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Use when analyzing published content (blog posts, social media, newsletters) for sentiment, structural quality, hook effectiveness, readability, topic classification, and engagement correlation. Invoke for daily post analysis, content audits, or engagement driver identification.
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Deep emotional sentiment analysis beyond positive/negative classification
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Multi-platform review analysis — pulls Yelp/Google/TripAdvisor reviews, identifies recurring complaints, sentiment patterns, response rate, and rating trajectory
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Analyze CSAT, NPS comments, reviews, and support feedback for sentiment, recurring themes, customer pain, and service improvement actions. Use for customer support and CX feedback reviews.
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Analyze customer message text for sentiment, urgency, and emotional tone.
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Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.
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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", "TextAnalyticsClien...
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Use when the user asks to "track our share of voice", "what share of the conversation do we own vs competitors", or "trend our SOV this quarter"; computes SOV% = brand mentions ÷ (brand + competitor panel mentions) per platform per period on a LOCKED competitor panel — a panel switch invalidates the trend (restart the series and log the break; the ECHO O3 rule) — plus a sentiment-weighted SOV variant (sentiment labeled Estimated unless human-coded) and a Wikipedia-pageviews attention-share alternative; built from keyless listening connectors, gdelt.py news echo, and user exports — public counts only, closed platforms are never scraped. Not for backlink or offsite SEO signals — use offsite-signal-analyzer. 声量份额/竞品声量对比/提及份额/注意力份额
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Analyze meeting transcripts, recordings, and notes to extract action items, key decisions, topics, sentiment, and generate summaries using NLP and AI. Use when processing meeting transcripts, extracting action items, summarizing discussions, identifying key decisions, tracking topics and themes, analyzing sentiment, generating meeting notes, or building collaboration and productivity tools.
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Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.
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Classify reply sentiment and determine next action
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调查最近30天社区和社交媒体上的讨论，将近期变化、好评、不满及多个来源反复出现的趋势整理为附有出处的Markdown报告，并记录各来源的调查覆盖情况。
通过Flow Nexus在E2B隔离环境中训练和部署神经网络，执行预测、监控分布式训练，并进行性能评估和模型共享。
Turn interviews, surveys, support conversations, and public reviews into evidence-based customer insights. Identify recurring problems, buying triggers, and customer language, then build research reports, quote collections, or personas and plan interviews or surveys to fill gaps.
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Find ranked crypto tokens and trader addresses by social buzz, search popularity, capital inflows, or trading profit and loss. Compare results across supported blockchains, with time windows and filters available for relevant rankings.
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Turn news, company announcements, and policy events into scored trading inputs. Store assessments in a standard CSV file, reduce their influence over time, and combine them with signals from price indicators to support event-based strategy testing.
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Assess whether sales and marketing are working by combining channel revenue, sales conversion, campaign returns, product performance, and customer feedback. Get a concise growth report with prioritized actions supported by available business data.
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Bring together public reviews, complaints, and customer activity to identify recurring issues and customers who may leave. Produce evidence-backed themes, review reply drafts in the owner's voice, and personalized messages to win back inactive customers.
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Maps people involved in a deal or account using CRM records, communications, or supplied files. Shows roles, engagement, and stance with evidence, identifies missing decision makers and dependence on one contact, and suggests paths to new relationships.
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Analyze customer recommendation and satisfaction surveys, NPS and CSAT, from Google Sheets. Group comments into themes, explain weekly score changes and low-score drivers, and post a Slack digest with representative quotes without modifying survey data.
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Investigate sharp stock price moves through company events, regulation, competitors, and market sentiment. Produce an event timeline and evidence-based explanations, showing whether the investment case needs review and where information remains missing.
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Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.
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Assess a brand’s presence on YouTube, Reddit, Wikipedia, LinkedIn, and other sources relevant to AI search. Review mentions, official profiles, and discussion sentiment, then produce a weighted authority score and recommendations for each platform.
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Classify messages, rate content, or estimate probabilities using defined question types. Receive machine-readable answers, with confidence scores for classification and ratings, and apply the same questions to multiple inputs in a batch.
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