Content Trend Analyzer
by @openlark
Aggregates and analyzes content trends across platforms to identify hot topics, user intent, content gaps, and generates data-driven article outlines.
clawhub install content-trend-analyzerπ About This Skill
name: content-trend-analyzer description: Cross-platform content trend analysis and outline generation tool. Platforms covered include but are not limited to: Google Trends, Reddit, YouTube, Medium, Substack, Twitter/X, Zhihu, Weibo, Douyin, Bilibili, Baidu Index, WeChat Official Accounts, GitHub Trending, Product Hunt.
Content Trend Analyzer
Multi-platform content trend aggregation and analysis, producing data-driven article outlines and content strategies. Triggers when users need: content trend analysis, topic heat tracking, trending topic discovery, user intent analysis, content gap mining, competitive content research, SEO keyword trends, data-driven article outline generation, content strategy formulation.
Trigger Keywords
Trend analysis, content trends, trending topics, trend analysis, content gap, topic analysis, topic selection, content strategy, outline generation, content outline.
Workflow
1. Requirement Understanding β Determine the analysis domain, target platforms, and time range 2. Data Collection β Perform layered search by platform, aggregate trend signals 3. Intent Analysis β Identify user pain points, interest shifts, and information gaps 4. Gap Mining β Compare existing content coverage to discover untapped opportunities 5. Outline Generation β Output structured article outlines + topic scores
Step 1: Requirement Understanding
Confirm with the user (if not explicitly provided):
Step 2: Data Collection
Collect data in layers by priority, using the corresponding tool for each layer:
Layer 1: Trend Baseline (Mandatory)
| Platform | Tool | Content Collected |
|----------|------|-------------------|
| Google Trends | web_fetch trends.google.com | Search heat trends, related queries, geographic distribution |
| Reddit | web_search site:reddit.com | Popular discussions, highly upvoted answers, community pain points |
| YouTube | web_search site:youtube.com | Video popularity, comment sentiment, title keywords |
Layer 2: In-Depth Content (On Demand)
| Platform | Tool | Content Collected |
|----------|------|-------------------|
| Medium/Substack | web_search site:medium.com OR site:substack.com | Long-form topic selection, subscriber interaction, writing styles |
| Twitter/X | web_search site:x.com | Real-time discussions, hashtags, KOL perspectives |
| Zhihu/Weibo | web_search site:zhihu.com OR site:weibo.com | Chinese community Q&A, trending topics |
| Baidu Index | web_fetch index.baidu.com | Chinese search trends, audience profiles |
| Product Hunt | web_search site:producthunt.com | New product trends, technology directions |
Layer 3: Competitive Benchmarking (For In-Depth Reports)
| Platform | Tool | Content Collected |
|----------|------|-------------------|
| Competitor Blogs/Official Accounts | web_fetch + web_search | Existing content coverage, publishing frequency, engagement data |
| GitHub Trending | web_search site:github.com/trending | Developer technology trends |
Collection Strategy:
"{domain} + {time-related term}", "{domain} + pain point term", "{domain} + how/why/what"Step 3: Intent Analysis
Perform the following analysis on the collected data:
1. Topic Clustering: Group similar topics; identify 3-5 core themes 2. Intent Classification: - π― Learning (how-to, tutorials, guides) - π€ Exploratory (comparisons, reviews, analysis) - π€ Pain Points (errors, problems, complaints) - π Forward-Looking (trend forecasts, new tools, best practices) 3. Sentiment Tendency: Positive/Negative/Neutral; identify controversial topics 4. User Personas: Infer technical level and role identity from discussion language
Step 4: Gap Mining
Compare existing content with user needs:
Existing Content Coverage Matrix:
Topic A: ββββββββ 50% (Lacks advanced content)
Topic B: ββββββββ 25% (Significant gaps)
Topic C: ββββββββ 90% (Saturated; difficult to differentiate)
Topic D: ββββββββ 0% (Blue ocean opportunity)
Scoring Dimensions:
Step 5: Outline Generation
See references/outline-templates.md for output format.
Generate for each high-scoring topic:
Article Outline Structure
## [Topic Title]
Recommendation Score: X.X/5.0
Target Platform: [Platform]
Estimated Word Count: [Word Count]
Difficulty: [Beginner/Intermediate/Expert] Core Value Proposition
[One sentence explaining what the reader will gain]Outline
1. [Introduction hook - based on real user pain points]
- Data Support: [Cite trend data]
2. [Core Argument 1]
- Sub-points + Examples/Data
3. [Core Argument 2]
- Sub-points + Examples/Data
4. [Core Argument 3]
- Sub-points + Examples/Data
5. [Conclusion + Call to Action]SEO Recommendations
Primary Keyword: [Keyword]
Long-Tail Keywords: [KW1], [KW2], [KW3]
Title Alternatives: [Alt Title 1], [Alt Title 2]
Topic Ranking Report
Generate a comparison table of all candidate topics:
| Rank | Topic | Rec. Score | Demand Intensity | Content Gap | Differentiation | Timeliness |
|------|-------|-----------|-----------------|-------------|----------------|------------|
| 1 | ... | 4.5 | 5 | 4 | 4 | 5 |