Analyze CRISPR-Cas9 genetic screens — MAGeCK gene-level scores, sgRNA count QC, replicate correlation, hit prioritization, and pathway GSEA on screen output.
SkillScore82Maintained
On-demandOn-demand: invoked explicitly — never occupies your agent's context until called.
◆Research pack
Last verified 8/6/2026
What it changes
Passed 15 of 18 test cases with the skill, and 11 of 18 without it.
Without skill64%
With skill82%
Checks it passed that the model alone missed
Requires a delta log2 fold change greater than 1.5.
Recommends filtering sgRNAs with fewer than 30 reads.
Ranks genes by log2 fold change or effect size rather than p-value.
and 2 more in the full report
5 fixed13 passed either way3 still failing1 broke
Pass-rate lift
+18 pp
Tokens
+91%
Agent turns
0%
Response time
-34%
Model gemini-3.6-flashMeasured 8/6/2026v118 of 22 cases passed with skill · 18 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
When filtering differential essentiality hits between wildtype and mutant lines, what minimum magnitude of delta log2 fold change is required to classify a hit as synthetic lethal?
What was checked, without and with the skill
Checked
Without skill
With skill
Requires a delta log2 fold change greater than 1.5.
Missed
Passed
case-10 · gemini-3.6-flash · 8/6/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md203 lines4 filesincludes scripts
ToolUniverse CRISPR Screen Analysis
RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
*_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer
Pre-computed result files (CSV/TSV with names like *results*, *deseq*, *enrich*, *stats*, *_simplified.csv) → read directly and report the requested value
Canonical analysis scripts (analysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
Comprehensive skill for analyzing CRISPR-Cas9 genetic screens to identify essential genes, synthetic lethal interactions, and therapeutic targets through robust statistical analysis and pathway enrichment.
Overview
CRISPR screens enable genome-wide functional genomics by systematically perturbing genes and measuring fitness effects. This skill provides an 8-phase workflow for:
Processing sgRNA count matrices
Quality control and normalization
Gene-level essentiality scoring (MAGeCK-like and BAGEL-like approaches)
Synthetic lethality detection
Pathway enrichment analysis
Drug target prioritization with DepMap integration
Integration with expression and mutation data
Core Workflow
Phase 1: Data Import & sgRNA Count Processing
Load sgRNA count matrix (MAGeCK format or generic TSV). Expected columns: sgRNA, Gene, plus sample columns. Create experimental design table linking samples to conditions (baseline/treatment) with replicate assignments.