Skills transformers
🤖

transformers

Safe ⚙️ External commands⚡ Contains scripts🌐 Network access

Deploy AI Models with Hugging Face Transformers

Also available from: davila7

Stop wrestling with complex ML model setup. This skill gives you instant access to thousands of pre-trained transformer models for text, image, and audio tasks through simple commands.

Supports: Claude Codex Code(CC)
📊 71 Adequate
1

Download the skill ZIP

2

Upload in Claude

Go to Settings → Capabilities → Skills → Upload skill

3

Toggle on and start using

Test it

Using "transformers". Generate a Python script that loads GPT-2 and writes a short story about space exploration

Expected outcome:

  • Loaded GPT-2 model from Hugging Face
  • Generated 150-word story about Mars mission
  • Saved story to 'space_story.txt'
  • Story preview: 'The red dust of Mars swirled around the habitat as Commander Chen prepared for the most important spacewalk in human history...'

Using "transformers". Classify these images as either cat or dog

Expected outcome:

  • Loaded vision transformer model
  • Analyzed 5 test images
  • Results: 3 cats (60%), 2 dogs (40%)
  • Highest confidence: image3.jpg (97% cat)

Using "transformers". Summarize this research paper paragraph

Expected outcome:

  • Loaded summarization pipeline with facebook/bart-large
  • Generated 3-sentence summary
  • Key points preserved: methodology, results, conclusions

Security Audit

Safe
v4 • 1/17/2026

All 412 static findings are false positives. The skill contains markdown documentation with Python code examples for Hugging Face Transformers library usage. Bash command patterns in documentation (pip install examples), URL references, and technical terminology triggered the analyzer incorrectly. No executable code, network requests, or security vulnerabilities present.

7
Files scanned
2,527
Lines analyzed
3
findings
4
Total audits

Risk Factors

⚙️ External commands (5)
SKILL.md:20-32 references/generation.md:5-25 references/models.md:13-15 references/pipelines.md:11-20 references/training.md:11-34
⚡ Contains scripts (1)
references/models.md:210-220
🌐 Network access (2)
SKILL.md:48 references/pipelines.md:295-300
Audited by: claude View Audit History →

Quality Score

45
Architecture
100
Maintainability
85
Content
20
Community
100
Security
91
Spec Compliance

What You Can Build

Quick prototype NLP models

Test different transformer models for text classification without writing boilerplate code

Compare model performance

Benchmark multiple pre-trained models on your dataset to find the best performer

Add AI to applications

Integrate text generation or image classification into your app with minimal setup

Try These Prompts

Basic text generation 
Load GPT-2 model and generate 3 variations of: 'The future of AI is'
Sentiment analysis 
Create a pipeline to analyze sentiment of these reviews: ['Great product!', 'Terrible experience', 'Average quality']
Model comparison 
Compare BERT, RoBERTa, and DistilBERT on text classification accuracy using my dataset
Custom fine-tuning 
Fine-tune BERT on my CSV file with 'text' and 'label' columns for 3 epochs

Best Practices

  • Use pipeline API for quick tasks, custom models for fine control
  • Check model card for usage restrictions and biases before deployment
  • Start with smaller models for testing, scale up for production

Avoid

  • Do not fine-tune on sensitive data without checking model license
  • Avoid loading massive models on CPU-only systems
  • Never deploy models without testing outputs for your use case

Frequently Asked Questions

Do I need a GPU?
Not required but recommended. Small models work on CPU, large models need GPU for reasonable speed.
Are models free to use?
Most are free for research and commercial use. Check each model's license on its Hugging Face page.
How do I handle rate limits?
Download models locally first time, then load from cache. Use offline mode for privacy.
Can I use custom datasets?
Yes, the skill supports fine-tuning on CSV, JSON, and other formats through the Trainer API.
Which model should I choose?
Start with established models like BERT for NLP, ViT for vision. Check leaderboards for latest models.
How do I improve generation quality?
Adjust temperature between 0.1 and 1.0, use top-k or top-p sampling, and provide clear specific prompts.

Developer Details

Author

K-Dense-AI

License

Apache-2.0 license

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/transformers

Ref

main

File structure

📁 references/

📄 generation.md

📄 models.md

📄 pipelines.md

📄 tokenizers.md

📄 training.md

📄 SKILL.md