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

Detect instrument type from file contents (auto-detect or user-specified)
Parse file using allotropy library (native) or flexible fallback parser
Generate outputs:
- ASM JSON (full semantic structure)
- Flattened CSV (2D tabular format)
- Python parser code (for data engineer handoff)
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

python

# 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

json

"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:

Instrument	Calculated Fields
Cell counter	Viability %, cell density dilution-adjusted values
Spectrophotometer	Concentration (from absorbance), 260/280 ratio
Plate reader	Concentrations from standard curve, %CV
Electrophoresis	DIN/RIN, region concentrations, average sizes
qPCR	Relative quantities, fold change

See

references/field_classification_guide.md

for detailed guidance on raw vs. calculated classification.

Validation

Always validate ASM output before delivering to the user:

bash

python scripts/validate_asm.py output.json
python scripts/validate_asm.py output.json --reference known_good.json  # Compare to reference
python scripts/validate_asm.py output.json --strict  # Treat warnings as errors

Validation Rules:

Based on Allotrope ASM specification (December 2024)
Last updated: 2026-01-07
Source: https://gitlab.com/allotrope-public/asm

Soft Validation Approach: Unknown techniques, units, or sample roles generate warnings (not errors) to allow for forward compatibility. If Allotrope adds new values after December 2024, the validator won't block them—it will flag them for manual verification. Use

--strict

mode to treat warnings as errors if you need stricter validation.

What it checks:

Correct technique selection (e.g., multi-analyte profiling vs plate reader)
Field naming conventions (space-separated, not hyphenated)
Calculated data has traceability (
```
data-source-aggregate-document
```
)
Unique identifiers exist for measurements and calculated values
Required metadata present
Valid units and sample roles (with soft validation for unknown values)

Supported Instruments

See

references/supported_instruments.md

for complete list. Key instruments:

Category	Instruments
Cell Counting	Vi-CELL BLU, Vi-CELL XR, NucleoCounter
Spectrophotometry	NanoDrop One/Eight/8000, Lunatic
Plate Readers	SoftMax Pro, EnVision, Gen5, CLARIOstar
ELISA	SoftMax Pro, BMG MARS, MSD Workbench
qPCR	QuantStudio, Bio-Rad CFX
Chromatography	Empower, Chromeleon

Detection & Parsing Strategy

Tier 1: Native allotropy parsing (PREFERRED)

Always try allotropy first. Check available vendors directly:

python

from allotropy.parser_factory import Vendor

# List all supported vendors
for v in Vendor:
    print(f"{v.name}")

# Common vendors:
# AGILENT_TAPESTATION_ANALYSIS  (for TapeStation XML)
# BECKMAN_VI_CELL_BLU
# THERMO_FISHER_NANODROP_EIGHT
# MOLDEV_SOFTMAX_PRO
# APPBIO_QUANTSTUDIO
# ... many more

When the user provides a file, check if allotropy supports it before falling back to manual parsing. The

scripts/convert_to_asm.py

auto-detection only covers a subset of allotropy vendors.

Tier 2: Flexible fallback parsing

Only use if allotropy doesn't support the instrument. This fallback:

Does NOT generate
```
calculated-data-aggregate-document
```
Does NOT include full traceability
Produces simplified ASM structure

Use flexible parser with:

Column name fuzzy matching
Unit extraction from headers
Metadata extraction from file structure

Tier 3: PDF extraction

For PDF-only files, extract tables using pdfplumber, then apply Tier 2 parsing.

Pre-Parsing Checklist

Before writing a custom parser, ALWAYS:

Check if allotropy supports it - Use native parser if available
Find a reference ASM file - Check
```
references/examples/
```
or ask user
Review instrument-specific guide - Check
```
references/instrument_guides/
```
Validate against reference - Run
```
validate_asm.py --reference <file>
```

Common Mistakes to Avoid

Mistake	Correct Approach
Manifest as object	Use URL string
Lowercase detection types	Use "Absorbance" not "absorbance"
"emission wavelength setting"	Use "detector wavelength setting" for emission
All measurements in one document	Group by well/sample location
Missing procedure metadata	Extract ALL device settings per measurement

Code Export for Data Engineers

Generate standalone Python scripts that scientists can hand off:

python

# Export parser code
python scripts/export_parser.py --input "data.csv" --vendor "VI_CELL_BLU" --output "parser_script.py"

The exported script:

Has no external dependencies beyond pandas/allotropy
Includes inline documentation
Can run in Jupyter notebooks
Is production-ready for data pipelines

File Structure

instrument-data-to-allotrope/
├── SKILL.md                          # This file
├── scripts/
│   ├── convert_to_asm.py            # Main conversion script
│   ├── flatten_asm.py               # ASM → 2D CSV conversion
│   ├── export_parser.py             # Generate standalone parser code
│   └── validate_asm.py              # Validate ASM output quality
└── references/
    ├── supported_instruments.md     # Full instrument list with Vendor enums
    ├── asm_schema_overview.md       # ASM structure reference
    ├── field_classification_guide.md # Where to put different field types
    └── flattening_guide.md          # How flattening works

Usage Examples

Example 1: Vi-CELL BLU file

User: "Convert this cell counting data to Allotrope format"
[uploads viCell_Results.xlsx]

Claude:
1. Detects Vi-CELL BLU (95% confidence)
2. Converts using allotropy native parser
3. Outputs:
   - viCell_Results_asm.json (full ASM)
   - viCell_Results_flat.csv (2D format)
   - viCell_parser.py (exportable code)

Example 2: Request for code handoff

User: "I need to give our data engineer code to parse NanoDrop files"

Claude:
1. Generates self-contained Python script
2. Includes sample input/output
3. Documents all assumptions
4. Provides Jupyter notebook version

Example 3: LIMS-ready flattened output

User: "Convert this ELISA data to a CSV I can upload to our LIMS"

Claude:
1. Parses plate reader data
2. Generates flattened CSV with columns:
   - sample_identifier, well_position, measurement_value, measurement_unit
   - instrument_serial_number, analysis_datetime, assay_type
3. Validates against common LIMS import requirements

Implementation Notes

Installing allotropy

bash

pip install allotropy --break-system-packages

Handling parse failures

If allotropy native parsing fails:

Log the error for debugging
Fall back to flexible parser
Report reduced metadata completeness to user
Suggest exporting different format from instrument

ASM Schema Validation

Validate output against Allotrope schemas when available:

python

import jsonschema
# Schema URLs in references/asm_schema_overview.md

instrument-data-to-allotrope

NPX Install

Tags

SKILL.md Content

Instrument Data to Allotrope Converter

Workflow Overview

Quick Start

Output Format Selection

Calculated Data Handling

Validation

Supported Instruments

Detection & Parsing Strategy

Tier 1: Native allotropy parsing (PREFERRED)

Tier 2: Flexible fallback parsing

Tier 3: PDF extraction

Pre-Parsing Checklist

Common Mistakes to Avoid

Code Export for Data Engineers

File Structure

Usage Examples

Example 1: Vi-CELL BLU file

Example 2: Request for code handoff

Example 3: LIMS-ready flattened output

Implementation Notes

Installing allotropy

Handling parse failures

ASM Schema Validation