hugging-face-evaluation
Lets the agent add and manage evaluation results for models on Hugging Face. It can extract evaluation tables and import scores from APIs.
What it does
This tool helps add test and evaluation results to Hugging Face model pages. It can extract result tables already in the page text, import data from other sources, or even run new tests.
When to use it
- ✓When you want to display the test results of an AI model on its page.
- ✓If you have test results elsewhere and want to put them on the Hugging Face page.
- ✓To prepare information for AI model competitions or leaderboards.
Example of what to ask your AI
“Add the evaluation results for my model to its Hugging Face page, extracting data from the README table.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool looks for result tables within the model's page text. It can also connect to other data sources or use specific programs to run new tests and add the results in an organized way.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill hugging-face-evaluationOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/hugging-face-evaluation/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Overview
This skill provides tools to add structured evaluation results to Hugging Face model cards. It supports multiple methods for adding evaluation data:
- Extracting existing evaluation tables from README content
- Importing benchmark scores from Artificial Analysis
- Running custom model evaluations with vLLM or accelerate backends (lighteval/inspect-ai)
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use
- You need to add structured evaluation results to a Hugging Face model card.
- You want to import benchmark data or run custom evaluations with vLLM, lighteval, or inspect-ai.
- You are preparing leaderboard-compatible
model-indexmetadata for a model release.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
FAQ
What is Hugging Face?
It's a popular website where people share and use artificial intelligence models.
What are 'evaluation results'?
These are the outcomes of tests that show how well an AI model performs on different tasks.
What is 'model-index metadata'?
This is a special format of information that helps organize and present AI model data, especially for leaderboards.
Details
Original description by the author
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3222
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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