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huggingface-tool-builder

Creates reusable CLI tools for Hugging Face Hub API workflows with chaining and piping support.

Updated May 5, 2026
Search Tags:#automation#api#cli#data-extraction#huggingface#machine-learning
One-click install
npx skills add https://github.com/iani-kuli/harness_bro --skill huggingface-tool-builder-iani-kuli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-tool-builder
Source: https://github.com/iani-kuli/harness_bro/tree/main/.claude/skills/ported/huggingface-tool-builder
Command: npx skills add https://github.com/iani-kuli/harness_bro --skill huggingface-tool-builder-iani-kuli

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires curl, jq, hf, python3, and includes references (resource) components.

What problem does it solve?

This skill solves the complexity of manually interacting with the Hugging Face API by providing a structured way to create, chain, and automate API-driven tasks.

Core Features & Use Cases

  • API Workflow Automation: Create reusable shell or Python scripts to fetch model metadata, dataset information, or paper associations.
  • Data Pipeline Integration: Use piping and NDJSON output to stream model data into downstream analysis tools like jq.
  • Use Case: Automatically fetch the top 50 trending models, extract their license and pipeline tags, and filter them by specific criteria for a research project.

Quick Start

Use the huggingface-tool-builder skill to create a script that fetches metadata for a list of model IDs provided via standard input.

Frequently Asked Questions about huggingface-tool-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate Hugging Face API requests to fetch model metadata in bulk?▼

Automate Hugging Face API requests by building reusable command-line utilities that use curl and the hf CLI to fetch model metadata, chaining commands and piping NDJSON output to parse JSON responses with jq.

What is the best way to extract and filter trending model pipeline tags from the Hugging Face Hub?▼

The best way to extract pipeline tags is to use a data pipeline that fetches trending models via the API and pipes the NDJSON output into jq to filter specific metadata fields like licenses and tags for research projects.

Do I need the hf CLI and jq installed to build Hugging Face data extraction workflows?▼

Yes, building Hugging Face data extraction workflows requires curl, jq, the hf CLI, and python3 to execute authenticated API requests and parse JSON responses for downstream data processing.

Can I pipe Hugging Face dataset information into downstream analysis tools?▼

Yes, you can pipe Hugging Face dataset information into downstream analysis tools by utilizing NDJSON output to stream model data directly into command-line utilities like jq for filtering.

How does chaining command-line utilities work for processing Hugging Face research paper associations?▼

Chaining for research paper associations works by creating reusable scripts that fetch paper data via the Hugging Face Hub API, pipe the output, and filter JSON responses to automate the data processing workflow.

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