Global Trend Radar
/install global-trend-radar
Global Trend Radar (鍏ㄧ悆瓒嬪娍闆疯揪)
Description
A comprehensive trend intelligence skill that monitors, analyzes, and synthesizes emerging global trends across technology, economics, geopolitics, climate, demographics, and innovation. Powered by multi-source web intelligence and structured trend taxonomy, it provides forward-looking analysis with actionable insights for decision-makers, researchers, and strategists worldwide.
Keywords: trends, global, forecasting, technology, innovation, market intelligence, future, megatrends, horizon scanning
Triggers
- "what are the latest trends in [domain]"
- "analyze global [industry/sector] trends"
- "is [technology] trending worldwide"
- "compare trends between [region A] and [region B]"
- "what megatrends will shape the next [N] years"
- "emerging technologies to watch in [year]"
- "global market forecast for [sector]"
Capabilities
1. Trend Discovery & Monitoring
- Execute multi-source web searches across 10+ authoritative trend data sources (Google Trends, Gartner, WIPO, WEF, OECD, McKinsey, Pew Research, arXiv, CB Insights, Statista)
- Track 8 persistent trend categories: AI & Automation, Climate & Sustainability, Digital Transformation, Biotech & Health, Geopolitics & Economics, Future of Work, Space & Deep Tech, Global Demographics
- Perform cross-regional trend comparison (US, EU, China, India, SE Asia, MENA, LATAM)
- Detect early signals via patent data analysis (WIPO PatentScope) and academic preprint surges (arXiv)
2. Trend Analysis Framework
- Hype Cycle Assessment: Map technologies to Gartner-style maturity phases (Innovation Trigger 鈫?Peak of Inflated Expectations 鈫?Trough of Disillusionment 鈫?Slope of Enlightenment 鈫?Plateau of Productivity)
- Adoption S-Curve: Estimate current adoption phase and trajectory for any trend
- Impact 脳 Probability Matrix: Score trends on potential impact (economic, social, environmental) vs. realization probability
- Cross-Domain Ripple Analysis: Identify second-order effects where trends in one domain cascade into others
3. Output Formats
Always structure trend reports with:
- Executive Summary (3-5 bullet points)
- Trend Landscape (categorized table with urgency/impact scores)
- Deep Dives (2-3 most significant trends with evidence, data points, and timeline)
- Regional Comparison (heatmap table: US vs EU vs APAC)
- Actionable Implications (for businesses, policymakers, individuals)
- Monitoring Dashboard (key metrics and indicators to watch going forward)
4. Data-Driven Requirements
- Cite at least 3 distinct sources per trend (URLs + publication dates required)
- Include quantitative data points where available (market size, growth rate, adoption %)
- Note conflicting viewpoints and uncertainties explicitly
- Distinguish between consensus trends and speculative projections
- Flag data freshness: mark sources older than 6 months as potentially stale
Workflow
User Query
鈫?[Step 1] Identify trend category & regions from structured taxonomy
鈫?[Step 2] Execute parallel web_search across 3-5 relevant data sources
鈫?[Step 3] Execute web_fetch on top 2-3 most promising results for depth
鈫?[Step 4] Cross-reference findings: compare data points, identify consensus
鈫?[Step 5] Apply analysis frameworks (Hype Cycle, S-Curve, Impact Matrix)
鈫?[Step 6] Generate structured report with citations, tables, and visual indicators
鈫?Final Output: Formatted report + monitoring recommendations
Usage Guidelines
- Default scope: When user doesn't specify timeframe, default to 1-3 year outlook
- Default regions: When user doesn't specify region, cover global + regional highlights
- Confidence levels: Always self-assess confidence (HIGH: multiple corroborating sources, MEDIUM: 2 sources aligned, LOW: single source or speculative)
- Language: Match response language to user's query language; support multi-language trend searches using keyword translations from references/data_sources.json
- Persistence: This skill tracks long-cycle trends (5-20 year horizons); avoid chasing viral fads
Examples
Query: "What are the top AI trends shaping the next 3 years globally?"
Response Structure:
- Executive Summary with top 5 AI trends ranked by impact
- Trend Landscape table with columns: Trend | Maturity Phase | Impact Score | Adoption Velocity | Key Players
- Deep Dive on #1 trend: Multi-modal AI / AI Agents - market data, patent surge, key papers
- Regional comparison: US (foundation models, venture funding) vs EU (regulation-first, industrial AI) vs China (application layer, manufacturing AI)
- Actionable implications for software companies, enterprises, and regulators
- Metrics to watch: GPU shipments, AI patent filings, AI regulation milestones
References
references/data_sources.json: Complete data source catalog with URLs, coverage regions, and keyword mappings for 10 authoritative sources across 8 trend categories 锛堝唴瀹圭敱AI鐢熸垚锛屼粎渚涘弬鑰冿級
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install global-trend-radar - 安装完成后,直接呼叫该 Skill 的名称或使用
/global-trend-radar触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Global Trend Radar 是什么?
Monitors and analyzes global emerging trends across key sectors using multi-source data to provide structured, actionable insights and forecasts. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 37 次。
如何安装 Global Trend Radar?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install global-trend-radar」即可一键安装,无需额外配置。
Global Trend Radar 是免费的吗?
是的,Global Trend Radar 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Global Trend Radar 支持哪些平台?
Global Trend Radar 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Global Trend Radar?
由 ai-gaoqian(@ai-gaoqian)开发并维护,当前版本 v1.0.0。