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Metadata Optimization

Checked
eronred·vlatest·other·MIT

Automatically refines file metadata (tags, descriptions, titles) to improve searchability and organization across platforms.

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 appeeky/aso-skills --skill metadata-optimization -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

eronred

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 eronred/aso-skills at 8de1ee18d8.

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

Commit
8de1ee18d8
Manifest
skills/metadata-optimization/SKILL.md
Scanned
1 file, 169 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 metadata-optimization skill allows AI agents to automatically refine and enhance data attributes associated with their tasks or outputs. It ensures that information is structured, searchable, and aligned with best practices for downstream processing or human consumption.

When to use it

  • Preparing datasets before feeding them into analysis models to improve accuracy.
  • Enhancing searchability of generated reports or logs within a knowledge base.
  • Standardizing variable names or tags across different project modules.
  • Optimizing content metadata for better indexing by external systems or APIs.

Key capabilities

  • Automatic refinement of data attributes and tags.
  • Structuring unorganized information into usable formats.
  • Aligning output metadata with specific schema requirements.

Example prompts

  • "Optimize the metadata for this dataset to ensure it is compatible with our analytics pipeline."
  • "Refine the tags and descriptions of these generated reports for better internal searchability."
  • "Standardize the variable names and attribute definitions in this configuration file."

Tips & gotchas

This skill relies on clear input data; providing well-structured initial information yields better optimization results. It is most effective when used as a preprocessing step before complex analysis or storage operations.