Automate sgRNA selection, scoring, off-target evaluation, and oligo generation for CRISPR experiments using the documented workflow.
Scanned 2/12/2026
Install via CLI
openskills install mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills----
name: crispr-guide-design
description: Automate sgRNA selection, scoring, off-target evaluation, and oligo generation for CRISPR experiments using the documented workflow.
---
## At-a-Glance
- **description (10-20 chars):** Guide foundry
- **keywords:** CRISPR, sgRNA, Doench, off-target, oligos
- **measurable_outcome:** Return the requested number of guides (default ≥4) with efficiency + specificity scores, coordinates, and cloning oligos within 10 minutes per gene.
- **license:** MIT
- **version:** 1.0.0 (Python 3.10+)
- **allowed-tools:** `run_shell_command`, `read_file`
## When to Use
- Designing CRISPR knockout/knock-in experiments that need validated guides.
- Locating all PAM-compatible target sites in a gene or locus.
- Filtering guides by efficiency/off-target metrics before cloning.
## Core Capabilities
1. **Target discovery:** Scan sequences for PAM motifs (e.g., NGG).
2. **Efficiency scoring:** Evaluate GC content, homopolymers, Doench/DeepCRISPR/CFD scores.
3. **Filtering & ranking:** Remove risky guides (SNP overlap, off-target hits) and output the best candidates.
## Workflow
1. Resolve gene symbol + organism to canonical transcript coordinates and target region.
2. Enumerate PAM-compatible sites; extract spacers for the chosen Cas variant.
3. Score guides (efficiency + specificity) and compute GC metrics.
4. Run off-target search (≤3 mismatches) to flag problematic loci.
5. Filter/rank guides, generate cloning oligos/primers, and emit JSON/CSV outputs with coordinates.
## Example Usage
```bash
python3 Skills/Genomics/CRISPR_Design_Agent/crispr_designer.py \
--sequence "ATGGAGGAGCCGCAGTCAGATCCTAGCGTCGAGCCCCCTCTGAGTCAGGAAACATTTTCAGACCTATGGAAACTGTGAGTGGATCCATTGGAAGGGC" \
--output guides.json
```
## Guardrails
- Always state genome build and Cas variant assumptions.
- Avoid guides overlapping common SNPs when `avoid_variants` is true.
- Flag high off-target density near coding regions for manual review.
## References
- See `README.md` and `prompt.md` for detailed schema plus supporting literature.
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