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Agent skill

Hugging Face Evaluation

by sickn33 in sickn33/agentic-awesome-skills

Add and manage evaluation results in Hugging Face model cards.

MITAuto-check passedAI & LLM Engineering

Install Hugging Face Evaluation

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-evaluation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills hugging-face-evaluation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/hugging-face-evaluation .claude/skills/hugging-face-evaluation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hugging-face-evaluation
GitHub stars
47k
Used in
2 other repos
Token cost
~418 tokens
SKILL.md length
173 words
Files
2 (incl. references)
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Add and manage evaluation results in Hugging Face model cards.

  • Tasks that involve Model hubs and datasets
  • SKILL.md covers Detailed Guide, When to Use and Limitations
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve LLM inference and serving

What it does

Hugging Face Evaluation is an agent skill from sickn33/agentic-awesome-skills. 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.

Its SKILL.md is about 420 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/detailed-guide.md`).

It sits in AI & LLM Engineering, covering Model hubs and datasets and LLM inference and serving. It works with Hugging Face and vLLM. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Model hubs and datasets
  • Tasks that involve LLM inference and serving

Example prompts

  • “/hugging-face-evaluation”

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hugging Face Evaluation loads about 418 tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 173 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~418
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 173 words, ~418 tokens.

Download SKILL.mdSave it as .claude/skills/hugging-face-evaluation/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
hugging-face-evaluation
description
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.
risk
critical
source
community
date_added
2026-09-04

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-index metadata 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.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/hugging-face-evaluation of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/detailed-guide.md

Open the folder on GitHubat commit 680176d

Used in 2 other repositories

We found 13 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 9, 2026.

Compare with similar skills

Hugging Face Evaluation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Hugging Face Evaluation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Hugging Face Evaluation this skillsickn33/agentic-awesome-skills47k2 repos~418Automated safety check: PassMIT
SageMaker Serving Image Selectionhuggingface/skills11k1 repos~4.6kAutomated safety check: PassApache-2.0
Hugging Face Local Model Evalshuggingface/skills11k2 repos~1.6kAutomated safety check: PassApache-2.0
Add Modelguoqingbao/xinfer334—~4.2kAutomated safety check: NotesMIT
Resolvealexziskind1/model-shelf130—~792Automated safety check: PassMIT
Check Modelguoqingbao/xinfer334—~3.8kAutomated safety check: PassMIT

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Works with

Categories

Questions about Hugging Face Evaluation

What does Hugging Face Evaluation do?

Add and manage evaluation results in Hugging Face model cards. Hugging Face Evaluation is an agent skill from sickn33/agentic-awesome-skills. Add and manage evaluation results in Hugging Face model cards.

When should I use Hugging Face Evaluation?

Hugging Face Evaluation fits situations like: tasks that involve Model hubs and datasets; tasks that involve LLM inference and serving.

How do I install Hugging Face Evaluation in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-evaluation -a claude-code`. Or copy the skill folder (skills/hugging-face-evaluation in sickn33/agentic-awesome-skills) into .claude/skills/hugging-face-evaluation in your project. Claude Code loads it when a task matches its description.

How do I install Hugging Face Evaluation in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-evaluation -a codex`. Or copy the skill folder (skills/hugging-face-evaluation in sickn33/agentic-awesome-skills) into .agents/skills/hugging-face-evaluation in your project. Codex loads it when a task matches its description.

Can I use Hugging Face Evaluation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hugging-face-evaluation, .gemini/skills/hugging-face-evaluation, .github/skills/hugging-face-evaluation and .opencode/skills/hugging-face-evaluation in your project.

What does Hugging Face Evaluation need to run?

SKILL.md names no scripts, command-line tools or credentials: Hugging Face Evaluation is instructions for the agent only.

Does Hugging Face Evaluation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hugging Face Evaluation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Hugging Face Evaluation use?

Hugging Face Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hugging Face Evaluation use?

About 418 tokens (SKILL.md is roughly 1.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.5k tokens, read only when the agent opens those files.

What are the alternatives to Hugging Face Evaluation?

Skills that share tags, products or a category with Hugging Face Evaluation: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars), Add Model (guoqingbao/xinfer, 334 stars) and Resolve (alexziskind1/model-shelf, 130 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hugging Face Evaluation?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.