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Instrument Data To Allotrope

Official
anthropics·vlatest·other·Apache-2.0

Transforms raw instrument data into actionable allotrope recommendations for material science applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add anthropics/knowledge-work-plugins --skill instrument-data-to-allotrope -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

anthropics

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: purl.allotrope.org.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    purl.allotrope.orgundeclaredgitlab.comsource host

    What it found (3 of 4)

    • bio-research/skills/instrument-data-to-allotrope/references/asm_schema_overview.md:17contacts purl.allotrope.org
      "vocabulary": ["http://purl.allotrope.org/voc/afo/REC/2023/09/"],
    • bio-research/skills/instrument-data-to-allotrope/references/asm_schema_overview.md:18contacts purl.allotrope.org
      "contexts": ["http://purl.allotrope.org/json-ld/afo-context-REC-2023-09.jsonld"]
    • bio-research/skills/instrument-data-to-allotrope/scripts/convert_to_asm.py:322contacts purl.allotrope.org
      "vocabulary": ["http://purl.allotrope.org/voc/afo/REC/2023/09/"],

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned anthropics/knowledge-work-plugins at da38ec1ee8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
da38ec1ee8
Manifest
bio-research/skills/instrument-data-to-allotrope/SKILL.md
Scanned
11 files, 3,713 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The instrument-data-to-allotrope skill converts raw experimental data into the standardized Allotrope format, enabling AI agents to process and analyze complex scientific datasets with precision. It handles various input types by mapping them to a consistent schema that supports downstream tasks like visualization or further computational analysis.

When to use it

  • Preparing raw sensor readings or lab results for integration into scientific knowledge graphs.
  • Standardizing heterogeneous data sources to ensure compatibility with Allotrope-based tools.
  • Facilitating automated data processing pipelines in chemistry, materials science, or biology research.
  • Generating structured metadata from unstructured experimental logs for better AI agent understanding.

Key capabilities

  • Transforms diverse input formats into the canonical Allotrope structure.
  • Preserves critical experimental context and measurement units during conversion.
  • Enables seamless interoperability between different scientific data ecosystems.

Example prompts

  • "Convert this CSV file of temperature measurements into Allotrope format for my analysis pipeline."
  • "Instrument this raw JSON dataset containing material properties so it can be queried by an AI agent."
  • "Transform these experimental logs into a standardized Allotrope document to share with collaborators."

Tips & gotchas

Ensure your input data includes necessary metadata (units, timestamps, sources) for accurate transformation. This skill is specialized for scientific data; using it on non-structured text may yield unexpected results.