Huggingface
Hugging Face transformers library and hub. Use for NLP models.
Install
$ agentstack add skill-g1joshi-agent-skills-huggingface ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-g1joshi-agent-skills-huggingface) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Hugging Face
Hugging Face is the GitHub of AI. It hosts 1M+ models. 2025 sees massive growth in Multimodal models and Robotics (LeRobot).
When to Use
- Model Discovery: Finding the SOTA open-source model for any task.
- Inference:
transformerslibrary is the standard way to run models in Python. - Datasets: Accessing standard datasets (
load_dataset('squad')).
Core Concepts
Transformers Library
The API to download and run models. pipeline('sentiment-analysis').
Hugging Face Hub (Hugging Face CLI)
Versioning, git-based storage for large model weights (git lfs).
Spaces
Hosting simple Gradio/Streamlit apps for model demos.
Best Practices (2025)
Do:
- Use
bitsandbytes: Load 70B models in 4-bit precision easily. - Use
accelerate: For multi-GPU training/inference distributed across devices. - Push to Hub: Share your fine-tunes.
Don't:
- Don't hardcode paths: Use
from_pretrained("repo/id")to auto-cache models.
References
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: G1Joshi
- Source: G1Joshi/Agent-Skills
- License: MIT
- Homepage: https://skills.sh/g1joshi/agent-skills
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.