「trends」の検索結果

[Research] Use when you need to analyze market landscape: competitors, sizing (TAM/SAM/SOM), trends, SWOT, customer segments.

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。

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。

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 should be used when analyzing bug ticket data to identify quality trends, assess software quality status, and provide improvement recommendations. Use when a user provides bug lists (CSV, Excel, JSON, or Markdown format) and needs comprehensive quality analysis from a QA manager perspective. Generates statistical analysis, identifies quality hotspots, provides actionable recommendations, and produces professional Markdown reports suitable for project managers and executive leadership. Ideal for IT system development, migration, and implementation projects.

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Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

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Perform comprehensive, multi-source research on any topic using AI-powered web search and analysis. Covers deep_research for broad discovery and semantic_content_search for site-specific knowledge retrieval. Use when you need to research brands, markets, competitors, or trends with citations — before creating content, planning campaigns, or making strategic decisions.

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Produces marketing performance reports and data-driven recommendations from WooCommerce sales data, social media metrics, and campaign analytics. Use when the task asks for "campaign performance", "ROI analysis", "monthly report", "sales trends", "product velocity", "audience insights", or "KPI tracking". Covers metric definition, reporting templates, slow-mover detection, and insight-to-action frameworks.

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Analyze Profound AI-visibility and citation-tracking data when an authorized connector is available. Use for time-series brand mentions, cited prompts, competitor co-mentions, and AI-answer visibility trends.

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Implements data visualization charts in Flutter using the fl_chart package (v1.2.0). Supports LineChart, BarChart, PieChart, ScatterChart, RadarChart, and CandlestickChart with implicit animations, touch interactivity, and pan/zoom transformations. Use when building any Flutter chart, graph, or data visualization — including line graphs for trends, bar charts for comparisons, pie/donut charts for proportions, scatter plots for distributions, radar charts for multi-axis comparisons, or candlestick charts for financial OHLC data. Also use when customizing chart axes, titles, tooltips, grid lines, touch callbacks, chart animations, or chart zoom/pan behavior.

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Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, Google Maps, Google Search, Google Trends, Booking.com, and TripAdvisor. Use for lead gener...

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 working with Coveralls — coveralls coverage tracking management. Covers coverage reporting, PR status checks, repository configuration, coverage history, and badge management. Use when managing Coveralls coverage reports, configuring coverage thresholds, debugging coverage uploads, or analyzing coverage trends.

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Use when working with Codeclimate — code Climate quality management. Covers maintainability analysis, test coverage tracking, issue detection, GPA scoring, and repository configuration. Use when managing Code Climate projects, reviewing maintainability ratings, tracking test coverage trends, or configuring analysis engines.

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Use when working with Codacy — codacy code quality management. Covers automated code reviews, pattern configuration, coverage tracking, security analysis, and repository settings. Use when managing Codacy projects, reviewing code patterns, configuring quality gates, or tracking code quality trends.

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 working with Blameless — blameless incident management, SLO tracking, retrospectives, and reliability insights. Covers incident lifecycle, blameless retrospective facilitation, follow-up tracking, reliability scorecards, and incident type categorization. Use when managing active incidents, conducting retrospectives, tracking SLO compliance, or analyzing reliability trends in Blameless.

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 working with Apache Airflow Deep — apache Airflow deep management covering DAG inventory, task instance monitoring, scheduler health, executor status, pool and variable management, connection auditing, and SLA miss tracking. Use when investigating DAG failures, analyzing task duration trends, monitoring scheduler performance, or auditing Airflow configurations.

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Use when working with Allure Testops — allure TestOps test management and analytics platform monitoring. Covers project dashboards, launch tracking, test case management, defect clustering, environment matrix analysis, and CI/CD integration review. Use when managing test cases in Allure TestOps, reviewing launch results, analyzing flaky tests, or tracking quality trends.

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 working with Aws Billing — analyze, break down, and report AWS costs and bills. Covers cost breakdown by service, account, or usage type; monthly/daily billing trends; cost anomaly detection; RI/SP utilization; cost forecasting; credit/discount analysis; and multi-account cost comparison. Uses anti-hallucination rules, mandatory currency/credit detection workflow, and reusable Cost Explorer functions.

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。

Choose typefaces based on voice and context, not trends. Use when selecting fonts for a project, evaluating type choices, or understanding what a typeface communicates before anyone reads a word.

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。

Tell the difference between design decisions that age well and decisions that follow the crowd. Use when evaluating whether a design choice is principled or fashionable, or when a client asks to 'make it look modern.'

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When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.

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When the user wants to monitor competitor apps on an ongoing basis — tracking metadata changes, keyword shifts, screenshot updates, rating trends, or new features. Use when the user mentions "competitor monitoring", "track competitors", "competitor alert", "competitor changed their title", "watch a competitor app", "competitor weekly report", "competitive intelligence", or "what changed in competitor's listing". For a one-time deep competitive analysis, see competitor-analysis. For market-wide chart movements, see market-movers.

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。

Deep web research skill using Firecrawl MCP. Use when estimating market size (TAM/SAM/SOM), analyzing trends, or scraping competitors. For competitor teardowns, see competitor-analysis.

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。

Product-research (选品) desk (Busabase App-in-Skill) for a cross-border e-commerce seller. The agent sweeps trend sources — Amazon BSR movers, TikTok viral product videos, Temu/AliExpress rising items, Google Trends terms, competitor new launches — and files product candidates with margin cards (landed cost, fees, breakeven ACOS) and competition reads; Kelly verdicts them develop / watch / drop, and develop items become sourcing and listing briefs handed to kelly-listing. Use when the user invokes $kelly-picks or /kelly-picks, or asks for 选品, product research, a product sourcing radar, BSR movers, TikTok viral products, a margin calculator, breakeven ACOS, competition reads, or product candidate triage.

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。

Kelly Retail Intel: App-in-Skill daily industry intelligence cockpit for retail stores and consumer brands (Busabase App-in-Skill). Use when the user asks about retail, stores, promotions, merchandising, shopping trends, weather/event sales opportunities,零售, or门店 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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。

Kelly Ecommerce Intel: App-in-Skill daily industry intelligence cockpit for e-commerce and cross-border sellers (Busabase App-in-Skill). Use when the user asks about e-commerce, cross-border selling, product trends, competitor prices, listing optimization, ads,电商, or跨境 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

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。