content-trend-analyzer（内容趋势分析器）

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)

Layer 2: In-Depth Content (On Demand)

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

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)

1. Sentiment Tendency: Positive/Negative/Neutral; identify controversial topics
2. 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: ████░░░░ 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

## [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          |

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