# Rasa Nlu Integration

> Rasa NLU pipeline configuration and training for intent and entity extraction

- Skill: `a5c-ai/rasa-nlu-integration` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add a5c-ai/rasa-nlu-integration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/rasa-nlu-integration/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/rasa-nlu-integration

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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

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