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Sentiment & Momentum Analysis — news sentiment scoring, social media buzz (Reddit/StockTwits/X), analyst ratings consensus, institutional 13F activity, insider trading signals, and short interest / squeeze potential. Returns Sentiment Score (0-100). Triggered by "trade sentiment <TICKER>".
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Research recent X/Twitter reactions to markets, companies, products, or communities. Produce a reusable Markdown briefing that groups supportive, critical, and neutral views, with citations, coverage details, and an assessment of overall sentiment.
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Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum. Use when gauging overall market mood, checking if markets are fearful or greedy, or analyzing sentiment for specific coins. Trigger with phrases like "analyze crypto sentiment", "check market mood", "is the market fearful", "sentiment for Bitcoin", or "Fear and Greed index".
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Amazon niche market review analysis and consumer sentiment insights. Trigger when the user mentions niche market review analysis, consumer sentiment, user pain points, customer feedback insights, review topic analysis, positive/negative review breakdown, niche market opinion mining, product review sentiment analysis, niche market reviews, consumer sentiment, customer pain points, review topic analysis, positive/negative reviews, opinion mining, Nexscope data. Even if the user does not explicitly mention "niche market reviews", if their need involves analyzing consumer reviews within Amazon niche markets or understanding customer sentiment at the niche market level, this skill should also be triggered.
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"Implement VADER sentiment analysis for social media text scoring. Use this skill when the user needs to analyze sentiment in tweets, reviews, or social posts, compute compound sentiment scores, or classify text polarity — even if they say 'is this positive or negative', 'sentiment of these comments', or 'social media mood analysis'.".
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Analyze brand or topic sentiment across Twitter, Reddit, and Instagram using Xpoz. Classifies posts as positive/neutral/negative, extracts recurring themes, and generates a sentiment report. Use when asked for "sentiment analysis", "what are people saying about X", "brand sentiment", or "social media opinion on X".
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Analyze brand or topic sentiment across Twitter, Reddit, and Instagram using Xpoz. Classifies posts as positive/neutral/negative, extracts recurring themes, and generates a sentiment report. Use when asked for "sentiment analysis", "what are people saying about X", "brand sentiment", or "social media opinion on X".
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Collect financial discussions from Twitter/X, Telegram, Discord, and Reddit, then measure sentiment and discussion volume. Use the resulting data to study market attention, distinguish author groups, and test whether social signals relate to future returns.
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Assess market sentiment using fear and greed measures, options activity, margin trading, foreign investment flows, and social media discussion. Combine indicators on a common scale to produce a dashboard and explain signs of excessive optimism or fear.
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Sentiment & Momentum Analysis Agent — news sentiment, social media buzz, analyst ratings, institutional activity, insider trading, and short interest with Sentiment Score (0-100)
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Earnings sentiment analysis, analyst estimates vs guidance, earnings surprise history, consensus sentiment, earnings revision trends, analyst rating changes, earnings beat miss track record, guidance accuracy, whisper numbers, pre-announcement sentiment
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Analyze sentiment in any text — reviews, social posts, feedback, or articles. Returns sentiment polarity (positive, negative, neutral), confidence score, and key phrases driving the sentiment. Useful for brand monitoring, customer feedback analysis, and content evaluation.
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Crypto Sentiment Analysis Agent — social media buzz, news tone, community health, developer activity, narrative momentum, and Fear & Greed Index with Sentiment Score (0-100)
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Build Python text analysis with Azure AI Language to detect sentiment, names, key phrases, languages, and personal information. Examples also cover healthcare terminology extraction, combined analyses, authentication, and handling results for multiple documents.
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Use investor psychology to examine trading signals and risk controls, especially in China A-shares. The skill provides sentiment measures, checklists for biased decisions, and frameworks for assessing momentum, which follows trends, and reversal strategies.
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Analyze reviews, surveys, and other user feedback by user group. Summarize satisfaction scores, recurring complaints, positive themes, and the goals users want to achieve, then recommend product improvements based on frequency, severity, and business impact.
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Research decentralized exchange activity using read-only data on token prices, holders, trades, news, and sentiment. Track large wallets, examine new token launches, and set up real-time monitoring through CLI commands or custom WebSocket scripts.
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Collect Trustpilot reviews for a company domain, with filters for ratings, language, dates, and reply status. Get structured review text, reviewer details, and company responses across pages to support reputation research and customer service analysis.
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Collect customer reviews from e-commerce product and review pages, including review text, ratings, dates, verified purchase status, and helpful votes. Gather multiple pages of feedback to prepare data for customer opinion research and sentiment analysis.
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Fetch normalized stock sentiment across Reddit, X.com, financial news, and Polymarket from the Adanos Finance API: buzz score, bullish percentage, mention or trade counts, and trend. Use this skill whenever the user asks how much a stock is being discussed or how hot it is on social platforms, how many Reddit mentions or Polymarket bets a company has, whether Reddit, X, and news agree, or wants to compare stock buzz and sentiment across tickers. Read-only: it never places trades or modifies anything.
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Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.
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Execute this skill enables AI assistant 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 emoti... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
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Get recent news and sentiment for a stock. Use when user asks about news, headlines, sentiment, what's happening with a stock, or recent developments.
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Analyze the sentiment or mood conveyed by an image with a local vision LLM. Wraps kuzco's tool_llm_image_sentiment() ellmer tool. Use when the task needs the emotional tone of an image, e.g. for content moderation, marketing, or curation.
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