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.
npx skills add ihatesea69/HieuNghi-AI-Skills --skill hugging-face-cligit clone --depth 1 https://github.com/ihatesea69/HieuNghi-AI-SkillsWrote 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.
[](https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/hugging-face-cli)<a href="https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/hugging-face-cli"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/hugging-face-cli/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/hugging-face-cli"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/hugging-face-cli.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.01804 |
| Opus 5 | $0.00034 | $0.00902 |
| Sonnet 5 | $0.00014 | $0.00361 |
| Haiku 4.5 | $0.00007 | $0.00180 |
Grade A, and why
hugging-face-cli scanned grade A with 0 findings 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 7d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to hugging-face-cli — 372 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hugging Face CLI
The hf CLI provides direct terminal access to the Hugging Face Hub for downloading, uploading, and managing repositories, cache, and compute resources.
Quick Command Reference
| 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 |
Core Commands
Authentication
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 out
Download
hf 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 version
Upload
hf 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 message
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 187 lines · 68 tokens per session scan A c011e33ff672
hugging-face-cli is a skill published in the GitHub repository ihatesea69/HieuNghi-AI-Skills (3 stars, last pushed 6mo ago), licensed MIT. It adds 68 tokens to every session and 1,804 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to hugging-face-cli, differing in 372 lines, and is treated as a copy.
Other skills, from other repositories
render-html
A command-line tool for managing GPU jobs on the Qizhi computing platform, a service for running machine-learning workloads on shared graphics processors.
serverless-modal
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says "modal run", "modal training", "modal inference", "deploy to modal", "need a GPU", "run on modal", "serverless GPU", or needs remote GPU compute.
qzcli
A command-line tool for managing GPU compute jobs on the Qizhi platform, a service for running machine-learning workloads on shared GPUs.
deepspeed
Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention.
ultralytics-platform
This skill should be used when user asks to "upload my model to Ultralytics Platform", "push this run to the platform", "upload a dataset to platform", "download a dataset from platform", "search platform datasets", "start cloud training", "train on platform GPUs", "export a model on platform", "deploy a model…
vllm
Operate, configure, benchmark, and troubleshoot vLLM inference servers: Docker and Kubernetes deployment, quantization-aware model configuration (tensor parallelism, KV cache), OpenAI-compatible API serving, throughput and latency benchmarking, continuous batching tuning, GPU operation, and upgrade/rollback. Use when…