HU

hugging-face-tool-builder

Create scripts to fetch, enrich, and process data from the Hugging Face API.

Install

mkdir -p .claude/skills/hugging-face-tool-builder && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2205" && unzip -o skill.zip -d .claude/skills/hugging-face-tool-builder && rm skill.zip

Installs to .claude/skills/hugging-face-tool-builder

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
303 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Create reusable command line scripts for Hugging Face API
  • Chain and pipe Hugging Face API requests
  • Extract metadata from model and dataset cards
  • Process API results using jq

How it works

The skill creates shell or Python scripts that interact with Hugging Face endpoints, utilizing the HF_TOKEN for authentication and piping data through tools like jq.

Inputs & outputs

You give it
Hugging Face API endpoint or model ID
You get back
Processed JSON data or shell script utility

When to use hugging-face-tool-builder

  • Fetch model metadata
  • Automate dataset downloads
  • Chain Hugging Face API requests
  • Build shell scripts for AI data access

About this skill

Hugging Face API Tool Builder

Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the hf command line tool. Model and Dataset cards can be accessed from repositories directly.

Script Rules

Make sure to follow these rules:

  • Scripts must take a --help command line argument to describe their inputs and outputs
  • Non-destructive scripts should be tested before handing over to the User
  • Shell scripts are preferred, but use Python or TSX if complexity or user need requires it.
  • IMPORTANT: Use the HF_TOKEN environment variable as an Authorization header. For example: curl -H "Authorization: Bearer ${HF_TOKEN}" https://huggingface.co/api/. This provides higher rate limits and appropriate authorization for data access.
  • Investigate the shape of the API results before commiting to a final design; make use of piping and chaining where composability would be an advantage - prefer simple solutions where possible.
  • Share usage examples once complete.

Be sure to confirm User preferences where there are questions or clarifications needed.

Sample Scripts

Paths below are relative to this skill directory.

Reference examples:

  • references/hf_model_papers_auth.sh — uses HF_TOKEN automatically and chains trending → model metadata → model card parsing with fallbacks; it demonstrates multi-step API usage plus auth hygiene for gated/private content.
  • references/find_models_by_paper.sh — optional HF_TOKEN usage via --token, consistent authenticated search, and a retry path when arXiv-prefixed searches are too narrow; it shows resilient query strategy and clear user-facing help.
  • references/hf_model_card_frontmatter.sh — uses the hf CLI to download model cards, extracts YAML frontmatter, and emits NDJSON summaries (license, pipeline tag, tags, gated prompt flag) for easy filtering.

Baseline examples (ultra-simple, minimal logic, raw JSON output with HF_TOKEN header):

  • references/baseline_hf_api.sh — bash
  • references/baseline_hf_api.py — python
  • references/baseline_hf_api.tsx — typescript executable

Composable utility (stdin → NDJSON):

  • references/hf_enrich_models.sh — reads model IDs from stdin, fetches metadata per ID, emits one JSON object per line for streaming pipelines.

Composability through piping (shell-friendly JSON output):

  • references/baseline_hf_api.sh 25 | jq -r '.[].id' | references/hf_enrich_models.sh | jq -s 'sort_by(.downloads) | reverse | .[:10]'
  • references/baseline_hf_api.sh 50 | jq '[.[] | {id, downloads}] | sort_by(.downloads) | reverse | .[:10]'
  • printf '%s\n' openai/gpt-oss-120b meta-llama/Meta-Llama-3.1-8B | references/hf_model_card_frontmatter.sh | jq -s 'map({id, license, has_extra_gated_prompt})'

High Level Endpoints

The following are the main API endpoints available at https://huggingface.co

/api/datasets
/api/models
/api/spaces
/api/collections
/api/daily_papers
/api/notifications
/api/settings
/api/whoami-v2
/api/trending
/oauth/userinfo

Accessing the API

The API is documented with the OpenAPI standard at https://huggingface.co/.well-known/openapi.json.

IMPORTANT: DO NOT ATTEMPT to read https://huggingface.co/.well-known/openapi.json directly as it is too large to process.

IMPORTANT Use jq to query and extract relevant parts. For example,

Command to Get All 160 Endpoints

curl -s "https://huggingface.co/.well-known/openapi.json" | jq '.paths | keys | sort'

Model Search Endpoint Details

curl -s "https://huggingface.co/.well-known/openapi.json" | jq '.paths["/api/models"]'

You can also query endpoints to see the shape of the data. When doing so constrain results to low numbers to make them easy to process, yet representative.

Using the HF command line tool

The hf command line tool gives you further access to Hugging Face repository content and infrastructure.

❯ hf --help
Usage: hf [OPTIONS] COMMAND [ARGS]...

  Hugging Face Hub CLI

Options:
  --help                Show this message and exit.

Commands:
  auth                 Manage authentication (login, logout, etc.).
  cache                Manage local cache directory.
  download             Download files from the Hub.
  endpoints            Manage Hugging Face Inference Endpoints.
  env                  Print information about the environment.
  jobs                 Run and manage Jobs on the Hub.
  repo                 Manage repos on the Hub.
  repo-files           Manage files in a repo on the Hub.
  upload               Upload a file or a folder to the Hub.
  upload-large-folder  Upload a large folder to the Hub.
  version              Print information about the hf version.

The hf CLI command has replaced the now deprecated huggingface_hub CLI command.

When not to use it

  • Directly reading large OpenAPI specification files

Prerequisites

HF_TOKEN environment variable

Limitations

  • OpenAPI spec file is too large to process directly

How it compares

It builds composable command-line utilities for data access instead of writing one-off manual API requests.

Compared to similar skills

hugging-face-tool-builder side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
hugging-face-tool-builder (this skill)76moReviewIntermediate
telegram-bot-builder1066moReviewIntermediate
langchain-architecture82moReviewIntermediate
robotics-code-generator147moNo flagsAdvanced

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