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データ・研究 · SKILL.md

nanomaterial-lims-manager

nanomaterial-lims-manager

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

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: nanomaterial-lims-manager description: Laboratory Information Management System skill for nanomaterial sample tracking and data management allowed-tools:

  • Read
  • Write
  • Glob
  • Grep
  • Bash metadata: specialization: nanotechnology domain: science category: infrastructure-quality priority: high phase: 6 tools-libraries:
    • LIMS systems
    • ELN platforms graph: domains: [domain:nanotechnology] skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis] workflows: [workflow:experiment-design] roles: [role:research-engineer]

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

{
  "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"
  }
}
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