Instrument Data To Allotrope
AnthropicOfficialPublished by Anthropic
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/instrument-data-to-allotrope/downloadUnzip 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 mappingsUse 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 standardsThis 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.mdfor 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:
| 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:
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 errorsValidation Rules:
Based on Allotrope ASM specification (December 2024)
Last updated: 2026-01-07
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:
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 moreWhen 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-documentDoes 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 userReview 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:
# 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 worksUsage 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 versionExample 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 requirementsImplementation Notes
Installing allotropy
pip install allotropy --break-system-packagesHandling 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:
import jsonschema
# Schema URLs in references/asm_schema_overview.mdFiles
- ATTRIBUTION.mdAdded by AutoGineer1004 B
- LICENSE.txt10.0 KiB
- SKILL.md10.5 KiB
- references/asm_schema_overview.md6.0 KiB
- references/field_classification_guide.md17.3 KiB
- references/flattening_guide.md7.3 KiB
- references/supported_instruments.md5.2 KiB
- requirements.txt811 B
- scripts/convert_to_asm.pyScript16.9 KiB
- scripts/export_parser.pyScript13.5 KiB
- scripts/flatten_asm.pyScript7.2 KiB
- scripts/validate_asm.pyScript35.9 KiB
LICENSE.txt
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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)
- Synced
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