LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
Execute Hugging Face Hub operations using the hf CLI. An agent skill from agent-skills-hub/agent-skills-hub.
$ npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agent-skills-hub/agent-skills-hub hugging-face-cli --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agent-skills-hub/agent-skills-hub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/hugging-face-cli .claude/skills/hugging-face-cli && rm -rf skills-srcUse ~/.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/
Install the "hugging-face-cli" agent skill from https://github.com/agent-skills-hub/agent-skills-hub/tree/main/skills/hugging-face-cli into .claude/skills/hugging-face-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agent-skills-hub/agent-skills-hub/tree/main/skills/hugging-face-cliType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agent-skills-hub/agent-skills-hub hugging-face-cli --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agent-skills-hub/agent-skills-hub.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/hugging-face-cli .agents/skills/hugging-face-cli && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hugging-face-cli" agent skill from https://github.com/agent-skills-hub/agent-skills-hub/tree/main/skills/hugging-face-cli into .agents/skills/hugging-face-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agent-skills-hub/agent-skills-hub hugging-face-cli --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agent-skills-hub/agent-skills-hub.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/hugging-face-cli .cursor/skills/hugging-face-cli && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hugging-face-cli" agent skill from https://github.com/agent-skills-hub/agent-skills-hub/tree/main/skills/hugging-face-cli into .cursor/skills/hugging-face-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agent-skills-hub/agent-skills-hub.git --path skills/hugging-face-cli--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agent-skills-hub/agent-skills-hub hugging-face-cli --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agent-skills-hub/agent-skills-hub.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/hugging-face-cli .gemini/skills/hugging-face-cli && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hugging-face-cli" agent skill from https://github.com/agent-skills-hub/agent-skills-hub/tree/main/skills/hugging-face-cli into .gemini/skills/hugging-face-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agent-skills-hub/agent-skills-hub hugging-face-cliInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agent-skills-hub/agent-skills-hub.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/hugging-face-cli .github/skills/hugging-face-cli && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-cli" agent skill from https://github.com/agent-skills-hub/agent-skills-hub/tree/main/skills/hugging-face-cli into .github/skills/hugging-face-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agent-skills-hub/agent-skills-hub hugging-face-cli --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agent-skills-hub/agent-skills-hub.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/hugging-face-cli .opencode/skills/hugging-face-cli && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-cli" agent skill from https://github.com/agent-skills-hub/agent-skills-hub/tree/main/skills/hugging-face-cli into .opencode/skills/hugging-face-cli/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-cli", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
hugging-face-cliExecute Hugging Face Hub operations using the hf CLI. An agent skill from agent-skills-hub/agent-skills-hub.
Hugging Face CLI is an agent skill from agent-skills-hub/agent-skills-hub. Execute Hugging Face Hub operations using the hf CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with Hugging Face. The repository describes itself as: Agent Skills Hub is a global library of AI agent skills that work across OpenClaw, Claude Code, Gemini, Cursor, Antigravity, and more. The licence is MIT.
Read from SKILL.md and the folder at commit efc0b96. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
hfFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
HF_TOKENFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Hugging Face CLI loads about 2k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 268 words of instructions outside code blocks.
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.
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.
The full file from agent-skills-hub/agent-skills-hub at commit efc0b96, republished under its MIT licence (© agent-skills-hub). 268 words, ~1,954 tokens.
.claude/skills/hugging-face-cli/SKILL.md (or your agent's skills folder).The hf CLI provides direct terminal access to the Hugging Face Hub for downloading, uploading, and managing repositories, cache, and compute resources.
Use this skill when:
| Task | Command |
|---|---|
| Login | hf auth login |
| Download model | hf download <repo_id> |
| Download to folder | hf download <repo_id> --local-dir ./path |
| Upload folder | hf upload <repo_id> . . |
| Create repo | hf repo create <name> |
| Create tag | hf repo tag create <repo_id> <tag> |
| Delete files | hf repo-files delete <repo_id> <files> |
| List cache | hf cache ls |
| Remove from cache | hf cache rm <repo_or_revision> |
| List models | hf models ls |
| Get model info | hf models info <model_id> |
| List datasets | hf datasets ls |
| Get dataset info | hf datasets info <dataset_id> |
| List spaces | hf spaces ls |
| Get space info | hf spaces info <space_id> |
| List endpoints | hf endpoints ls |
| Run GPU job | hf jobs run --flavor a10g-small <image> <cmd> |
| Environment info | hf env |
hf auth login # Interactive login
hf auth login --token $HF_TOKEN # Non-interactive
hf auth whoami # Check current user
hf auth list # List stored tokens
hf auth switch # Switch between tokens
hf auth logout # Log outhf download <repo_id> # Full repo to cache
hf download <repo_id> file.safetensors # Specific file
hf download <repo_id> --local-dir ./models # To local directory
hf download <repo_id> --include "*.safetensors" # Filter by pattern
hf download <repo_id> --repo-type dataset # Dataset
hf download <repo_id> --revision v1.0 # Specific versionhf upload <repo_id> . . # Current dir to root
hf upload <repo_id> ./models /weights # Folder to path
hf upload <repo_id> model.safetensors # Single file
hf upload <repo_id> . . --repo-type dataset # Dataset
hf upload <repo_id> . . --create-pr # Create PR
hf upload <repo_id> . . --commit-message="msg" # Custom messagehf repo create <name> # Create model repo
hf repo create <name> --repo-type dataset # Create dataset
hf repo create <name> --private # Private repo
hf repo create <name> --repo-type space --space_sdk gradio # Gradio space
hf repo delete <repo_id> # Delete repo
hf repo move <from_id> <to_id> # Move repo to new namespace
hf repo settings <repo_id> --private true # Update repo settings
hf repo list --repo-type model # List repos
hf repo branch create <repo_id> release-v1 # Create branch
hf repo branch delete <repo_id> release-v1 # Delete branch
hf repo tag create <repo_id> v1.0 # Create tag
hf repo tag list <repo_id> # List tags
hf repo tag delete <repo_id> v1.0 # Delete taghf repo-files delete <repo_id> folder/ # Delete folder
hf repo-files delete <repo_id> "*.txt" # Delete with patternhf cache ls # List cached repos
hf cache ls --revisions # Include individual revisions
hf cache rm model/gpt2 # Remove cached repo
hf cache rm <revision_hash> # Remove cached revision
hf cache prune # Remove detached revisions
hf cache verify gpt2 # Verify checksums from cache# Models
hf models ls # List top trending models
hf models ls --search "MiniMax" --author MiniMaxAI # Search models
hf models ls --filter "text-generation" --limit 20 # Filter by task
hf models info MiniMaxAI/MiniMax-M2.1 # Get model info
# Datasets
hf datasets ls # List top trending datasets
hf datasets ls --search "finepdfs" --sort downloads # Search datasets
hf datasets info HuggingFaceFW/finepdfs # Get dataset info
# Spaces
hf spaces ls # List top trending spaces
hf spaces ls --filter "3d" --limit 10 # Filter by 3D modeling spaces
hf spaces info enzostvs/deepsite # Get space infohf jobs run python:3.12 python script.py # Run on CPU
hf jobs run --flavor a10g-small <image> <cmd> # Run on GPU
hf jobs run --secrets HF_TOKEN <image> <cmd> # With HF token
hf jobs ps # List jobs
hf jobs logs <job_id> # View logs
hf jobs cancel <job_id> # Cancel jobhf endpoints ls # List endpoints
hf endpoints deploy my-endpoint \
--repo openai/gpt-oss-120b \
--framework vllm \
--accelerator gpu \
--instance-size x4 \
--instance-type nvidia-a10g \
--region us-east-1 \
--vendor aws
hf endpoints describe my-endpoint # Show endpoint details
hf endpoints pause my-endpoint # Pause endpoint
hf endpoints resume my-endpoint # Resume endpoint
hf endpoints scale-to-zero my-endpoint # Scale to zero
hf endpoints delete my-endpoint --yes # Delete endpointGPU Flavors: cpu-basic, cpu-upgrade, cpu-xl, t4-small, t4-medium, l4x1, l4x4, l40sx1, l40sx4, l40sx8, a10g-small, a10g-large, a10g-largex2, a10g-largex4, a100-large, h100, h100x8
# Download to local directory for deployment
hf download meta-llama/Llama-3.2-1B-Instruct --local-dir ./model
# Or use cache and get path
MODEL_PATH=$(hf download meta-llama/Llama-3.2-1B-Instruct --quiet)hf repo create my-username/my-model --private
hf upload my-username/my-model ./output . --commit-message="Initial release"
hf repo tag create my-username/my-model v1.0hf upload my-username/my-space . . --repo-type space \
--exclude="logs/*" --delete="*" --commit-message="Sync"hf cache ls # See all cached repos and sizes
hf cache rm model/gpt2 # Remove a repo from cache--repo-type: model (default), dataset, space--revision: Branch, tag, or commit hash--token: Override authentication--quiet: Output only essential info (paths/URLs)© agent-skills-hub, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/hugging-face-cli of agent-skills-hub/agent-skills-hub.
