# Nanomaterial Lims Manager

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

- Skill: `a5c-ai/nanomaterial-lims-manager` (Agent Skill)
- Install (CLI): `npx skillmds@latest add a5c-ai/nanomaterial-lims-manager`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/nanomaterial-lims-manager/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/nanomaterial-lims-manager

---


# 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"
  }
}
```