Agent skill

nanomaterial-lims-manager

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

Stars 514
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/nanotechnology/skills/nanomaterial-lims-manager

Metadata

Additional technical details for this skill

phase
6
domain
science
category
infrastructure-quality
priority
high
specialization
nanotechnology
tools libraries
[
    "LIMS systems",
    "ELN platforms"
]

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

  1. Sample Management

    • Register new samples
    • Track sample locations
    • Maintain chain of custody
  2. Data Integration

    • Link synthesis records
    • Associate characterization data
    • Build batch genealogy
  3. Quality Management

    • Define QC checkpoints
    • Track specifications
    • Generate certificates

Process Integration

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

Input Schema

json
{
  "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

json
{
  "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"
  }
}

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