Antigravity-awesome-skills hugging-face-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.

install
source · Clone the upstream repo
git clone https://github.com/sickn33/antigravity-awesome-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/sickn33/antigravity-awesome-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/antigravity-awesome-skills/skills/hugging-face-tool-builder" ~/.claude/skills/sickn33-antigravity-awesome-skills-hugging-face-tool-builder-7b12df && rm -rf "$T"
manifest: plugins/antigravity-awesome-skills/skills/hugging-face-tool-builder/SKILL.md
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source content

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.

When to Use

  • You need reusable CLI scripts around the Hugging Face API or
    hf
    command line tool.
  • You want shell-friendly utilities that support chaining, piping, and intermediate processing.
  • You are automating repeated Hub tasks and need a composable interface instead of ad hoc API calls.

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.

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.