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 → Determine the analysis domain, target platforms, and time range
2. Data Collection → Perform layered search by platform, aggregate trend signals
3. Intent Analysis → Identify user pain points, interest shifts, and information gaps
4. Gap Mining → Compare existing content coverage to discover untapped opportunities
5. Outline Generation → Output structured article outlines + topic scores

Step 1: Requirement Understanding#

Confirm with the user (if not explicitly provided):

• Domain/Industry: Technology, Finance, Health, Education, etc.
• Target Audience: B2B/B2C, technical level, region
• Target Platforms: Platforms where content will be published (affects style and depth)
• Time Range: Real-time trending / Last 7 days / Last 30 days / Quarterly
• Analysis Depth: Quick scan / Standard report / Competitive benchmarking

Step 2: Data Collection#

Collect data in layers by priority, using the corresponding tool for each layer:

Layer 1: Trend Baseline (Mandatory)#

PlatformToolContent CollectedGoogle Trendsweb_fetch trends.google.comSearch heat trends, related queries, geographic distributionRedditweb_search site:reddit.comPopular discussions, highly upvoted answers, community pain pointsYouTubeweb_search site:youtube.comVideo popularity, comment sentiment, title keywords

Layer 2: In-Depth Content (On Demand)#

PlatformToolContent CollectedMedium/Substackweb_search site:medium.com OR site:substack.comLong-form topic selection, subscriber interaction, writing stylesTwitter/Xweb_search site:x.comReal-time discussions, hashtags, KOL perspectivesZhihu/Weiboweb_search site:zhihu.com OR site:weibo.comChinese community Q&A, trending topicsBaidu Indexweb_fetch index.baidu.comChinese search trends, audience profilesProduct Huntweb_search site:producthunt.comNew product trends, technology directions

Layer 3: Competitive Benchmarking (For In-Depth Reports)#

PlatformToolContent CollectedCompetitor Blogs/Official Accountsweb_fetch + web_searchExisting content coverage, publishing frequency, engagement dataGitHub Trendingweb_search site:github.com/trendingDeveloper technology trends

Collection Strategy:

• Execute 2-3 targeted searches per platform (from different angles)
• Search query combinations: "{domain} + {time-related term}", "{domain} + pain point term", "{domain} + how/why/what"
• Record for each finding: source, popularity metric, core topic, user sentiment

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:

   • 🎯 Learning (how-to, tutorials, guides)
   • 🤔 Exploratory (comparisons, reviews, analysis)
   • 😤 Pain Points (errors, problems, complaints)
   • 🚀 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:

text

Existing Content Coverage Matrix:
  Topic A: ████░░░░ 50% (Lacks advanced content)
  Topic B: ██░░░░░░ 25% (Significant gaps)
  Topic C: ████████ 90% (Saturated; difficult to differentiate)
  Topic D: ░░░░░░░░  0% (Blue ocean opportunity)

Scoring Dimensions:

• Demand Intensity (search volume + discussion heat) → Scale of 1-5
• Content Gap (insufficient existing coverage) → Scale of 1-5
• Differentiation Potential (likelihood of a unique angle) → Scale of 1-5
• Timeliness (current heat window) → Scale of 1-5
• Composite Recommendation Score = Weighted average

Step 5: Outline Generation#

See references/outline-templates.md for output format.

Generate for each high-scoring topic:

Article Outline Structure#

text

## [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:

text

| Rank | Topic | Rec. Score | Demand Intensity | Content Gap | Differentiation | Timeliness |
|------|-------|-----------|-----------------|-------------|----------------|------------|
| 1    | ...   | 4.5       | 5               | 4           | 4              | 5          |

Output Format Selection#

• Quick Scan: Concise table + Top 3 outlines
• Standard Report: Full analysis + Top 5 outlines + Gap matrix
• In-Depth Report: Full dataset + Competitive benchmarking + Top 10 outlines + Monthly recommendations

Notes#

• Annotate all data points with source URLs to ensure traceability
• Distinguish between "noise topics" (short-term hype) and "trend topics" (sustained growth)
• For Chinese content, prioritize data from Zhihu, Weibo, and Baidu Index
• For English content, prioritize Google Trends, Reddit, and Hacker News
• For tech topics, additionally check GitHub Trending and Stack Overflow