Huggingface API

What it does

When your agent uses it

• Tasks that involve Model hubs and datasets
• Tasks that involve Machine learning

Example prompts

• “/huggingface-api”

Requirements

• Python 3
• Docker

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Model selection for benchmarks: Search by pipeline tag (text-classification, token-classification, summarization) and sort by downloads to…
2. 2Dataset discovery: Filter by task_categories, language, and size_categories tags to find training data matching your experimental…
3. 3Reproducibility: Pin model versions using the sha field from model details -- load exact revisions with revision="86b5e093..." in…
4. 4Citation tracking: Extract arxiv: tags from model/dataset metadata to trace foundational papers
5. 5Ecosystem analysis: Aggregate download/like counts across model families to study adoption trends in ML research

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. 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

  Shell commands in SKILL.md call:

  • 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 API loads about 2k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 394 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~21

When it runs · the whole SKILL.md, loaded when a task matches

~2k

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

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 wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 394 words, ~1,984 tokens.

name

huggingface-api

description

Search and discover ML models, datasets, and Spaces on Hugging Face

export HF_TOKEN="hf_..."
# Pass via header:
curl -H "Authorization: Bearer $HF_TOKEN" https://huggingface.co/api/...

GET https://huggingface.co/api/models?search={query}&limit={n}&sort={field}&direction={-1|1}

curl -s "https://huggingface.co/api/models?search=bert&limit=2&sort=downloads&direction=-1"

[
  {
    "id": "google-bert/bert-base-uncased",
    "likes": 2587,
    "downloads": 71053483,
    "pipeline_tag": "fill-mask",
    "library_name": "transformers",
    "tags": ["transformers","pytorch","tf","jax","bert","fill-mask","en",
             "dataset:bookcorpus","dataset:wikipedia","arxiv:1810.04805",
             "license:apache-2.0"]
  },
  {
    "id": "google-bert/bert-base-multilingual-uncased",
    "likes": 153,
    "downloads": 5017183,
    "pipeline_tag": "fill-mask",
    "library_name": "transformers"
  }
]

GET https://huggingface.co/api/models/{owner}/{model_name}

curl -s "https://huggingface.co/api/models/google-bert/bert-base-uncased"

{
  "id": "google-bert/bert-base-uncased",
  "sha": "86b5e0934494bd15c9632b12f734a8a67f723594",
  "lastModified": "2024-02-19T11:06:12.000Z",
  "downloads": 71053483,
  "config": { "architectures": ["BertForMaskedLM"], "model_type": "bert" },
  "cardData": { "language": "en", "license": "apache-2.0",
                "datasets": ["bookcorpus","wikipedia"] }
}

GET https://huggingface.co/api/datasets?search={query}&limit={n}

curl -s "https://huggingface.co/api/datasets?search=squad&limit=2"

[
  {
    "id": "rajpurkar/squad_v2",
    "likes": 242,
    "downloads": 36017,
    "description": "Stanford Question Answering Dataset (SQuAD)...",
    "tags": ["task_categories:question-answering","language:en",
             "license:cc-by-sa-4.0","size_categories:100K<n<1M",
             "arxiv:1806.03822"]
  }
]

GET https://huggingface.co/api/datasets/{owner}/{dataset_name}

curl -s "https://huggingface.co/api/datasets/rajpurkar/squad_v2"

GET https://huggingface.co/api/spaces?search={query}&limit={n}

curl -s "https://huggingface.co/api/spaces?search=chatbot&limit=2"

[
  {
    "id": "21Hg/chatbot",
    "likes": 5,
    "sdk": "docker",
    "tags": ["docker","streamlit","region:us"]
  },
  {
    "id": "lmarena-ai/chatbot-arena",
    "likes": 234,
    "sdk": "static"
  }
]

# PyTorch text-generation models with 1000+ likes
curl -s "https://huggingface.co/api/models?filter=text-generation&library=pytorch&sort=likes&direction=-1&limit=5"

# Datasets for NER tasks in Chinese
curl -s "https://huggingface.co/api/datasets?filter=token-classification&language=zh&limit=10"

# Gradio Spaces sorted by trending
curl -s "https://huggingface.co/api/spaces?filter=gradio&sort=trending&direction=-1&limit=5"

import requests

# Search for top text-classification models
resp = requests.get("https://huggingface.co/api/models", params={
    "filter": "text-classification",
    "sort": "downloads",
    "direction": -1,
    "limit": 10
})
models = resp.json()
for m in models:
    print(f"{m['id']:50s}  downloads={m.get('downloads',0):>12,}")

# Get specific model metadata
detail = requests.get("https://huggingface.co/api/models/google-bert/bert-base-uncased").json()
print(f"SHA: {detail['sha']}")
print(f"License: {detail['cardData'].get('license')}")

from huggingface_hub import HfApi

api = HfApi()

# Search models (returns ModelInfo objects)
models = api.list_models(search="bert", sort="downloads", direction=-1, limit=5)
for m in models:
    print(f"{m.id}  downloads={m.downloads}")

# Get full model info
info = api.model_info("google-bert/bert-base-uncased")
print(f"Pipeline: {info.pipeline_tag}, SHA: {info.sha}")

# Search datasets
datasets = api.list_datasets(search="squad", sort="downloads", direction=-1, limit=5)
for d in datasets:
    print(f"{d.id}  downloads={d.downloads}")

# List Spaces
spaces = api.list_spaces(search="chatbot", limit=5)
for s in spaces:
    print(f"{s.id}  sdk={s.sdk}")

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/domains/ai-ml/huggingface-api of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

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

• brycewang-stanford/Auto-Empirical-Research-Skillsskills/43-wentorai-research-plugins/skills/domains/ai-ml/huggingface-api