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Agent skill

Hugging Face Datasets

by sickn33 in sickn33/agentic-awesome-skills

Create and manage datasets on Hugging Face Hub. An agent skill from sickn33/agentic-awesome-skills.

MITAuto-check passedAI & LLM Engineering

Install Hugging Face Datasets

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

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills hugging-face-datasets --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-datasets .claude/skills/hugging-face-datasets && 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-datasets
GitHub stars
47k
Used in
2 other repos
Token cost
~1.1k tokens
SKILL.md length
203 words
Files
2 (incl. references)
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Create and manage datasets on Hugging Face Hub. An agent skill from sickn33/agentic-awesome-skills.

  • Tasks that involve Model hubs and datasets
  • SKILL.md covers Detailed Guide, When to Use, Python API Usage and Example 1: Create Training…, plus 5 more sections
  • Calls uv
  • Tasks that involve Prompt engineering

What it does

Hugging Face Datasets is an agent skill from sickn33/agentic-awesome-skills. Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.

Its SKILL.md is about 1.1k 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, Prompt engineering and SQL. It works with Hugging Face, SQL and Model Context Protocol. 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 Prompt engineering
  • Tasks that involve SQL

Example prompts

  • “/hugging-face-datasets”

Requirements

  • Python 3

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

    Shell commands in SKILL.md call:

    • uv

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

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    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 Datasets loads about 1.1k tokens when it runs, and up to ~4.4k if it reads all its reference files. Until then it costs about 70 tokens; SKILL.md has 203 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.4k

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). 203 words, ~1,083 tokens.

Download SKILL.mdSave it as .claude/skills/hugging-face-datasets/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
hugging-face-datasets
description
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
risk
critical
source
community
date_added
2026-09-04

Overview

This skill provides tools to manage datasets on the Hugging Face Hub with a focus on creation, configuration, content management, and SQL-based data manipulation. It is designed to complement the existing Hugging Face MCP server by providing dataset editing and querying capabilities.

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 create, configure, or update datasets on the Hugging Face Hub.
  • You want SQL-style querying, transformation, or export flows over Hub datasets.
  • You are managing dataset content and metadata directly rather than only searching existing datasets.

Python API Usage

python
from sql_manager import HFDatasetSQL

sql = HFDatasetSQL()

# Query
results = sql.query("cais/mmlu", "SELECT * FROM data WHERE subject='nutrition' LIMIT 10")

# Get schema
schema = sql.describe("cais/mmlu")

# Sample
samples = sql.sample("cais/mmlu", n=5, seed=42)

# Count
count = sql.count("cais/mmlu", where="subject='nutrition'")

# Histogram
dist = sql.histogram("cais/mmlu", "subject")

# Filter and transform
results = sql.filter_and_transform(
    "cais/mmlu",
    select="subject, COUNT(*) as cnt",
    group_by="subject",
    order_by="cnt DESC",
    limit=10
)

# Push to Hub
url = sql.push_to_hub(
    "cais/mmlu",
    "username/nutrition-subset",
    sql="SELECT * FROM data WHERE subject='nutrition'",
    private=True
)

# Export locally
sql.export_to_parquet("cais/mmlu", "output.parquet", sql="SELECT * FROM data LIMIT 100")

sql.close()

Example 1: Create Training Subset from Existing Dataset

bash
# 1. Explore the source dataset
uv run scripts/sql_manager.py describe --dataset "cais/mmlu"
uv run scripts/sql_manager.py histogram --dataset "cais/mmlu" --column "subject"

# 2. Query and create subset
uv run scripts/sql_manager.py query \
  --dataset "cais/mmlu" \
  --sql "SELECT * FROM data WHERE subject IN ('nutrition', 'anatomy', 'clinical_knowledge')" \
  --push-to "username/mmlu-medical-subset" \
  --private

Example 2: Transform and Reshape Data

bash
# Transform MMLU to QA format with correct answers extracted
uv run scripts/sql_manager.py query \
  --dataset "cais/mmlu" \
  --sql "SELECT question, choices[answer] as correct_answer, subject FROM data" \
  --push-to "username/mmlu-qa-format"

Example 3: Merge Multiple Dataset Splits

bash
# Export multiple splits and combine
uv run scripts/sql_manager.py export \
  --dataset "cais/mmlu" \
  --split "*" \
  --output "mmlu_all.parquet"

Example 4: Quality Filtering

bash
# Filter for high-quality examples
uv run scripts/sql_manager.py query \
  --dataset "squad" \
  --sql "SELECT * FROM data WHERE LENGTH(context) > 500 AND LENGTH(question) > 20" \
  --push-to "username/squad-filtered"

Example 5: Create Custom Training Dataset

bash
# 1. Query source data
uv run scripts/sql_manager.py export \
  --dataset "cais/mmlu" \
  --sql "SELECT question, subject FROM data WHERE subject='nutrition'" \
  --output "nutrition_source.jsonl" \
  --format jsonl

# 2. Process with your pipeline (add answers, format, etc.)

# 3. Push processed data
uv run scripts/dataset_manager.py init --repo_id "username/nutrition-training"
uv run scripts/dataset_manager.py add_rows \
  --repo_id "username/nutrition-training" \
  --template qa \
  --rows_json "$(cat processed_data.json)"

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-datasets 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 11 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 7, 2026.

Compare with similar skills

Hugging Face Datasets 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 Datasets compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Hugging Face Datasets this skillsickn33/agentic-awesome-skills47k2 repos~1.1kAutomated safety check: PassMIT
Hugging Face LLM Trainerhuggingface/skills11k1 repos~7.2kAutomated safety check: PassApache-2.0
Generate Openenv Envadithya-s-k/FineEnvs456—~2.4kAutomated safety check: PassApache-2.0
Hf MCPhuggingface/skills11k2 repos~1.2kAutomated safety check: PassApache-2.0
NaturalNPC-Worldwide/npcpy1.5k—~161Automated safety check: PassMIT
TMA1 Observability Querytma1-ai/tma1119—~5.1kAutomated safety check: NotesApache-2.0

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Works with

Categories

Questions about Hugging Face Datasets

What does Hugging Face Datasets do?

Create and manage datasets on Hugging Face Hub. An agent skill from sickn33/agentic-awesome-skills. Hugging Face Datasets is an agent skill from sickn33/agentic-awesome-skills. Create and manage datasets on Hugging Face Hub.

When should I use Hugging Face Datasets?

Hugging Face Datasets fits situations like: tasks that involve Model hubs and datasets; tasks that involve Prompt engineering; tasks that involve SQL.

How do I install Hugging Face Datasets in Claude Code?

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

How do I install Hugging Face Datasets in Codex?

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

Can I use Hugging Face Datasets 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-datasets -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-datasets, .gemini/skills/hugging-face-datasets, .github/skills/hugging-face-datasets and .opencode/skills/hugging-face-datasets in your project.

What does Hugging Face Datasets need to run?

Going by SKILL.md and its folder, Hugging Face Datasets needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Hugging Face Datasets access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Hugging Face Datasets 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 Datasets use?

Hugging Face Datasets 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 Datasets use?

About 1.1k tokens (SKILL.md is roughly 4.3k 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 3.3k tokens, read only when the agent opens those files.

What are the alternatives to Hugging Face Datasets?

Skills that share tags, products or a category with Hugging Face Datasets: Hugging Face LLM Trainer (huggingface/skills, 11k stars), Generate Openenv Env (adithya-s-k/FineEnvs, 456 stars), Hf MCP (huggingface/skills, 11k stars) and Natural (NPC-Worldwide/npcpy, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hugging Face Datasets?

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.