Instrument Data To Allotrope

What it does

When your agent uses it

• Scientists need to standardize instrument data for LIMS systems
• Downstream analysis
• Include converting instrument files
• Standardizing lab data

Example prompts

• “/instrument-data-to-allotrope”

Requirements

• Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Detect instrument type from file contents (auto-detect or user-specified)
2. 2Parse file using allotropy library (native) or flexible fallback parser
3. 3Generate outputs
4. 4Deliver files with summary and usage instructions

What it can do on your machine

Read from SKILL.md and the folder at commit 9960565. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 4 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python
  • pip

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • gitlab.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Instrument Data To Allotrope loads about 2.7k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 153 tokens; SKILL.md has 818 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~153

When it runs · the whole SKILL.md, loaded when a task matches

~2.7k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aws-samples/amazon-bedrock-agents-healthcare-lifesciences at commit 9960565, republished under its Apache-2.0 licence (© aws-samples). 818 words, ~2,661 tokens.

name

instrument-data-to-allotrope

description

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.

# 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)

"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"
      }]
    }
  }]
}

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

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

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

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

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)

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

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

pip install allotropy --break-system-packages

import jsonschema
# Schema URLs in references/asm_schema_overview.md

© aws-samples, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references) in agents_catalog/36-C4LS-example-agent/C4LS/src/skills/instrument-data-to-allotrope of aws-samples/amazon-bedrock-agents-healthcare-lifesciences.

• SKILL.md
• LICENSE.txt
• references/asm_schema_overview.md
• references/field_classification_guide.md
• references/flattening_guide.md
• references/supported_instruments.md
• requirements.txt
• scripts/convert_to_asm.py
• scripts/export_parser.py
• scripts/flatten_asm.py
• scripts/validate_asm.py

Open the folder on GitHubat commit 9960565

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in aws-samples/amazon-bedrock-agents-healthcare-lifesciences, which our catalogue first saw on October 7, 2026.

• FreedomIntelligence/OpenClaw-Medical-Skillsskills/instrument-data-to-allotrope
• BioTender-max/awesome-bio-agent-skillsskills/openclaw/instrument-data-to-allotrope