SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models
Paper • 2610.17727 • Published • 1
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForSequenceClassification
processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-SBR")
model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-SBR", device_map="auto")Quick Links
SkillScreen-2B-SBR
This model is described in the paper: SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models.
2B decision model for detecting malicious agent skills from SKILL.md.
Trained on MaliciousSkillBench using Source-Balanced Random (SBR), with an 8K input budget.
Based on OpenJev 2B. Full weights are at the root. The original LoRA adapter and classification head are in adapter/.
Use
Requires CUDA with BF16 support.
pip install huggingface_hub
hf download SkillScreen/SkillScreen-2B-SBR --revision v1 --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 · Export verification · Project page · Code
Jerrold Huang, Junwei Quan, Saiyue Lyu, Zihan Guo, Yingqiang Ge.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-SBR")