Score breakdown0 Version history1 Source
Category Weight Category score Contribution
Security prompt, exec, net, exfil, eval
35%
100
35.0 pts
Supply chain hash, typosquat, maintainer, lockfile
20%
100
20.0 pts
Maintenance staleness, pinning, CI
15%
100
15.0 pts
Transparency SKILL.md, perms, README
15%
100
15.0 pts
Community installs, verify, response
15%
100
15.0 pts
Findings & checks · 0 flagged
Security score 100 · 0 findings
✓ — All security checks passedNo findings in this category for the latest scan. pass
Supply chain score 100 · 0 findings
✓ — All supply chain checks passedNo findings in this category for the latest scan. pass
Maintenance score 100 · 0 findings
✓ — All maintenance checks passedNo findings in this category for the latest scan. pass
Transparency score 100 · 0 findings
✓ — All transparency checks passedNo findings in this category for the latest scan. pass
Community score 100 · 0 findings
✓ — All community checks passedNo findings in this category for the latest scan. pass
Every scanned point with the score it earned and what moved between them.
1 scans · 90 days d97a2e4 latest
Jun 23, 2026 100 d97a2e4
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
library/specializations/ai-agents-conversational/skills/huggingface-classifier/SKILL.md · 1 file library/specializations/ai-agents-conversational/skills/huggingface-classifier/SKILL.md 1.2 KB · Markdown Rendered Raw ⧉ Copy
name : huggingface-classifier
description : Hugging Face transformer model fine-tuning and inference for intent classification
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HuggingFace Classifier Skill Capabilities Fine-tune transformer models for classification Configure training pipelines with Trainer API Implement inference with optimizations Design label schemas and mappings Set up model evaluation and metrics Deploy models with HF Inference API Target Processes intent-classification-system entity-extraction-slot-filling Implementation Details Model Types BERT-based : bert-base-uncased, distilbertRoBERTa-based : roberta-base, xlm-robertaDeBERTa : deberta-v3-baseDomain-specific : FinBERT, BioBERTTraining Configuration Dataset preparation Tokenization settings Training arguments Evaluation metrics Early stopping Configuration Options Model selection Number of labels Training hyperparameters Batch sizes Learning rate schedules Best Practices Use appropriate base model Proper train/val/test splits Monitor for overfitting Evaluate on representative data Dependencies transformers datasets accelerate