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Hugging face datasets

Skill newmindsgroup/ai-agent-skills-library/dist/skills/hugging-face-datasets

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.From its SKILL.md

Install
npx -y skills add newmindsgroup/ai-agent-skills-library --skill hugging-face-datasets

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.7 KB, 454 tokens by cl100k_base, as published. Nobody here has run it

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.

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.

Core Workflow

  1. Confirm the request matches this skill's trigger, scope, and risk profile.
  2. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.
  3. Load references/full-guidance.md when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.
  4. Apply only the relevant guidance instead of loading or repeating the entire reference by default.
  5. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.

Topic Map

  • Integration with HF MCP Server
  • Dataset Lifecycle Management
  • SQL-Based Dataset Querying (NEW)
  • Multi-Format Dataset Support
  • Quality Assurance Features
  • Prerequisites
  • Quick Start
  • SQL Query Syntax
  • Common Operations
  • Explore Dataset Structure
  • Filter and Transform
  • Create Subsets and Push to Hub
  • Export to Local Files
  • Working with Dataset Configs/Splits
  • Raw SQL with Full Paths
  • Python API Usage
  • HF Path Format
  • Useful DuckDB SQL Functions

Reference Map

  • references/full-guidance.md preserves the complete original guidance, including examples and detailed edge cases.

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.

Progressive Loading

Keep this SKILL.md as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.

What ships with it: 1 file

16.5 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most mcp tooling skills give in 454 tokens

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-07

  • Create ten complex or independent read-only evaluation questionsin 69 of 638, across 15 files
  • Test servers using MCP Inspectorin 61 of 638, across 19 files
  • Provide actionable error messages with specific next stepsin 54 of 638, across 12 files
  • Prioritize comprehensive API coverage over specific workflows or workflow toolsin 54 of 638, across 12 files
  • Use TypeScript and Streamable HTTP for remote servers or clientsin 54 of 638, across 8 files
  • Define structured output schemas where possiblein 50 of 638, across 8 files
  • Use Zod or Pydantic for input schemasin 47 of 638, across 5 files
  • Fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • Load framework documentation using WebFetchin 45 of 638, across 3 files
  • Verify each evaluation answer independentlyin 45 of 638, across 3 files
  • Implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 27 of 638, across 9 files

Said here and by no other author read

  • confirm the request matches skill scope
  • use the topic map to identify relevant patterns
  • load full guidance for implementation details
  • apply only relevant guidance from references
  • verify results against validation checks

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 326,782. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.