「sentiment」の検索結果

This skill helps users automatically extract structured article details and full content from Zhihu via the BrowserAct API. Agent should proactively apply this skill when users express needs like: searching for Zhihu articles on a specific topic, tracking industry trends on Zhihu, monitoring public relations or sentiment on Zhihu, collecting competitor updates, getting the latest reports on specific keywords, monitoring brand exposure in Zhihu media, researching market hot topics, summarizing daily Zhihu industry news, retrieving hot events from the past week, extracting structured data for market research, finding full Zhihu articles for AI agents, extracting full article body from Zhihu links.

日本語の概要は準備中です。原文の説明を表示しています。

This skill is designed to help users automatically extract reviews from Google Maps via the Google Maps Reviews API. Agent should proactively apply this skill when users request to find reviews for local businesses (e.g., coffee shops, clinics), monitor customer feedback for a specific brand or location, analyze sentiment of reviews for competitors, extract reviews for a chain of stores or services, track reputation of a local restaurant, gather user testimonials for a specific venue, conduct market research on service quality of local businesses, monitor reviews for a new retail location, collect feedback on public attractions or parks, identify common complaints for a specific service provider, research the best-rated places in a city, analyze recurring themes in reviews for a specific industry.

日本語の概要は準備中です。原文の説明を表示しています。

This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a competitive product, tracking sentiment of recent Amazon reviews, extracting verified purchase reviews for quality assessment, summarizing user experiences from Amazon product pages, monitoring product performance through customer reviews, collecting reviewer profiles and links for market research, gathering review titles and descriptions for content analysis, scraping Amazon reviews without requiring a login.

日本語の概要は準備中です。原文の説明を表示しています。

Extract customer reviews from any e-commerce product page or reviews page. Returns reviewer name, star rating, date, review title, review body, verified purchase status, and helpful votes per review. Works on Amazon, WooCommerce, Shopify, and any site with standard review markup. Supports pagination for multi-page review sections. Use when: product reviews, customer feedback, review scraping, get reviews, sentiment analysis data, review extraction, customer ratings, extract customer opinions, product feedback, user reviews, review mining, bulk review collection, review analysis, scrape ratings and comments, ecommerce review data.

日本語の概要は準備中です。原文の説明を表示しています。

Fetch customer reviews for a Taobao or Tmall product by itemId, returning reviewer name, date, purchased variant, review text, and photo URLs. Use when user asks to get product reviews from Taobao, scrape Taobao customer feedback, extract buyer reviews by item ID, collect Tmall ratings and comments, 采集淘宝商品评价, 抓取淘宝买家评论, 获取淘宝商品评论, 天猫商品评价抓取, 按商品ID获取评价. Also applies to sentiment analysis of product reviews, building review datasets, and monitoring product rating changes.

日本語の概要は準備中です。原文の説明を表示しています。

Connect fundamentals, sentiment and risk to supporting and opposing evidence. Use when the user asks to stock opportunities with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

日本語の概要は準備中です。原文の説明を表示しています。

Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants a social listening report about what people are saying about a brand, person, product, topic, category, or niche across public social platforms. Produces cited themes, sentiment caveats, notable posts, and recommended actions.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.

日本語の概要は準備中です。原文の説明を表示しています。

Read LinkedIn for financial research through opencli: the user's feed, and job search with location, seniority, job type, remote, company, and date filters. Use this skill whenever the user wants to check their LinkedIn feed, see what professionals or analysts are posting about a company, market, or earnings, gauge professional sentiment, or search finance and trading jobs on LinkedIn. Read-only: it cannot post, like, comment, connect, message, or apply.

日本語の概要は準備中です。原文の説明を表示しています。

Calculate an ETF's premium or discount to NAV from Yahoo Finance data (yfinance), compare or screen ETFs by premium, explain why a gap exists, and decompose a sudden ETF move into NAV-driven vs structural components (dealer gamma exposure, blocked AP arbitrage, sentiment). Use this skill whenever the user asks whether an ETF trades above or below NAV, compares ETF premiums or discounts, screens for the biggest ones, asks about ETF arbitrage or premium convergence, or wants to know why an ETF jumped or diverged from its holdings — including gamma squeezes, dealer gamma exposure (GEX), and blocked creation/redemption. Especially relevant for leveraged, inverse, international, bond, commodity, and crypto ETFs (IBIT, BITO, HYG, KWEB).

