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Instrument Data To Allotrope

AnthropicOfficialPublished by Anthropic

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
— the description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill automates the transformation of raw laboratory instrument output files (such as PDF, CSV, Excel, or TXT) into the standardized Allotrope Simple Model (ASM) JSON format. It supports auto-detection of various instrument types, provides flattened 2D CSV outputs for easier analysis, and generates production-ready Python parser code for data engineering pipelines. Designed for scientists and data engineers, it ensures data consistency for LIMS systems, data lakes, and downstream analysis.

What it does

  • Auto-detects instrument types from input files.
  • Parses instrument data into semantic ASM JSON structures.
  • Generates flattened 2D CSV files for tabular analysis.
  • Produces exportable, standalone Python parser code.
  • Validates output against Allotrope ASM specifications.
  • Handles traceability for calculated data fields.

When to use it

  • Standardizing heterogeneous lab data for LIMS or ELN systems.
  • Preparing instrument data for ingestion into data lakes.
  • Creating reproducible parsers for data engineering pipelines.
  • Converting proprietary instrument outputs into a vendor-neutral format.
  • Performing quick analysis on instrument data using tabular formats.

Advantages

  • Ensures semantic consistency using the Allotrope standard.
  • Reduces manual effort in data cleaning and transformation.
  • Provides both machine-readable JSON and human-readable CSV outputs.
  • Facilitates data traceability for calculated results.
  • Supports extensibility for custom instrument formats.

Usage examples

“Convert this cell counting data to Allotrope format”
The agent detects the instrument, parses the file using the native allotropy library, and returns the ASM JSON, a flattened CSV, and a custom Python parser script.
“I need to give our data engineer code to parse NanoDrop files”
The agent generates a self-contained, documented Python script tailored for the specific instrument, ready for integration into a data pipeline.
“Convert this ELISA data to a CSV I can upload to our LIMS”
The agent parses the plate reader data and outputs a flattened CSV with standardized columns and metadata, validated for LIMS import requirements.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/anthropics/instrument-data-to-allotrope/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip instrument-data-to-allotrope.zip -d .agents/skills

Documentation by Anthropic

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:

InstrumentCalculated Fields
Cell counterViability %, cell density dilution-adjusted values
SpectrophotometerConcentration (from absorbance), 260/280 ratio
Plate readerConcentrations from standard curve, %CV
ElectrophoresisDIN/RIN, region concentrations, average sizes
qPCRRelative 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:

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:

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:

CategoryInstruments
Cell CountingVi-CELL BLU, Vi-CELL XR, NucleoCounter
SpectrophotometryNanoDrop One/Eight/8000, Lunatic
Plate ReadersSoftMax Pro, EnVision, Gen5, CLARIOstar
ELISASoftMax Pro, BMG MARS, MSD Workbench
qPCRQuantStudio, Bio-Rad CFX
ChromatographyEmpower, Chromeleon

Detection & Parsing Strategy

Tier 1: Native allotropy parsing (PREFERRED)

Always try allotropy first. Check available vendors directly:

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:

  1. Check if allotropy supports it - Use native parser if available

  2. Find a reference ASM file - Check references/examples/ or ask user

  3. Review instrument-specific guide - Check references/instrument_guides/

  4. Validate against reference - Run validate_asm.py --reference <file>

Common Mistakes to Avoid

MistakeCorrect Approach
Manifest as objectUse URL string
Lowercase detection typesUse "Absorbance" not "absorbance"
"emission wavelength setting"Use "detector wavelength setting" for emission
All measurements in one documentGroup by well/sample location
Missing procedure metadataExtract ALL device settings per measurement

Code Export for Data Engineers

Generate standalone Python scripts that scientists can hand off:

# 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
pip install allotropy --break-system-packages
Handling parse failures

If allotropy native parsing fails:

  1. Log the error for debugging

  2. Fall back to flexible parser

  3. Report reduced metadata completeness to user

  4. Suggest exporting different format from instrument

ASM Schema Validation

Validate output against Allotrope schemas when available:

import jsonschema
# Schema URLs in references/asm_schema_overview.md

Files

LICENSE.txt

Apache License
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http://www.apache.org/licenses/

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Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python

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Attribution and licence

Published by
Anthropic
Publisher
Anthropic (Official)
Source
anthropics/knowledge-work-plugins · bio-research/skills/instrument-data-to-allotrope · commit e050c2e
Licence
Apache License 2.0 (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
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