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huggingfacehuggingface 命令行

Agent Skill

huggingface 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

28,873

周安装

1,168

GitHub Stars

1

下载量

9,064
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:huggingface(huggingface 命令行)
来源仓库:https://github.com/tsukisama9292/huggingface
安装命令:
openclaw skills install huggingface
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install huggingface

简介

使用 Hugging Face CLI (hf) 管理模型、数据集、空间和存储库。支持认证、上传、下载、创建空间等。

SKILL.md

name
huggingface
description
Manage models, datasets, Spaces, and repositories using Hugging Face CLI (hf). Supports authentication, upload, download, Space creation, and more.
metadata
{"openclaw":{"emoji":"🤗","requires":{"bins":["hf"],"env":["HF_TOKEN"]}}}

Hugging Face CLI Skill

Use Hugging Face Hub CLI (hf) for various operations.

Environment Variables

  • HF_TOKEN: Hugging Face API Token (get from https://huggingface.co/settings/tokens)

Core Features

1. Authentication Management (hf auth)

# Check login status
hf auth whoami

# List all tokens
hf auth list

# Login
hf auth login

# Logout
hf auth logout

# Switch token
hf auth switch

2. Model Management (hf models)

# List models (supports sorting and filtering)
hf models ls --sort downloads --limit 10
hf models ls --search "llama"

# Get model info
hf models info meta-llama/Llama-3.2-1B-Instruct

3. Dataset Management (hf datasets)

# List datasets
hf datasets ls --limit 10
hf datasets ls --search "imagenet"

# Get dataset info
hf datasets info HuggingFaceFW/fineweb

4. Spaces Management (hf spaces)

# List Spaces
hf spaces ls --limit 10

# Get Space info
hf spaces info username/repo-name

# Hot-reload (experimental, for Gradio 6.1+)
hf spaces hot-reload username/repo-name app.py
hf spaces hot-reload username/repo-name -f ./local/app.py

5. Repository Management (hf repos)

# Create new repository
hf repos create my-model --type model
hf repos create my-dataset --type dataset
hf repos create my-space --type space

# Delete repository
hf repos delete username/repo-name

# Set as private
hf repos settings username/repo-name --private

# Manage branches
hf repos branch create username/repo-name feature-branch
hf repos branch delete username/repo-name feature-branch

# Manage tags
hf repos tag create username/repo-name v1.0
hf repos tag delete username/repo-name v1.0

# Move repository to another namespace
hf repos move old-namespace/my-model new-namespace/my-model

6. Download Files (hf download)

# Download entire model
hf download meta-llama/Llama-3.2-1B-Instruct

# Download specific files
hf download meta-llama/Llama-3.2-1B-Instruct config.json tokenizer.json

# Download with glob patterns
hf download meta-llama/Llama-3.2-1B-Instruct --include "*.safetensors"
hf download meta-llama/Llama-3.2-1B-Instruct --include "*.json" --exclude "*.bin"

# Download to local directory
hf download meta-llama/Llama-3.2-1B-Instruct --local-dir ./models/llama

# Download dataset
hf download HuggingFaceM4/FineVision --repo-type dataset

7. Upload Files (hf upload)

# Upload entire directory
hf upload my-cool-model . .

# Upload single file
hf upload username/my-model ./models/model.safetensors

# Upload to dataset
hf upload username/my-dataset ./data /train --repo-type dataset

# With commit message
hf upload username/my-model ./models . --commit-message="Epoch 34/50" --commit-description="Val accuracy: 68%"

# Create Pull Request
hf upload bigcode/the-stack . . --repo-type dataset --create-pr

# Create private repository
hf upload username/my-private-model . . --private

8. Collection Management (hf collections)

# Create collection
hf collections create "My Models"

# Add item to collection
hf collections add-item username/my-collection moonshotai/kimi-k2 model

# List collections
hf collections ls

# Get collection info
hf collections info username/my-collection

# Update collection
hf collections update username/my-collection --title "New Title"

# Update collection item
hf collections update-item username/my-collection ITEM_OBJECT_ID --note "Updated note"

# Delete item
hf collections delete-item username/my-collection ITEM_OBJECT_ID

# Delete collection
hf collections delete username/my-collection

Usage Examples

Example 1: Download and Upload Model

# Download model
hf download meta-llama/Llama-3.2-1B-Instruct --local-dir ./llama-model

# Upload to your repository
hf upload username/my-llama ./llama-model .

Example 2: Manage Space

# Create Space
hf repos create my-app --type space

# Upload code
hf upload username/my-app ./app.py

# Hot-reload for development
hf spaces hot-reload username/my-app app.py

Example 3: Batch Operations

# Download all safetensors files
hf download meta-llama/Llama-3.2-1B-Instruct --include "*.safetensors"

# Upload and create PR
hf upload username/model . . --create-pr --commit-message="Update model"

Notes

  1. Token Management: Ensure HF_TOKEN environment variable is set, or use --token parameter
  2. Large File Upload: For large folders, consider using hf upload-large-folder
  3. Space Hot-Reload: Only works with Gradio 6.1+, experimental feature
  4. Free Space Limits:

- Free fixed vCPU: 2 - RAM: 16GB - No persistent storage (use external storage or HF Datasets)

Resources

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

73.21%
按下载量换算6,636

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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