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 perturb-seq-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/perturb-seq-analysis)<a href="https://agentmods.dev/skills/gptomics/bioskills/perturb-seq-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/perturb-seq-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/perturb-seq-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/perturb-seq-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 | $0.00262 | $0.04863 |
| Opus 5 | $0.00131 | $0.02431 |
| Sonnet 5 | $0.00052 | $0.00973 |
| Haiku 4.5 | $0.00026 | $0.00486 |
Grade A, and why
bio-crispr-screens-perturb-seq-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-perturb-seq-analysis — 92% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: Pertpy 0.6+, SCEPTRE 0.10+ (R / katsevich-lab/sceptre), Mixscape via Seurat 4.3+ or Pertpy, scanpy 1.10+, anndata 0.10+, pandas 2.2+, numpy 1.26+, scipy 1.12+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show pertpy scanpy anndata - R:
packageVersion('sceptre');?sceptre;?Seurat::PrepLDA
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Single-Cell Perturb-Seq Analysis
"Analyze a single-cell pooled CRISPR perturbation screen" -> Assign sgRNAs to cells, filter unperturbed escapers, normalize counts, fit per-gene differential expression conditioned on perturbation, and rank perturbations by their molecular effect.
- Python:
pertpyunified framework for Mixscape + SCEPTRE-via-R + differential expression - R:
sceptrefor low-MOI NB GLM + permutation testing - Python/R:
Seurat::MixscapeLDAand downstream
Experimental Architecture Comparison
| Method | Year | Architecture | Readout | MOI | Single-cell sgRNA detection |
|---|---|---|---|---|---|
| Perturb-seq (Dixit 2016, Cell) | 2016 | sgRNA expressed in cassette; direct PCR capture | scRNA-seq | Low-to-moderate (MOI ~0.35-1.4; a minority of cells receive multiple guides, enabling epistasis analysis) | Yes via amplicon-PCR pre-sequencing |
| CROP-seq (Datlinger 2017, Nat Methods) | 2017 | hU6-sgRNA cassette placed in the 3' LTR of lentiGuide-Puro; LTR duplication puts the sgRNA in the 3'UTR of the Pol II puromycin-resistance transcript | scRNA-seq | Low (1-2 sgRNAs/cell) | Native via 10X 3' chemistry |
| Perturb-CITE-seq (Frangieh 2021, Nat Genet) | 2021 | Adds surface-protein hashtag oligos to CROP-seq | scRNA-seq + ADT (protein) | Low | CROP-seq architecture |
| ECCITE-seq (Mimitou 2019, Nat Methods) | 2019 | Surface-protein hashtag with sgRNA-marked cells | scRNA-seq + ADT | Low | Hash + sgRNA |
| Perturb-ATAC (Rubin 2019, Cell) | 2019 | scATAC-seq readout | scATAC | Low | sgRNA capture via separate library prep |
| Perturb-multiome (10X) | 2021+ | scRNA + scATAC simultaneously | scRNA + ATAC | Low | Direct capture from sgRNA cassette |
| Replogle GW Perturb-seq (2022, Cell) | 2022 | Multiplexed CRISPRi with sgRNA barcoding | scRNA-seq | 1 sgRNA/cell | Direct capture |
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 · 309 lines · 262 tokens per session scan A 7307655de8a1
bio-crispr-screens-perturb-seq-analysis is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 262 tokens to every session and 4,863 once invoked, about $0.0013 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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