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Large File Refactorer

Listed
alcyone-labs·vlatest·backend

This tool automatically refactors large files for improved code quality and maintainability, saving developers time and effort.

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 alcyone-labs/agent-skills --skill large-file-refactorer -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

alcyone-labs

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 pipe-to-shell installers: failed

    Runs code fetched at install time in 8 places.

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

    What it found (3 of 8)

    • skills/large-file-refactorer/README.md:25remote script piped into a shell
      curl -fsSL https://raw.githubusercontent.com/Alcyone-Labs/large-file-refactorer/main/install.sh | bash -s -- --global
    • skills/large-file-refactorer/README.md:31remote script piped into a shell
      curl -fsSL https://raw.githubusercontent.com/Alcyone-Labs/large-file-refactorer/main/install.sh | bash -s -- --local
    • skills/large-file-refactorer/README.md:38remote script piped into a shell
      curl -fsSL https://raw.githubusercontent.com/Alcyone-Labs/large-file-refactorer/main/install.sh | bash -s -- --global --opencode

  • 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

    Only contacts known or declared hosts: raw.githubusercontent.com.

    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

    raw.githubusercontent.comsource host

  • 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.

  • Pinned to a commit: passed

    Scanned alcyone-labs/agent-skills at 84ef45cb67.

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

Commit
84ef45cb67
Manifest
skills/large-file-refactorer/SKILL.md
Scanned
8 files, 1,382 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

What it does

The large-file-refactorer skill helps to break down large files into smaller, more manageable chunks for easier processing and analysis. It can split files based on size or content patterns, allowing agents to work with datasets that exceed typical memory limits. This facilitates tasks like incremental data loading and parallel processing of file segments.

When to use it

  • Processing massive log files: When analyzing server logs exceeding available RAM.
  • Splitting large CSVs for batch import: To load a very large CSV into a database in smaller, more efficient batches.
  • Refactoring monolithic codebases: Breaking down extremely long source code files to improve readability and maintainability.
  • Analyzing video or audio files: Dividing large media files into segments for individual processing or feature extraction.

Key capabilities

  • File splitting by size
  • File splitting based on content patterns
  • Incremental data loading support
  • Parallel processing of file segments

Example prompts

  • "Split this 10GB log file into chunks no larger than 500MB."
  • "Refactor this codebase, breaking down files longer than 2000 lines."
  • “Divide this CSV file into smaller files based on the presence of a new header row every 1000 rows.”

Tips & gotchas

The skill requires access to the file system where the large file is located. Be mindful of potential performance bottlenecks when splitting very large files, as it may involve significant disk I/O.