instrument-data-to-allotrope

仓库: anthropics/knowledge-work-plugins
安装量: 147
排名: #5869

安装

npx skills add https://github.com/anthropics/knowledge-work-plugins --skill instrument-data-to-allotrope

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

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 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: 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 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 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: 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.md

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