Babysitter targeting-ligand-designer

Active targeting skill for designing and validating nanoparticle targeting strategies

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/nanotechnology/skills/targeting-ligand-designer" ~/.claude/skills/a5c-ai-babysitter-targeting-ligand-designer && rm -rf "$T"

manifest: library/specializations/domains/science/nanotechnology/skills/targeting-ligand-designer/SKILL.md

Targeting Ligand Designer

Purpose

The Targeting Ligand Designer skill provides systematic design of active targeting strategies for nanoparticle drug delivery, enabling selection and validation of targeting moieties for specific cellular or tissue targets.

Capabilities

Targeting ligand selection (antibodies, peptides, aptamers)
Conjugation chemistry optimization
Binding affinity assessment
Biodistribution prediction
Receptor expression analysis
In vitro targeting validation

Usage Guidelines

Targeting Design

Ligand Selection
- Identify target receptor
- Evaluate ligand options
- Consider size and stability
Conjugation Optimization
- Select chemistry
- Optimize ligand density
- Preserve binding activity
Validation
- Measure binding affinity
- Test cellular uptake
- Assess selectivity

Process Integration

Nanoparticle Drug Delivery System Development
Nanosensor Development and Validation Pipeline

Input Schema

{
  "target_receptor": "string",
  "cell_type": "string",
  "nanoparticle_type": "string",
  "ligand_candidates": ["string"],
  "required_specificity": "number (fold)"
}

Output Schema

{
  "recommended_ligand": {
    "name": "string",
    "type": "antibody|peptide|aptamer|small_molecule",
    "Kd": "number (nM)"
  },
  "conjugation_strategy": {
    "chemistry": "string",
    "ligand_density": "number (ligands/NP)",
    "orientation": "string"
  },
  "predicted_performance": {
    "specificity": "number (fold)",
    "uptake_enhancement": "number (fold)"
  }
}