Loading...
Loading...
Found 91 Skills
Auto-sort, categorize, or label content using AI. Use when sorting tickets into categories, auto-tagging content, labeling emails, detecting sentiment, routing messages to the right team, triaging support requests, building a spam filter, intent detection, topic classification, or any task where text goes in and a category comes out.
Text analytics using LLM APIs — sentiment analysis, customer feedback classification, document entity extraction, multi-language support (English/Luganda/Swahili), feedback aggregation, and NLP feature implementation for PHP/Android/iOS. Sources...
Brand monitoring tool for tracking mentions across social media platforms. Monitor Reddit, Google News, YouTube, and DuckDuckGo for brand mentions. Includes sentiment analysis, trend tracking, crisis detection, and competitor comparison. No API key required for basic monitoring.
Generate a structured daily market briefing based on YouTuber sentiment. Use when users want a morning report or daily market overview.
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.
Aggregated news, regulatory filings, and Longbridge-community discussion for a single stock — classified into catalyst / regulatory / strategic / financial / opinion / other, with a fact-only key-takeaway summary and a sentiment skew. Falls back to WebSearch only when data is sparse or stale, and labels the source. Triggers: "X 最近新闻", "X 公告", "市场对 X 财报怎么看", "X 社区讨论", "X 公司动态", "市场情绪", "最近怎么了", "X 最近新聞", "X 公告", "市場對 X 財報怎麼看", "X 社區討論", "recent news", "company filings", "market reaction", "what is everyone saying about X", "community sentiment", "8-K", "港交所披露", "earnings reaction".
Community sentiment via Gate-News MCP, X/Twitter-first. Use for social discussion, KOL takes, or opinion on a coin or topic. Triggers: what does the community think about ETH, Twitter or X sentiment, what are people saying, KOL opinions. Reddit, Discord, Telegram when search_ugc is available; until then label output as X/Twitter-only. Tools: news_feed_search_x, news_feed_get_social_sentiment.
Tracks sports team fan sentiment on Twitter using apidojo's Tweet scraper. Triggers when the user asks to: track fan sentiment about a sports team on Twitter, monitor Twitter reactions to sports team news, analyze fan mood after a game result on Twitter, measure public sentiment around a sports team, monitor Twitter buzz around a sports event, analyze fan reactions to player trades or news, or build a sentiment tracker for a sports team's social media presence. Returns sentiment distribution, volume trends, top fan reactions, topic themes, and event-triggered spikes. Ideal for sports marketing teams, brand sponsors, sports analytics firms, and sports media companies.
Generate a comprehensive sentiment analysis report for a single stock. Use when users want deep analysis of a specific ticker like NVDA, TSLA, or AAPL.
Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.
Discover and track emerging trends across Twitter/X, Reddit, YouTube, LinkedIn, and Instagram using anysite MCP server. Identify viral content, monitor topic momentum, detect trending hashtags, analyze search patterns, and track industry shifts. Supports multi-platform trend detection, sentiment analysis, and momentum tracking. Use when users need to identify emerging trends, track viral content, monitor market shifts, discover trending topics, or analyze social media conversations for strategic insights.
When the user wants to build or improve a sales bot's ability to identify emerging objections across campaigns. Also use when the user mentions "objection patterns," "objection learning," "new objection detection," "objection analysis," or "objection flagging."