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Agent skill

Huggingface Tool Builder

by sickn33 in sickn33/agentic-awesome-skills

A skill your agent uses when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Huggingface Tool Builder

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill huggingface-tool-builder -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills huggingface-tool-builder --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/huggingface-tool-builder .claude/skills/huggingface-tool-builder && 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
huggingface-tool-builder
GitHub stars
47k
Used in
1 other repo
Token cost
~1.7k tokens
SKILL.md length
579 words
Files
8 (incl. references)
Skills in repo
1,354
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help.

  • The user wants to build tool/scripts
  • SKILL.md covers When to Use, Script Rules, Sample Scripts and High Level Endpoints, plus 3 more sections
  • Runs Shell and Python scripts from its folder; calls jq and curl; reaches huggingface.co; needs HF_TOKEN
  • Achieve a task where using data from the Hugging Face API would help

What it does

Huggingface Tool Builder is an agent skill from sickn33/agentic-awesome-skills. 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.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/baseline_hf_api.py`, `references/baseline_hf_api.sh` and `references/find_models_by_paper.sh`).

It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with Hugging Face. 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 Apache-2.0.

When your agent uses it

  • The user wants to build tool/scripts
  • Achieve a task where using data from the Hugging Face API would help

Example prompts

  • “/huggingface-tool-builder”

Requirements

  • Python 3
  • A Bash shell

What it can do on your machine

Read from SKILL.md and the folder at commit ec02547. 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

    Ships script files (Shell and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • jq
    • curl

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • huggingface.co

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • HF_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Huggingface Tool Builder loads about 1.7k tokens when it runs, and up to ~7.4k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 579 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.4k

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 ec02547, republished under its Apache-2.0 licence (© sickn33). 579 words, ~1,699 tokens.

Download SKILL.mdSave it as .claude/skills/huggingface-tool-builder/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
huggingface-tool-builder
description
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.
risk
critical
source
https://github.com/huggingface/skills/tree/main/skills/huggingface-tool-builder
source_repo
huggingface/skills
source_type
official
date_added
2026-07-01
license
Apache-2.0
license_source
https://github.com/huggingface/skills/blob/main/LICENSE

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 --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})'
Show full SKILL.md (177 more words)Show less

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

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

Model Search Endpoint Details

bash
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.

bash
❯ 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.).
  buckets              Commands to interact with buckets.
  cache                Manage local cache directory.
  collections          Interact with collections on the Hub.
  datasets             Interact with datasets on the Hub.
  discussions          Manage discussions and pull requests on the Hub.
  download             Download files from the Hub.
  endpoints            Manage Hugging Face Inference Endpoints.
  env                  Print information about the environment.
  extensions           Manage hf CLI extensions.
  jobs                 Run and manage Jobs on the Hub.
  models               Interact with models on the Hub.
  papers               Interact with papers on the Hub.
  repos                Manage repos on the Hub.
  skills               Manage skills for AI assistants.
  spaces               Interact with spaces on the Hub.
  sync                 Sync files between local directory and a bucket.
  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.
  webhooks             Manage webhooks on the Hub.

The hf CLI command has replaced the now deprecated huggingface-cli command.

Limitations

  • Use this skill only when the task clearly matches its upstream product or API scope.
  • Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.
  • Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

© sickn33, Apache-2.0. 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 7 other files (references) in skills/huggingface-tool-builder of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/baseline_hf_api.py
  • references/baseline_hf_api.sh
  • references/baseline_hf_api.tsx
  • references/find_models_by_paper.sh
  • references/hf_enrich_models.sh
  • references/hf_model_card_frontmatter.sh
  • references/hf_model_papers_auth.sh

Open the folder on GitHubat commit ec02547

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Huggingface Tool Builder 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.

Huggingface Tool Builder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Huggingface Tool Builder this skillsickn33/agentic-awesome-skills47k1 repos~1.7kAutomated safety check: PassApache-2.0
SageMaker Serving Image Selectionhuggingface/skills11k1 repos~4.6kAutomated safety check: PassApache-2.0
Hugging Face LLM Trainerhuggingface/skills11k3 repos~7.2kAutomated safety check: PassApache-2.0
LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs13k8 repos~3kAutomated safety check: PassMIT
Upload Post Imagehuggingface/blog3.5k—~1.1kAutomated safety check: PassNone
Hugging Face Local Model Evalshuggingface/skills11k2 repos~1.6kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Huggingface Tool Builder

What does Huggingface Tool Builder do?

A skill your agent uses when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. Huggingface Tool Builder is an agent skill from sickn33/agentic-awesome-skills. 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.

When should I use Huggingface Tool Builder?

Huggingface Tool Builder fits situations like: the user wants to build tool/scripts; achieve a task where using data from the Hugging Face API would help.

How do I install Huggingface Tool Builder in Claude Code?

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

How do I install Huggingface Tool Builder in Codex?

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

Can I use Huggingface Tool Builder 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 huggingface-tool-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/huggingface-tool-builder, .gemini/skills/huggingface-tool-builder, .github/skills/huggingface-tool-builder and .opencode/skills/huggingface-tool-builder in your project.

What does Huggingface Tool Builder need to run?

Going by SKILL.md and its folder, Huggingface Tool Builder needs a shell and Python for the scripts in its folder, the command-line tools its instructions call (jq and curl) and credentials named HF_TOKEN. Our summary lists: Python 3; A Bash shell.

Does Huggingface Tool Builder access the network?

SKILL.md names 1 domain. In commands or code: huggingface.co; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Huggingface Tool Builder 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 Huggingface Tool Builder use?

Huggingface Tool Builder is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Huggingface Tool Builder use?

About 1.7k tokens (SKILL.md is roughly 6.8k 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 5.7k tokens, read only when the agent opens those files.

What are the alternatives to Huggingface Tool Builder?

Skills that share tags, products or a category with Huggingface Tool Builder: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face LLM Trainer (huggingface/skills, 11k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Upload Post Image (huggingface/blog, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Huggingface Tool Builder?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,343 GitHub stars. The repository holds 1,354 skills in this directory. The repository was last updated on October 7, 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.