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Hugging Face Evaluation

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patchy631·vlatest·ai-ml·MIT

This skill uses Hugging Face to evaluate model performance on datasets, streamlining benchmarking and ensuring consistent results for your AI 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 patchy631/ai-engineering-hub --skill hugging-face-evaluation -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

patchy631

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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 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 undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: artificialanalysis.ai.

    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

    artificialanalysis.aiundeclaredhuggingface.comodel provider

    What it found (3 of 5)

    • hugging-face-skills/skills/hugging-face-evaluation/examples/artificial_analysis_to_hub.py:44contacts artificialanalysis.ai
      URL = "https://artificialanalysis.ai/api/v2/data/llms/models"
    • hugging-face-skills/skills/hugging-face-evaluation/examples/artificial_analysis_to_hub.py:97contacts artificialanalysis.ai
      "url": "https://artificialanalysis.ai",
    • hugging-face-skills/skills/hugging-face-evaluation/examples/artificial_analysis_to_hub.py:128contacts artificialanalysis.ai
      "To see the scores, visit the [Artificial Analysis](https://artificialanalysis.ai) website."

  • 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 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 patchy631/ai-engineering-hub at 7875e2c388.

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

Commit
7875e2c388
Manifest
hugging-face-skills/skills/hugging-face-evaluation/SKILL.md
Scanned
13 files, 3,967 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 provides access to Hugging Face's evaluation tools. It allows you to assess the performance of machine learning models, particularly large language models (LLMs), using datasets and metrics available on the Hugging Face Hub. You can leverage this for tasks like benchmarking model accuracy or evaluating generation quality.

When to use it

  • Model Selection: Compare different LLMs based on their performance on specific evaluation datasets.
  • Fine-tuning Evaluation: Measure the impact of fine-tuning a model by running evaluations before and after training.
  • Prompt Engineering Assessment: Determine which prompts yield the best results for a given task using Hugging Face's evaluation infrastructure.
  • Benchmarking: Track changes in model performance over time or across different versions.

Key capabilities

  • Access to datasets on the Hugging Face Hub.
  • Utilization of various metrics for evaluating model outputs.
  • Integration with Hugging Face’s existing tools and resources.

Example prompts

  • "Evaluate the 'bert-base-uncased' model on the 'glue/mrpc' dataset."
  • "Run a sentiment analysis evaluation using the 'cardiffnlp/twitter-roberta-base-sentiment' model."
  • "Benchmark my fine-tuned model against the original 'facebook/bart-large' on the 'xsum' summarization dataset."

Tips & gotchas

Requires familiarity with Hugging Face’s ecosystem and terminology. Be mindful of resource constraints when evaluating large models or datasets; consider using smaller subsets for initial testing.