.3 | Founder effects; polyclonality with high MOI |
| Selection (puro/blast) | % cells surviving, time-course Gini | 30-40% survival at 5-7 days; Gini drift <0.05 | Selection artifact; fast-growers enriched |
| Endpoint | Replicate correlation, depth | Pearson >=0.8 on log-counts (MAGeCK-VISPR floor), Spearman >0.7-0.8, >500 reads/sgRNA (Joung 2017 screening) | Noise dominates; FDR inflates |
| Biological signal | CEGv2 PR-AUC, NEGv1 false-positive rate | PR-AUC >0.7 at FDR 5% (community "passing" convention); CEGv2 enrichment in top 1k | Screen lacks essentiality signal; hits not credible |
| Copy-number artifact | Amplified-region enrichment, sgRNA-cut-count correlation | No correlation between sgRNA off-target count and depletion | False-positive essentiality at amplicons; ERBB2 in HER2+ etc. |
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 · 363 lines · 173 tokens per session scan A 69bd4fbbdb93
bio-crispr-screens-screen-qc is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 173 tokens to every session and 6,433 once invoked, about $0.0009 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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