Instructions to use SkillScreen/SkillScreen-2B-MSD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillScreen/SkillScreen-2B-MSD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-MSD")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-MSD") model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-MSD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,115 Bytes
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library_name: transformers
pipeline_tag: text-classification
license: apache-2.0
base_model: AlexWortega/openjev
datasets:
- ProtectSkills/MaliciousSkillBench
tags:
- skillscreen
- agent-security
- nli
- msd
---
# SkillScreen-2B-MSD
This model is described in the paper: [SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models](https://huggingface.co/papers/2610.17727).
2B decision model for detecting malicious agent skills from `SKILL.md`. Trained on MaliciousSkillBench using Malicious-Structural-Disjoint (MSD), with an 8K input budget.
Based on [OpenJev 2B](https://huggingface.co/AlexWortega/openjev). Full weights are at the root. The original LoRA adapter and classification head are in `adapter/`.
## Use
Requires CUDA with BF16 support.
```bash
pip install huggingface_hub
hf download SkillScreen/SkillScreen-2B-MSD --revision v1.1 --local-dir skillscreen
pip install -r skillscreen/requirements.txt
python skillscreen/score.py path/to/SKILL.md
```
Native maliciousness score **≥ 0.5** is flagged as suspicious.
[Checkpoint details](provenance.json) · [Export verification](merge_verification.json) · [Project page](https://jerroldh.github.io/SkillScreen-webpage/) · [Code](https://github.com/JerroldH/SkillScreen)
Jerrold Huang, Junwei Quan, Saiyue Lyu, Zihan Guo, Yingqiang Ge.
## Citation
If you use SkillScreen's code, models, or predictions, or build models based on SkillScreen in your research, please cite our paper. If you release a derived model, please identify SkillScreen as its source and link to the original checkpoint.
```bibtex
@article{huang2026skillscreen,
title = {SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models},
author = {Huang, Jerrold and Quan, Junwei and Lyu, Saiyue and Guo, Zihan and Ge, Yingqiang},
journal = {arXiv preprint arXiv:2610.17727},
year = {2026},
doi = {10.48550/arXiv.2610.17727},
url = {https://arxiv.org/abs/2610.17727}
}
```
Machine-readable citation metadata is provided in [CITATION.cff](CITATION.cff). These citation requests do not add conditions to the licenses below.
## License
Model weights (including the LoRA adapter and classification head), project-authored code, configuration, and code examples are licensed under [Apache-2.0](LICENSE), for rights held by the SkillScreen contributors. The model card, documentation, citation metadata, and released provenance/evaluation reports are licensed under [CC BY 4.0](LICENSE-CC-BY-4.0).
Research and commercial use, fine-tuning, and redistribution are permitted under these licenses without individual permission requests. Preserve the applicable attribution and license notices, and identify modifications as required by each license.
Upstream OpenJev and Qwen rights and notices remain applicable. See [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md), [LICENSE-OPENJEV](LICENSE-OPENJEV), and [LICENSE-QWEN](LICENSE-QWEN).
The `v1.1` revision updates licensing and citation information. Its model weights, adapters, tokenizer, scoring code, and configurations are unchanged from `v1`.
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