named-entity-extractor

npx skills add majiayu000/claude-skill-registry --skill named-entity-extractor-dkyazzentwatwa-chatgpt-skills-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Named Entity Extractor Extract named entities from text including people, organizations, locations, dates, and more. ## Features - **Entity Types**: People, organizations, locations, dates, money, percentages - **Multiple Models**: spaCy for accuracy, regex for speed - **Batch Processing**: Process multiple documents - **Entity Linking**: Group same entities across text - **Export**: JSON, CSV output formats - **Visualization**: Entity highlighting ## Quick Start ```python from entity_extractor import EntityExtractor extractor = EntityExtractor() text = "Apple Inc. was founded by Steve Jobs in Cupertino, California in 1976." entities = extractor.extract(text) for entity in entities: print(f"{entity['text']}: {entity['type']}") # Output: # Apple Inc.: ORG # Steve Jobs: PERSON # Cupertino: GPE # California: GPE # 1976: DATE ``` ## CLI Usage ```bash # Extract from text python entity_extractor.py --text "Steve Jobs founded Apple in California." # Extract from file python entity_extractor.py --input document.txt # Batch process folder python entity_extractor.py --input ./documents/ --output entities.csv # Filter by entity type python entity_extractor.py --input document.txt --types PE

Extract from text
python entity_extractor.py --text "Steve Jobs founded Apple in California."
Extract from file
python entity_extractor.py --input document.txt
Batch process folder
python entity_extractor.py --input ./documents/ --output entities.csv
Filter by entity type
python entity_extractor.py --input document.txt --types PERSON,ORG
Use regex mode (faster, less accurate)
python entity_extractor.py --input document.txt --mode regex