
analyzing-text-with-nlp
by jeremylongshore
Hundreds of Claude Code plugins with embedded AI skills. Learn via interactive Jupyter tutorials.
SKILL.md
name: analyzing-text-with-nlp description: | Execute this skill enables AI assistant to perform natural language processing and text analysis using the nlp-text-analyzer plugin. it should be used when the user requests analysis of text, including sentiment analysis, keyword extraction, topic modeling, or ... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'. allowed-tools: Read, Bash(cmd:*), Grep, Glob version: 1.0.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT
Nlp Text Analyzer
This skill provides automated assistance for nlp text analyzer tasks.
Overview
This skill empowers Claude to analyze text using the nlp-text-analyzer plugin, extracting meaningful information and insights. It facilitates tasks such as sentiment analysis, keyword extraction, and topic modeling, enabling a deeper understanding of textual data.
How It Works
- Request Analysis: Claude receives a user request to analyze text.
- Text Processing: The nlp-text-analyzer plugin processes the text using NLP techniques.
- Insight Extraction: The plugin extracts insights such as sentiment, keywords, and topics.
When to Use This Skill
This skill activates when you need to:
- Perform sentiment analysis on a piece of text.
- Extract keywords from a document.
- Identify the main topics discussed in a text.
Examples
Example 1: Sentiment Analysis
User request: "Analyze the sentiment of this product review: 'I loved the product! It exceeded my expectations.'"
The skill will:
- Process the review text using the nlp-text-analyzer plugin.
- Determine the sentiment as positive and provide a confidence score.
Example 2: Keyword Extraction
User request: "Extract the keywords from this news article about the latest AI advancements."
The skill will:
- Process the article text using the nlp-text-analyzer plugin.
- Identify and return a list of relevant keywords, such as "AI", "advancements", "machine learning", and "neural networks".
Best Practices
- Clarity: Be specific in your requests to ensure accurate and relevant analysis.
- Context: Provide sufficient context to improve the quality of the analysis.
- Iteration: Refine your requests based on the initial results to achieve the desired outcome.
Integration
This skill can be integrated with other tools to provide a comprehensive workflow, such as using the extracted keywords to perform further research or using sentiment analysis to categorize customer feedback.
Prerequisites
- Appropriate file access permissions
- Required dependencies installed
Instructions
- Invoke this skill when the trigger conditions are met
- Provide necessary context and parameters
- Review the generated output
- Apply modifications as needed
Output
The skill produces structured output relevant to the task.
Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
Resources
- Project documentation
- Related skills and commands
Score
Total Score
Based on repository quality metrics
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GitHub Stars 1000以上
1ヶ月以内に更新
10回以上フォークされている
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Reviews
Reviews coming soon

