Instrument Data to Allotrope Converter

Convert instrument files into standardized Allotrope Simple Model (ASM) format for LIMS upload, data lakes, or handoff to data engineering teams.

Note: This is an Example Skill

This skill demonstrates how skills can support your data engineering tasks—automating schema transformations, parsing instrument outputs, and generating production-ready code.

To customize for your organization:

• Modify the references/ files to include your company's specific schemas or ontology mappings
• Use an MCP server to connect to systems that define your schemas (e.g., your LIMS, data catalog, or schema registry)
• Extend the scripts/ to handle proprietary instrument formats or internal data standards

This pattern can be adapted for any data transformation workflow where you need to convert between formats or validate against organizational standards.

Workflow Overview

1. Detect instrument type from file contents (auto-detect or user-specified)
2. Parse file using allotropy library (native) or flexible fallback parser
3. Generate outputs:
   • ASM JSON (full semantic structure)
   • Flattened CSV (2D tabular format)
   • Python parser code (for data engineer handoff)
4. Deliver files with summary and usage instructions

When Uncertain: If you're unsure how to map a field to ASM (e.g., is this raw data or calculated? device setting or environmental condition?), ask the user for clarification. Refer to references/field_classification_guide.md for guidance, but when ambiguity remains, confirm with the user rather than guessing.

Quick Start

# Install requirements first
pip install allotropy pandas openpyxl pdfplumber --break-system-packages

# Core conversion
from allotropy.parser_factory import Vendor
from allotropy.to_allotrope import allotrope_from_file

# Convert with allotropy
asm = allotrope_from_file("instrument_data.csv", Vendor.BECKMAN_VI_CELL_BLU)

Output Format Selection

ASM JSON (default) - Full semantic structure with ontology URIs

• Best for: LIMS systems expecting ASM, data lakes, long-term archival
• Validates against Allotrope schemas

Flattened CSV - 2D tabular representation

• Best for: Quick analysis, Excel users, systems without JSON support
• Each measurement becomes one row with metadata repeated

Both - Generate both formats for maximum flexibility

Calculated Data Handling

IMPORTANT: Separate raw measurements from calculated/derived values.

• Raw data → measurement-document (direct instrument readings)
• Calculated data → calculated-data-aggregate-document (derived values)

Calculated values MUST include traceability via data-source-aggregate-document:

"calculated-data-aggregate-document": {
  "calculated-data-document": [{
    "calculated-data-identifier": "SAMPLE_B1_DIN_001",
    "calculated-data-name": "DNA integrity number",
    "calculated-result": {"value": 9.5, "unit": "(unitless)"},
    "data-source-aggregate-document": {
      "data-source-document": [{
        "data-source-identifier": "SAMPLE_B1_MEASUREMENT",
        "data-source-feature": "electrophoresis trace"
      }]
    }
  }]
}

Common calculated fields by instrument type:

See references/field_classification_guide.md for detailed guidance on raw vs. calculated classification.

Validation

Always validate ASM output before delivering to the user:

python scripts/validate_asm.py output.json
python scripts/validate_asm.py output.json --reference known_good.json  # Compare to reference

…