instrument-data-to-allotrope

instrument-data-to-allotrope is a skill for Claude Code, Codex from Shoko-official/Claude-Science-System-Prompts. It costs 123 tokens per session (2,419 once invoked), scanned A, a copy of instrument-data-to-allotrope, Apache-2.0.

A data-conversion workflow for turning laboratory instrument files such as PDFs, spreadsheets, CSVs, and text files into standardized Allotrope Simple Model JSON or flat CSV files.

In plain words
What is it for?
Use it to detect instrument types, parse their output, create standardized files, and generate Python parsers for supported formats.
Why use it?
It reduces manual reformatting when instrument results need to enter a LIMS, data lake, or another analysis system.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to detect instrument types, parse their output, create standardized files, and generate Python parsers for supported formats.

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Install with agentmods
npx agentmods add skills/shoko-official/claude-science-system-prompts/instrument-data-to-allotrope
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Shoko-official/Claude-Science-System-Prompts --skill instrument-data-to-allotrope
Clone the repo
git clone --depth 1 https://github.com/Shoko-official/Claude-Science-System-Prompts

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for instrument-data-to-allotrope

README.md
[![agentmods](https://agentmods.dev/badge/skills/shoko-official/claude-science-system-prompts/instrument-data-to-allotrope/github.svg)](https://agentmods.dev/skills/shoko-official/claude-science-system-prompts/instrument-data-to-allotrope)
Your own site
<a href="https://agentmods.dev/skills/shoko-official/claude-science-system-prompts/instrument-data-to-allotrope"><img src="https://agentmods.dev/badge/skills/shoko-official/claude-science-system-prompts/instrument-data-to-allotrope/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for instrument-data-to-allotrope

Your own site · 80×15
<a href="https://agentmods.dev/skills/shoko-official/claude-science-system-prompts/instrument-data-to-allotrope"><img src="https://agentmods.dev/badge/skills/shoko-official/claude-science-system-prompts/instrument-data-to-allotrope.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 123 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,419 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00123 $0.02419
Opus 5 $0.00062 $0.01210
Sonnet 5 $0.00025 $0.00484
Haiku 4.5 $0.00012 $0.00242

Measured 12d ago against content hash 6167bf1731e5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

instrument-data-to-allotrope scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/convert_to_asm.py, scripts/export_parser.py, scripts/flatten_asm.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to instrument-data-to-allotrope — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/instrument-data-to-allotrope/SKILL.md · 281 lines

How it starts

The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 281 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 281 lines · 123 tokens per session scan A 6167bf1731e5

Subscribe to this mod's changes

instrument-data-to-allotrope is a skill published in the GitHub repository Shoko-official/Claude-Science-System-Prompts (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 123 tokens to every session and 2,419 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to instrument-data-to-allotrope, differing in 0 lines, and is treated as a copy.

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