Targeting ligand designer

Skill a5c-ai/babysitter/library/specializations/domains/science/nanotechnology/skills/targeting-ligand-designer

Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration

Install

npx -y skills add a5c-ai/babysitter --skill targeting-ligand-designer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Active targeting skill for designing and validating nanoparticle targeting strategies

SKILL.md

2.2 KB, as published. Nobody here has run it

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)"
  }
}

Gives 0 of the 12 instructions most design frontend skills give

Counted across 1,170 of the 1,878 authors here whose files we hold, read 2026-08-06

use css variables for color consistencyin 73 of 1170, across 24 files
match implementation complexity to the aesthetic visionin 70 of 1170, across 20 files
commit to one bold aesthetic direction before codingin 70 of 1170, across 25 files
add atmospheric background effects and texturesin 58 of 1170, across 10 files
use unexpected spatial compositions and layoutsin 55 of 1170, across 7 files
implement real working codein 55 of 1170, across 7 files
vary themes and aesthetics across different designsin 48 of 1170, across 7 files
launch chromium in headless modein 47 of 1170, across 4 files
close the browser when donein 47 of 1170, across 4 files
run provided scripts with help flag firstin 47 of 1170, across 4 files
use descriptive selectors for elementsin 47 of 1170, across 4 files
wait for network idle statein 46 of 1170, across 3 files

Said here and by no other author read

Identify the target receptor
Evaluate ligand options
Consider ligand size and stability
Select conjugation chemistry
Optimize ligand density
Preserve ligand binding activity

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.