How do I install the Crispr Guide Design skill?

Run npx skillmds@latest add biotender-max/crispr-guide-design in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Crispr Guide Design skill do?

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Is Crispr Guide Design safe to use?

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Which AI agents work with Crispr Guide Design?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Crispr Guide Design free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Crispr Guide Design?

BioTender-max (@biotender-max) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Crispr Guide Design by biotender-max

Name: Crispr Guide Design
Author: BioTender-max

File contents

name: crispr-guide-design description: 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 metadata: author: CRISPR-GPT Team version: "1.0.0" compatibility: - system: Python 3.10+ allowed-tools: - run_shell_command - read_file

CRISPR Design Agent

Automate sgRNA selection, scoring, off-target evaluation, and oligo generation for CRISPR experiments using the documented workflow.

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

Target discovery: Scan sequences for PAM motifs (e.g., NGG).
Efficiency scoring: Evaluate GC content, homopolymers, Doench/DeepCRISPR/CFD scores.
Filtering & ranking: Remove risky guides (SNP overlap, off-target hits) and output the best candidates.

Workflow

Resolve gene symbol + organism to canonical transcript coordinates and target region.
Enumerate PAM-compatible sites; extract spacers for the chosen Cas variant.
Score guides (efficiency + specificity) and compute GC metrics.
Run off-target search (≤3 mismatches) to flag problematic loci.
Filter/rank guides, generate cloning oligos/primers, and emit JSON/CSV outputs with coordinates.

Example Usage

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.

---name: crispr-guide-designdescription: ---name: crispr-guide-designdescription: Guide foundrykeywords: - CRISPR - sgRNA - Doench - off-target - oligosmeasurable_outcome: Return the requested number of guides (default ≥4) with efficiency + specificity scores, coordinates, and cloning oligos within 10 minutes per gene.license: MITmetadata: author: CRISPR-GPT Team version: "1.0.0"compatibility: - system: Python 3.10+allowed-tools: - run_shell_command - read_file---# CRISPR Design AgentAutomate sgRNA selection, scoring, off-target evaluation, and oligo generation for CRISPR experiments using the documented workflow.## 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 Capabilities1. **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.## Workflow1. 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```bashpython3 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.

Frequently asked questions

Crispr Guide Design