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Prototype To Production

Checked
ariegoldkin·vlatest·frontend·MIT

This skill rapidly transforms AI prototypes into deployable applications, streamlining development and accelerating time-to-market for your projects.

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 ariegoldkin/ai-agent-hub --skill prototype-to-production -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

ariegoldkin

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

    Only contacts known or declared hosts: www.w3.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

    www.w3.orgstandards

  • 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 ariegoldkin/ai-agent-hub at 758a7f7453.

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

Commit
758a7f7453
Manifest
skills/prototype-to-production/SKILL.md
Scanned
5 files, 1,003 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

This skill helps bridge the gap between experimental prototypes and production-ready code. It focuses on identifying potential issues, suggesting optimizations for scalability, and ensuring adherence to best practices for deployment. The tool analyzes prototype code, generates a report of areas needing attention, and can automatically refactor code snippets to improve robustness.

When to use it

  • You've built a functional AI agent prototype but are unsure how to deploy it safely and efficiently.
  • Your team wants to ensure that new features meet production standards before release.
  • You need assistance in identifying performance bottlenecks or potential security vulnerabilities in your code.
  • You want to automate some of the tedious refactoring tasks involved in moving from prototype to production.

Key capabilities

  • Code analysis and reporting
  • Scalability optimization suggestions
  • Best practices adherence checks
  • Automated code refactoring

Example prompts

  • "Analyze this Python script for potential deployment issues."
  • "Suggest optimizations for scaling my AI agent's API endpoint."
  • "Refactor this JavaScript function to improve its robustness and readability."

Tips & gotchas

The skill is most effective when provided with well-documented code. While it can identify many common problems, manual review of the generated reports remains crucial for ensuring quality.