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.
npx skills add fergupa/claude_plugins --skill instrument-data-to-allotropegit clone --depth 1 https://github.com/fergupa/claude_pluginsWrote 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.
[](https://agentmods.dev/skills/fergupa/claude_plugins/instrument-data-to-allotrope)<a href="https://agentmods.dev/skills/fergupa/claude_plugins/instrument-data-to-allotrope"><img src="https://agentmods.dev/badge/skills/fergupa/claude_plugins/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.
<a href="https://agentmods.dev/skills/fergupa/claude_plugins/instrument-data-to-allotrope"><img src="https://agentmods.dev/badge/skills/fergupa/claude_plugins/instrument-data-to-allotrope.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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 10d ago.
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.
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.
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 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
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- LICENSE.txt 10 KB
- references/asm_schema_overview.md 6.0 KB
- references/field_classification_guide.md 17 KB
- references/flattening_guide.md 7.3 KB
- references/supported_instruments.md 5.2 KB
- requirements.txt 811 B
- scripts/convert_to_asm.py 17 KB runs code
- scripts/export_parser.py 14 KB runs code
- scripts/flatten_asm.py 7.2 KB runs code
- scripts/validate_asm.py 36 KB runs code
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.
- 10d ago First seen · 281 lines · 123 tokens per session scan A 6167bf1731e5
instrument-data-to-allotrope is a skill published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 7mo 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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