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Fine Tuning Data Generator

Checked
markpitt·vlatest·ai-ml

Generates synthetic training data tailored to your model's needs, improving performance without real-world examples.

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 markpitt/claude-skills --skill fine-tuning-data-generator -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

markpitt

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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

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

  • Pinned to a commit: passed

    Scanned markpitt/claude-skills at 43b02eee90.

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Commit
43b02eee90
Manifest
skills/fine-tuning-data-generator/SKILL.md
Scanned
11 files, 2,513 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.
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About this skill

What it does

The Fine-Tuning Data Generator skill creates synthetic data suitable for fine-tuning large language models. It can generate structured datasets based on provided schemas, allowing users to create training examples without needing to manually craft them. This is particularly useful when real-world data is scarce or sensitive.

When to use it

  • Limited Training Data: You need to fine-tune a model but have very few existing examples.
  • Data Privacy Concerns: You want to train on specific scenarios without exposing actual user data.
  • Schema-Driven Tasks: You're building an agent for tasks with well-defined input/output structures (e.g., code generation, document summarization).
  • Rapid Prototyping: Quickly generate training data to test and iterate on fine-tuning approaches.

Key capabilities

  • Generates synthetic datasets based on provided schemas.
  • Supports structured data formats for model training.
  • Allows customization of generated data characteristics.

Example prompts

  • "Generate 10 examples of customer service dialogues with the schema: { 'customer_query': string, 'agent_response': string }"
  • "Create a dataset of 5 code snippets in Python following this format: { 'prompt': string, 'solution': string }"
  • "Give me 3 examples of JSON objects representing product reviews, using the schema: { 'product_id': integer, 'rating': integer, 'comment': string }"

Tips & gotchas

The quality of generated data heavily relies on the accuracy and completeness of the provided schemas. Iteratively refine both the schema and the generated output to achieve desired fine-tuning results.