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Llm Training

Checked
eyadsibai·vlatest·ai-ml

Provides LLMs guidance and assistance for building AI and machine learning applications.

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 eyadsibai/ltk --skill llm-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

eyadsibai

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 eyadsibai/ltk at f8e85697b9.

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

Commit
f8e85697b9
Manifest
plugins/ltk-data/skills/llm-training/SKILL.md
Scanned
1 file, 97 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 allows you to train Large Language Models (LLMs) by providing them with specific datasets and instructions. It can fine-tune existing models for specialized tasks, improve their performance on particular domains, or adapt them to new styles of writing. The training process involves iterative adjustments to the model's parameters based on the provided data.

When to use it

  • Specialized Task Training: You need an LLM to perform a very specific task like generating legal contracts or summarizing medical reports.
  • Domain Adaptation: You want to improve an LLM’s understanding and generation capabilities within a niche area, such as financial analysis or scientific research.
  • Style Customization: You desire the LLM to adopt a particular writing style (e.g., formal, humorous, poetic).
  • Data Augmentation: You have limited data for a specific task and need to generate synthetic examples to improve model performance.

Key capabilities

  • Dataset ingestion from various formats.
  • Instruction-based training.
  • Fine-tuning of existing LLMs.
  • Parameter adjustment based on provided data.

Example prompts

  • "Train this LLM on the attached dataset of customer service transcripts to improve its ability to handle support requests."
  • "Fine-tune the model using these legal documents and instruct it to generate summaries in a formal tone."
  • "Adapt the LLM to write marketing copy with a humorous style, using the provided examples as training data."

Tips & gotchas

  • The quality of your training data is crucial. Garbage in, garbage out applies here – ensure the dataset is clean and relevant.
  • Training an LLM can be computationally expensive; consider resource limitations before starting a large-scale training process.