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Nutrient Document Processing

Listed
affaan-m·vlatest·writing·MIT

Extracts key nutritional information (macros, vitamins, minerals) from food labels and ingredient lists.

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 affaan-m/ecc --skill nutrient-document-processing -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

affaan-m

Taken from the source listing. We haven't verified who controls this account.

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: api.nutrient.io.

    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

    api.nutrient.ioundeclared

    What it found (3 of 33)

    • docs/ja-JP/skills/nutrient-document-processing/SKILL.md:26contacts api.nutrient.io
      curl -X POST https://api.nutrient.io/build \
    • docs/ja-JP/skills/nutrient-document-processing/SKILL.md:33contacts api.nutrient.io
      curl -X POST https://api.nutrient.io/build \
    • docs/ja-JP/skills/nutrient-document-processing/SKILL.md:40contacts api.nutrient.io
      curl -X POST https://api.nutrient.io/build \

  • 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 affaan-m/everything-claude-code at d3b8a3e908.

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

Commit
d3b8a3e908
Manifest
skills/nutrient-document-processing/SKILL.md
Scanned
3 files, 446 lines
Copies in repo
3 — all of them scanned
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

What it does

The nutrient-document-processing skill enables AI agents to analyze and extract nutritional information from structured or unstructured documents, such as food labels, scientific reports, or dietary guidelines. It can identify key nutrients like calories, vitamins, minerals, and macronutrients, and organize them into standardized formats for easy use.

When to use it

  • Extracting nutrient data from scanned food packaging for a diet-tracking app.
  • Converting unstructured nutritional research papers into structured databases.
  • Automating the creation of meal plans based on dietary requirements by parsing nutrient content from recipes or ingredient lists.

Key capabilities

  • Parsing text and tables in documents to identify nutrients and their quantities.
  • Standardizing nutrient data across different formats and units.
  • Supporting multiple document types, including PDFs, images, and plain text files.

Example prompts

  • "Extract all the vitamins listed in this food label and convert them into a CSV format."
  • "Identify the macronutrient content of this meal plan and summarize it by percentage."
  • "Convert this nutritional research paper into a structured database with nutrient names, values, and units."

Tips & gotchas

  • Ensure documents are clear and legible for accurate parsing.
  • Some complex formatting or handwritten text may reduce accuracy, so use high-quality scans when possible.