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Uv Moe Training

Checked
uv-xiao·vlatest·ai-ml·MIT

Uv Moe Training guides AI models to generate charming, cute (moe) aesthetics and styles for creative content generation.

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 uv-xiao/pkbllm --skill uv-moe-training -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

uv-xiao

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 uv-xiao/pkbllm at 5477767f22.

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

Commit
5477767f22
Manifest
knowledge/ML/model-architecture/moe-training/SKILL.md
Scanned
4 files, 1,613 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 uv-moe-training skill enables AI agents to efficiently train Mixture of Experts (MoE) models. It facilitates the creation and optimization of these complex models by intelligently routing training data to specific expert networks within the MoE architecture, leading to faster convergence and improved performance. This skill is particularly useful for handling large datasets and diverse tasks where specialized expertise is beneficial.

When to use it

  • Large Language Model Fine-tuning: Train a custom LLM on a massive dataset by distributing the workload across multiple expert networks.
  • Personalized AI Assistants: Develop assistants that specialize in different areas (e.g., coding, writing, research) using MoE for targeted expertise.
  • Multi-Task Learning: Build an agent capable of handling various tasks simultaneously by assigning each task to a dedicated expert within the MoE model.
  • Resource Constrained Environments: Optimize training efficiency and reduce computational costs in scenarios with limited resources.

Key capabilities

  • MoE Model Training
  • Expert Network Routing
  • Data Distribution Optimization
  • Efficient Convergence

Example prompts

  • "Train this dataset for a MoE model, routing data based on task type."
  • "Optimize the expert network assignments for improved accuracy in this training run."
  • "Create an MoE model with three experts, one for each of these categories: coding, writing, and research."

Tips & gotchas

The skill requires a foundational understanding of Mixture of Experts architectures. Ensure your data is appropriately labeled or categorized to enable effective expert routing during training.