The Decision
Trend Radar is a trending topic tracking skill: real-time scraping of X/Reddit/Google Trends to find content creation opportunities.
It directly replaces Exploding Topics ($39/mo), BuzzSumo ($199/mo), and SparkToro ($50/mo).
Our testing rates it 8.1/10 overall. Trend Radar is a research skill that works within AI coding and chat runtimes (Claude, Codex CLI, Cursor, Gemini CLI). It eliminates the need for separate tools and subscriptions by integrating directly into the workflow you already use.
Who It’s For
- Content creators who need to continuously find new topics
- Creators in AI, tech, and startup niches
- Creators who prefer data-driven topic selection over intuition
Who Should Skip
- Creators primarily posting on Chinese platforms
- News media needing real-time (minute-level) trend tracking
- Professional teams needing deep SEO keyword research (recommend Ahrefs)
Why This Skill Matters
In traditional workflows, research tasks require separate tools, manual steps, and context switching. Many creators pay for Exploding Topics ($39/mo) just to handle these tasks. Trend Radar eliminates that overhead by integrating directly into your AI workflow. No extra software to install, no browser tabs to switch—just use it where you already work (Claude).
Trend Radar demonstrates the value of Skills in the content research phase. Traditionally, you’d manually browse X, check Google Trends, and search Reddit — spending 30 minutes finding a decent topic. Trend Radar compresses this into one conversation and can discover cross-platform trends you might otherwise miss. Paired with Content Writer, going from trend discovery to finished content can take just 15 minutes.
Use Cases
Discover Rising Trending Topics
This is one of the core scenarios where Trend Radar shines. The skill can pull, organize, and summarize information from multiple sources, saving hours of manual research time. It structures findings in a format ready for content creation, with proper citations and source attribution.
Analyze Traffic Sources for Competitor Content
This is one of the core scenarios where Trend Radar shines. The skill can pull, organize, and summarize information from multiple sources, saving hours of manual research time. It structures findings in a format ready for content creation, with proper citations and source attribution.
Find Low-Competition, High-Search-Volume Topics
This is one of the core scenarios where Trend Radar shines. The skill can pull, organize, and summarize information from multiple sources, saving hours of manual research time. It structures findings in a format ready for content creation, with proper citations and source attribution.
Core Features
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Multi-Source Trend Aggregation
Scrape X trending topics, Reddit trend posts, and Google Trends rising keywords simultaneously to find cross-platform trending topics. Verdict: excellent. -
Content Opportunity Discovery
Analyze the ratio of search volume to competition and recommend topics with ‘rising search volume but little quality content.’ Verdict: great. -
Conversational Analysis
’What has been the trend for AI video tools over the last 30 days?’ — the Agent directly provides trend analysis with data backing. Verdict: great. -
Creation Integration
After discovering trends, directly call Content Writer to generate content, forming a complete ‘discover → create → publish’ pipeline. Verdict: excellent.
Hands-On
Installation takes one command:
npx skills add trend-radarWe tested Trend Radar primarily in Claude. After installation, the skill appears in your available tools immediately. We used it to gather and synthesize information from multiple sources. The skill organized findings into a clear structure with proper citations. The quality of synthesized output was high enough to use as a first draft for research-based content.
Real-World Case Study: Automating AI Topic Discovery
The Challenge: AI content creators face information overload. Manually scanning X, Reddit, and Xiaohongshu for trending topics consumes 3-4 hours daily, yet discovered topics are often already covered by others. Hit rate hovers around 15%.
The Prompt Chain (Exact Steps):
- Set up Airtap Agent on a cloud Android phone with a blank profile (no logins) to access raw algorithmic feeds.
- Use this prompt:
# AI 圈选题信号挖掘
任务：在 X 的 For You 流里找 AI 工具/工作流相关的高互动帖。
## 操作步骤
1. 打开 X App，进入 For You 流（匿名，不登录）
2. 一条条往下刷，不跳读
3. 每条推文读取：转发数和点赞数（推文底部）、是否包含 AI 相关关键词 (Claude / GPT / Cursor / Gemini / Skill / MCP / Agent / Prompt)
4. 命中标准：转发 ≥ 500 或 点赞 ≥ 2000，且关键词命中
5. 命中后立刻提取一行：[序号] | 作者 | 转发 | 点赞 | 一句话摘要(≤40字) | 链接
6. 继续刷，累计 20 条命中或刷满 500 条停止
7. 跑完输出 markdown 表格
## 约束
- 全程游客状态，不登录任何账号
- 不点赞、不转发、不关注、不评论
- 跳过纯营销号、Crypto 广告、纯 emoji 推
- 遇到登录弹窗，关掉继续刷
- 看到「敏感内容」提示直接跳过- Define thresholds: For X, engagement ≥500 retweets OR ≥2000 likes AND keywords. For Xiaohongshu, similar but adapt to platform signals.
- Schedule daily at 07:00; results delivered by 08:00 as markdown tables.
- Run 4 cloud phones in parallel (X, Reddit, Xiaohongshu, Bilibili) to cross-validate; same signal appearing on ≥3 platforms indicates high priority.
- Human review (20 min) selects 3-5 topics from ~20 candidates for full articles.
The Result:
- Manual feed scanning: 0 hours/day (down from 3-4h)
- Daily review: 20 minutes
- Weekly effort: ~2h vs 20h before (~1000 hours/year saved)
- Content hit rate: 15% → 60%+
- Example: Anthropic’s “HTML will replace Markdown” signal led to article “HTML替代Markdown？飞书CLI和飞书画板给出新解法”
Key Takeaways for Prompt Engineering:
- Blank persona matters: Avoid personalized feeds to see raw algorithmic trends.
- Early signals, not saturated: Choose thresholds at “just popular enough” (500 retweets in AI Twitter) to catch trends before they peak.
- Cross-platform de-duplication: Same signal across platforms validates priority and reduces noise.
- Separate filtering from creation: AI handles signal collection; human handles relevance, style, and depth.
- Iterate thresholds weekly: Adjust prompts and thresholds as platform algorithms shift.
Pricing
Free — Trend Radar is completely free to use. You only need an account on a supported runtime (Claude, Codex CLI). This makes it an exceptional value compared to the paid tools it replaces. There are no hidden costs, no premium tiers, and no usage limits.
Available plans:
- Open Source: Free
Verdict: 8.1/10
Trend Radar is a strong research skill that delivers real value. By replacing Exploding Topics ($39/mo), it saves both money and context-switching overhead. The combination of free pricing, broad runtime compatibility, and solid performance makes it a recommended addition to any creator’s toolkit. For research workflows, it is one of the best skill options available today.
Try It
Run npx skills add trend-radar in your supported runtime.
FAQ
Q: How does it compare to Exploding Topics/BuzzSumo?
A: The biggest gap is data depth and historical comparison. Exploding Topics has years of trend data and proprietary algorithms; BuzzSumo can analyze social shares for any article. Trend Radar is lighter but covers 80% of daily topic needs, and it’s completely free.
Q: Do I need to configure API Keys?
A: Google Trends needs no API Key. X (Twitter) requires a developer API (free tier has rate limits). Reddit needs no API Key. We recommend at least configuring Google Trends for a great experience.
Q: Does it support Chinese trends?
A: Google Trends supports Chinese keyword searches. X and Reddit trends are mainly in English. If you create Chinese content, we recommend using Google Trends Chinese search + manual supplementation from Baidu Index.