Babysitter nanomaterial-lims-manager

Laboratory Information Management System skill for nanomaterial sample tracking and data management

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 "$T/library/specializations/domains/science/nanotechnology/skills/nanomaterial-lims-manager" ~/.claude/skills/a5c-ai-babysitter-nanomaterial-lims-manager && rm -rf "$T"

manifest: library/specializations/domains/science/nanotechnology/skills/nanomaterial-lims-manager/SKILL.md

Nanomaterial LIMS Manager

Purpose

The Nanomaterial LIMS Manager skill provides comprehensive laboratory information management for nanomaterial research, enabling systematic sample tracking, data linking, and quality assurance throughout the development lifecycle.

Capabilities

Sample tracking and chain of custody
Synthesis parameter logging
Characterization data linking
Batch genealogy tracking
Quality control checkpoints
Regulatory documentation

Usage Guidelines

LIMS Operations

Sample Management
- Register new samples
- Track sample locations
- Maintain chain of custody
Data Integration
- Link synthesis records
- Associate characterization data
- Build batch genealogy
Quality Management
- Define QC checkpoints
- Track specifications
- Generate certificates

Process Integration

All synthesis and characterization processes
Nanomaterial Scale-Up and Process Transfer

Input Schema

{
  "operation": "register|track|query|report",
  "sample_id": "string",
  "sample_type": "nanoparticle|thin_film|device",
  "metadata": {
    "project": "string",
    "synthesized_by": "string",
    "synthesis_date": "string"
  }
}

Output Schema

{
  "sample_record": {
    "sample_id": "string",
    "status": "active|consumed|archived",
    "location": "string",
    "linked_data": [{
      "data_type": "string",
      "record_id": "string"
    }]
  },
  "genealogy": {
    "parent_batch": "string",
    "derived_samples": ["string"]
  },
  "qc_status": {
    "checkpoints_passed": "number",
    "checkpoints_total": "number",
    "status": "pass|fail|pending"
  }
}