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/spacy-ner/SKILL.md · 1 file library/specializations/ai-agents-conversational/skills/spacy-ner/SKILL.md 1.1 KB · Markdown Rendered Raw ⧉ Copy
name : spacy-ner
description : spaCy NER model training and entity extraction for conversational AI
allowed-tools :
- Read
- Write
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- Grep
spaCy NER Skill Capabilities Train custom spaCy NER models Configure entity extraction pipelines Design annotation schemas Implement entity linking Set up model evaluation Deploy efficient NER inference Target Processes entity-extraction-slot-filling chatbot-design-implementation Implementation Details spaCy Components NER : Named Entity RecognitionEntityLinker : Link to knowledge basesEntityRuler : Rule-based matchingSpanCategorizer : Overlapping entitiesTraining Configuration config.cfg setup Training data format (spaCy v3) Augmentation strategies Evaluation metrics Configuration Options Base model selection (en_core_web_*) Custom entity types Training parameters GPU acceleration Model packaging Best Practices Quality annotation data Balance entity types Use prodigy for annotation Regular model evaluation Dependencies spacy spacy-transformers (optional)