Targeting Ligand Designer

SkillDev tools

Active targeting skill for designing and validating nanoparticle targeting strategies

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Targeting Ligand Designer skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/nanotechnology/skills/targeting-ligand-designer/SKILL.md and read by ahel’s review.

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)"
  }
}

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: targeting-ligand-designer
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter