Verified:GitHub

nlp-pipeline

Automate tokenization, NER, classification, summarization, sentiment analysis, and relation extraction.

4|1|Updated Jan 7, 2026

Search Tags:#summarization #nlp #sentiment analysis #text analysis #named entity recognition #relation extraction #text classification

Ready to run

npx skills add https://github.com/an8079/take-skills --skill nlp-pipeline-an8079

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nlp-pipeline
Source: https://github.com/an8079/take-skills/tree/main/skills/nlp-pipeline
Command: npx skills add https://github.com/an8079/take-skills --skill nlp-pipeline-an8079

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, datasets, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates complex natural language processing tasks, transforming raw text into structured, actionable insights.

Core Features & Use Cases

Comprehensive NLP Suite: Perform tokenization, Named Entity Recognition (NER), text classification, summarization, sentiment analysis, and relation extraction.
Knowledge Graph Construction: Extract entities and relationships to build knowledge graphs from documents.
Document Analysis: Analyze sentiment, classify content, and summarize long texts for quick understanding.
Use Case: Process customer feedback to identify key issues (NER), gauge overall sentiment, and categorize feedback types, enabling targeted product improvements.

Quick Start

Use the nlp-pipeline skill to extract named entities and analyze the sentiment of the provided text.

Frequently Asked Questions about nlp-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate named entity recognition and sentiment analysis on customer feedback?▼

You can automate named entity recognition and sentiment analysis on customer feedback by applying an NLP pipeline that leverages Hugging Face Transformers to process raw text and extract structured insights.

Can I build a knowledge graph from raw documents using relation extraction?▼

Yes, you can build a knowledge graph from raw documents by using an NLP pipeline to perform relation extraction and named entity recognition, identifying entities and their relationships to structure the data.

Does this text analysis pipeline work with Hugging Face Transformers and PyTorch?▼

Yes, this text analysis pipeline works directly with Hugging Face Transformers and PyTorch. It leverages these dependencies to provide robust model performance for tasks like tokenization and text classification.

What is the best way to summarize long texts and classify their content?▼

The best way to summarize long texts and classify their content is using a comprehensive NLP suite that automates summarization and text classification, transforming lengthy documents into quick, structured insights.

Do I need to manually tokenize text before performing relation extraction?▼

No, you do not need to manually tokenize text before performing relation extraction. This NLP pipeline automates tokenization as part of its suite, preparing raw text directly for downstream analysis.

What NLP tasks are included in an automated text analysis pipeline?▼

An automated text analysis pipeline includes tokenization, named entity recognition, text classification, summarization, sentiment analysis, and relation extraction to transform raw text into actionable insights.