huggingface-api

huggingface-api is a skill for Claude Code, Codex from wentorai/research-plugins. It costs 19 tokens per session (2,169 once invoked), scanned A, original, MIT.

A guide to searching the Hugging Face Hub, a public online library of machine-learning models, datasets, and demo applications.

In plain words
What is it for?
Use it to discover models and datasets, compare community usage, select resources for experiments, and reference specific model versions in repeatable workflows.
Why use it?
It removes the need to browse the library manually and helps you find resources and their metadata through the Hub’s API, or programmatic interface.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/wentorai/research-plugins/huggingface-api
Any agent
npx skills add wentorai/research-plugins --skill huggingface-api
Clone the repo
git clone --depth 1 https://github.com/wentorai/research-plugins

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for huggingface-api

README.md
[![agentmods](https://agentmods.dev/badge/skills/wentorai/research-plugins/huggingface-api.svg)](https://agentmods.dev/skills/wentorai/research-plugins/huggingface-api)
Your own site
<a href="https://agentmods.dev/skills/wentorai/research-plugins/huggingface-api"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/huggingface-api.svg" alt="Measured on agentmods" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,169 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00019 $0.02169
Opus 5 $0.00010 $0.01085
Sonnet 5 $0.00004 $0.00434
Haiku 4.5 $0.00002 $0.00217

Measured 4d ago against content hash 402bcb76cfa3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

huggingface-api scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -H "Authorization: Bearer $HF_TOKEN" https://huggingface.co/api/...
skills/domains/ai-ml/huggingface-api/SKILL.md · 252 lines

How it starts

The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Hugging Face Hub API

Overview

The Hugging Face Hub is the largest open-source ML ecosystem, hosting over 1 million models, 200,000+ datasets, and 400,000+ Spaces (demo apps). The Hub API at https://huggingface.co/api provides programmatic access to search, discover, and retrieve metadata for all public resources without authentication.

For academic researchers, the Hub API enables systematic model selection for benchmarking, dataset discovery for experiments, tracking community adoption metrics (downloads, likes), and building reproducible ML pipelines that reference specific model revisions by SHA.

Authentication

Read endpoints require no authentication. All search and metadata queries work without a token.

For write operations (uploading models, creating repos), set a User Access Token:

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

Generate tokens at: https://huggingface.co/settings/tokens

Core Endpoints

Search Models

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

Parameters: search (query string), limit (max results), sort (field: downloads, likes, lastModified, trending), direction (-1 descending, 1 ascending), filter (pipeline tag like text-classification), author (org/user filter), library (e.g. transformers, pytorch)

Example -- top 2 models for "bert" by downloads:

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"
  }
]

Read the full file on GitHub · 252 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 252 lines · 19 tokens per session scan A 402bcb76cfa3

Subscribe to this mod's changes

huggingface-api is a skill published in the GitHub repository wentorai/research-plugins (285 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 2,169 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens