huggingface-tool-builder
Helps your agent build scripts or automate tasks using data from the Hugging Face API. It's useful for combining API calls or repeating actions.
What it does
This tool builds scripts to fetch and use information from the Hugging Face platform. It helps automate repetitive tasks or combine different information efficiently.
When to use it
- ✓When you need to download data from models or datasets.
- ✓To create scripts that automatically fetch specific Hugging Face information.
- ✓When combining multiple Hugging Face API calls for a complex task.
Example of what to ask your AI
“Create a script that shows me the top 10 most downloaded models on Hugging Face.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses the Hugging Face API or the `hf` command-line tool to access data. It can create shell, Python, or TypeScript scripts to perform the desired task.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill huggingface-tool-builderOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/huggingface-tool-builder/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Hugging Face API Tool Builder
When to Use
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...
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
--helpcommand 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_TOKENenvironment 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— usesHF_TOKENautomatically 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— optionalHF_TOKENusage 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 thehfCLI 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— bashreferences/baseline_hf_api.py— pythonreferences/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:
…
FAQ
What is Hugging Face?
It's a platform that offers machine learning models, datasets, and tools to build AI applications.
What is an API?
It's an interface that allows different computer programs to talk to each other and exchange information.
Why use `HF_TOKEN`?
Using the `HF_TOKEN` allows access to more data with higher limits and authorizes access to restricted content.
Details
Original description by the author
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.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3236
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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