> browse/registry

> review-analyst-agent

Use this skill to analyze product reviews, find common issues, and prioritize improvements. Triggers: "analyze reviews", "review analysis", "customer feedback", "what are people saying", "product reviews", "review sentiment", "find complaints", "customer complaints", "improvement recommendations", "voice of customer", "VOC analysis", "feedback analysis" Outputs: Prioritized issues, sentiment analysis, improvement recommendations.

> feedback-analysis

Collect, categorize, and synthesize user feedback from multiple channels into actionable product insights. Use when tasks involve analyzing support tickets, app store reviews, NPS survey responses, social media mentions, user interviews, feature request prioritization, sentiment analysis, churn prediction from feedback patterns, or building voice-of-customer reports. Covers multi-channel feedback aggregation and data-driven product decisions.

> pytorch

Assists with building, training, and deploying neural networks using PyTorch. Use when designing architectures for computer vision, NLP, or tabular data, optimizing training with mixed precision and distributed strategies, or exporting models for production inference. Trigger words: pytorch, torch, neural network, deep learning, training loop, cuda.

> azure-ai-language-conversations-py

Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.

> analyzing-text-sentiment

This skill enables Claude to analyze the sentiment of text data. It identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. Use this skill when a user requests sentiment analysis, opinion mining, or emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include "sentiment analysis", "analyze sentiment", "opinion mining", "emotion detection", and "polarity".

> sentiment-analysis

Analyze user feedback data to identify segments with sentiment scores, JTBD, and product satisfaction insights. Use when analyzing user feedback at scale, running sentiment analysis on reviews or surveys, or identifying satisfaction patterns.

> analyzing-text-with-nlp

This skill enables Claude 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 other NLP tasks. The skill is triggered by requests involving "analyze text", "sentiment analysis", "keyword extraction", "topic modeling", or similar phrases related to text processing. It leverages AI/ML techniques to understand and extract insights f

> customer-support

Elite AI-powered customer support specialist mastering conversational AI, automated ticketing, sentiment analysis, and omnichannel support experiences. Integrates modern support tools, chatbot platforms, and CX optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive customer experience management.

> azure-ai-textanalytics-py

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text. Triggers: "text analytics", "sentiment analysis", "entity recognition", "key phrase", "PII detection", "TextAnalyticsClient".