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to "build our social crisis protocol", "mentions are exploding — what do we do first", or "when do we pause the posting queue"; produces a 1-5 severity ladder with tunable Estimated trigger thresholds (mention-velocity multiples vs the 7-day listening baseline, sentiment flip, journalist/regulator contact, employee-conduct class), the first-mechanical-action rule — pause ALL scheduled posts AND paid amplification, with dated state markers dropped to the channels proposal protocol and reconciled post-incident — a pre-approved holding-statement library with committed update cadences, when-NOT-to-post rules, a spokesperson/approval matrix, and a post-crisis retro template; re-runs the social-quality-auditor pre-publish gate before un-pausing the queue. Not for email deliverability incidents (blocklist, spam-rate spikes) — use deliverability-qa; inside a launch window launch-day-conductor owns incident handling. 社媒危机预案/暂停队列/声明库/发言人矩阵

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to "triage our comments, DMs, and mentions", "draft replies to this thread", "can we repost this fan post", or "set up inbox SLAs and an escalation path"; produces a ranked triage queue with register detection (sincere / ironic / performative / parasocial, sentiment-inversion table included — "this is so bad" under a comedy register is praise), a commenter taxonomy (troll monitor-only / rager / misguided / unhappy-customer / advocate) with a response-tier ladder and per-channel SLAs, an escalation matrix ending at the crisis path, a moderation ladder plus house rules for owned spaces, and a UGC curation-and-rights mode whose dated permission entries route to the channel registry — every reply is a ranked DRAFT a human posts; nothing is ever auto-sent. Not for launch-window feedback triage — use launch-feedback-synthesizer. 评论私信提及分诊/语域识别/回复草稿/UGC授权

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion-attribution breakdowns, and ranked learnings. Not for dollar-level return math — use roi-calculator. 达人营销效果分析/投放复盘

日本語の概要は準備中です。原文の説明を表示しています。

Track crypto options flow to identify institutional positioning and market sentiment. Use when tracking institutional options flow. Trigger with phrases like "track options flow", "analyze derivatives", or "check institutional".

日本語の概要は準備中です。原文の説明を表示しています。

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'.

日本語の概要は準備中です。原文の説明を表示しています。

Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

A-share sector research: computes board-wide Top N gainers, cross-checks week/month announcement windows across two sources, and grades market-sentiment evidence (L1–L3), via parallel Agent Team execution with fresh-context adversarial verification. Use for 「XX 行业 Top N/Top 10 标的」, 「这些标的最近一周/一个月发过哪些公告」, 「判断 XX 行业/板块现在的市场情绪」, or 「用 Agent Team 做行业投研」/对抗验证行业研究结论。

日本語の概要は準備中です。原文の説明を表示しています。

Pulls Bigdata.com (RavenPack) financial and news data via the official bigdata-client SDK and /v1/* REST endpoints — structured financials, prices, analyst estimates, entity-sentiment series, annotated chunk search, screener. Use when the Bigdata MCP's tearsheets/search feel thin and you need the machine-readable substrate: mentions of Bigdata.com, RavenPack, a bd_v2_ key, rp_entity_id, or chunk/query_unit cost.

日本語の概要は準備中です。原文の説明を表示しています。

Use for VidSeeds MCP analytics and research - YouTube analytics, channel intelligence, video autopsy, outliers, competitors, trending/breakout discovery, keyword research, comment sentiment, and best-practices tools. Outcomes only; not internal scoring details.

日本語の概要は準備中です。原文の説明を表示しています。

Produce a decision-ready brief of current brand, product, category, and competitor conversations across social platforms, including sentiment drivers, questions, risks, and growth opportunities.

日本語の概要は準備中です。原文の説明を表示しています。

Aggregate customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review data.

日本語の概要は準備中です。原文の説明を表示しています。

Scrape product reviews from G2, Capterra, and Trustpilot using Apify. Single script with platform dispatch. Use when you need to monitor competitor reviews, track product sentiment, or gather customer feedback from review sites.

日本語の概要は準備中です。原文の説明を表示しています。