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/rasa-nlu-integration/SKILL.md · 1 file library/specializations/ai-agents-conversational/skills/rasa-nlu-integration/SKILL.md 1.3 KB · Markdown Rendered Raw ⧉ Copy
name : rasa-nlu-integration
description : Rasa NLU pipeline configuration and training for intent and entity extraction
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Rasa NLU Integration Skill Capabilities Configure Rasa NLU pipelines Design training data in Rasa format Set up intent classification components Configure entity extraction (DIETClassifier) Implement pipeline optimization Set up model evaluation and testing Target Processes intent-classification-system chatbot-design-implementation Implementation Details Pipeline Components Tokenizers : WhitespaceTokenizer, SpacyTokenizerFeaturizers : CountVectorsFeaturizer, SpacyFeaturizerClassifiers : DIETClassifier, FallbackClassifierEntity Extractors : DIETClassifier, SpacyEntityExtractorConfiguration Files config.yml: Pipeline configuration nlu.yml: Training data domain.yml: Intents and entities Configuration Options Pipeline component selection Featurizer settings Classifier parameters Entity extraction rules Fallback thresholds Best Practices Start with recommended pipelines Tune based on domain Balance complexity vs performance Regular model retraining Dependencies