rasa-nlu-integration
Rasa NLU pipeline configuration and training for intent and entity extraction
npx skills add a5c-ai/babysitter --skill rasa-nlu-integration --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 1. **Tokenizers**: WhitespaceTokenizer, SpacyTokenizer 2. **Featurizers**: CountVectorsFeaturizer, SpacyFeaturizer 3. **Classifiers**: DIETClassifier, FallbackClassifier 4. **Entity Extractors**: DIETClassifier, SpacyEntityExtractor ### Configuration 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 - rasa
- Capabilities
- Target Processes
- Implementation Details
- Pipeline Components
- Configuration Files
- Configuration Options
- Best Practices
- Dependencies
What does the rasa-nlu-integration skill do?
Rasa NLU pipeline configuration and training for intent and entity extraction
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill rasa-nlu-integration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.