Open the folder on GitHubat commit efc0b96
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 agent-skills-hub/agent-skills-hub, which our catalogue first saw on October 9, 2026.
Hugging Face CLI 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Hugging Face CLI this skillagent-skills-hub/agent-skills-hub | 112 | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Upload Post Imagehuggingface/blog | 3.5k | — | ~1.1k | Automated safety check: Pass | None | |
| Esmfold2JimLiu/science-skills | 228 | 4 repos | ~2.5k | Automated safety check: Pass | Apache-2.0 |
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
huggingface/blog
A skill your agent uses when adding or migrating non-thumbnail images for a Hugging Face Blog post.
JimLiu/science-skills
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.
areal-project/AReaL
Guide for adding a new model to the Archon engine. An agent skill from areal-project/AReaL.
agent-skills-hub/agent-skills-hub
Drives a self-hosted Open Notebook instance to organize sources into notebooks, chat with documents, generate notes and multi-speaker podcasts, and search across material.
agent-skills-hub/agent-skills-hub
Zero-shot time series forecasting with Google's TimesFM foundation model.
agent-skills-hub/agent-skills-hub
Interact with Zotero reference management libraries using the pyzotero Python client.
agent-skills-hub/agent-skills-hub
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API.
agent-skills-hub/agent-skills-hub
Build lightweight SDF/canvas displacement glass with Vaso. An agent skill from agent-skills-hub/agent-skills-hub.
agent-skills-hub/agent-skills-hub
Python library for accessing, analyzing, and extracting data from SEC EDGAR filings.
Works with
Categories
Execute Hugging Face Hub operations using the hf CLI. An agent skill from agent-skills-hub/agent-skills-hub. Hugging Face CLI is an agent skill from agent-skills-hub/agent-skills-hub. Execute Hugging Face Hub operations using the hf CLI.
Hugging Face CLI fits situations like: the user needs to download models/datasets/spaces; upload files to Hub repositories; manage local cache; run compute jobs on HF infrastructure.
Run `npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a claude-code`. Or copy the skill folder (skills/hugging-face-cli in agent-skills-hub/agent-skills-hub) into .claude/skills/hugging-face-cli in your project. Claude Code loads it when a task matches its description.
Run `npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a codex`. Or copy the skill folder (skills/hugging-face-cli in agent-skills-hub/agent-skills-hub) into .agents/skills/hugging-face-cli in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agent-skills-hub/agent-skills-hub --skill hugging-face-cli -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hugging-face-cli, .gemini/skills/hugging-face-cli, .github/skills/hugging-face-cli and .opencode/skills/hugging-face-cli in your project.
Going by SKILL.md and its folder, Hugging Face CLI needs the command-line tools its instructions call (hf) and credentials named HF_TOKEN. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
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.
Hugging Face CLI is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Hugging Face CLI: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face Local Model Evals (huggingface/skills, 11k 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.
agent-skills-hub (a GitHub organization) maintains it in agent-skills-hub/agent-skills-hub, which has 112 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 2, 2026.
Source: agent-skills-hub/agent-skills-hub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.