Skill: openlark/content-trend-analyzer (version 1.0.0)

--- 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** → 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) | 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:** - 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: ``` 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](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

path: references/outline-templates.md
content: # Article Outline Template Library

Outline templates organized by content type, for use in Step 5 outline generation.

## Table of Contents

1. [Tutorial / How-To Guide](#tutorial--how-to-guide)
2. [In-Depth Analysis](#in-depth-analysis)
3. [Comparison / Review](#comparison--review)
4. [Trend Forecasting](#trend-forecasting)
5. [Problem Solving](#problem-solving)
6. [Opinion / Insight](#opinion--insight)

---

## Tutorial / How-To Guide

Suitable for: how-to articles, beginner guides, best practices

```
# [Title: How to / Guide / Complete Handbook - Specific to quantifiable results]

## Hook (100-150 words)
- Describe the current obstacles readers face
- Cite trend data to illustrate the prevalence of the problem
- Preview the solutions the article will provide

## Background and Preparation (200-300 words)
- Why this topic matters now (trend data)
- Prerequisites / tool checklist
- Target audience description

## Core Steps (3-5 steps, 300-500 words per step)
### Step 1: [Step Name]
- Instructions + screenshots/code
- Common mistake alerts

### Step 2: [Step Name]
- ...

### Step 3: [Step Name]
- ...

## Advanced Tips (200-300 words)
- Tips that 80% of people don't know
- Performance optimization / advanced configuration

## FAQ (200-300 words)
- 3-5 high-frequency questions + answers

## Conclusion
- Recap of core takeaways
- Recommended next steps
- Related further reading
```

## In-Depth Analysis

Suitable for: industry analysis, technical deep dives, phenomenon interpretation

```
# [Title: In-Depth Analysis / Comprehensive Interpretation / Underlying Logic - of a certain phenomenon/technology]

## Hook (150 words)
- Impactful description of the phenomenon/event
- "But what's truly interesting is..." twist introduction

## I. Phenomenon Overview (300 words)
- Data overview (growth curves, user numbers, funding amounts, etc.)
- Timeline summary
- Key milestones

## II. Underlying Driving Factors (500-800 words)
### 2.1 [Factor 1: Technology / Market / Policy]
- Data support + case studies

### 2.2 [Factor 2: User Behavior Changes]
- Trend data + user research

### 2.3 [Factor 3: Industry Chain Changes]
- Upstream / downstream impact analysis

## III. Key Player Landscape (300 words)
- Leading players + market share
- Emerging challengers
- Competitive landscape map

## IV. Risks and Challenges (300 words)
- Primary risk points
- Possible turning point signals

## V. Future Outlook (200 words)
- Short-term (3-6 months) forecast
- Long-term (1-3 years) outlook
- Metrics to watch

## Conclusion
- Reiterate core viewpoint
- Recommended reader actions
```

## Comparison / Review

Suitable for: product comparisons, solution selection, tool reviews

```
# [Title: A vs B vs C - The Ultimate 202X Comparison / Selection Guide]

## Hook (100 words)
- The importance of selection decisions
- "Pitfalls many teams have fallen into..."

## Review Criteria (100 words)
- List of evaluation criteria
- Scoring weights

## Contender Introductions (150 words each)
### [Tool A]
- Positioning, core strengths, applicable scenarios

### [Tool B]
- ...

## Head-to-Head Comparison (Core 500-800 words)
| Dimension | A | B | C |
|-----------|---|---|---|
| Performance | ... | ... | ... |
| Ease of Use | ... | ... | ... |
| Ecosystem | ... | ... | ... |
| Pricing | ... | ... | ... |

## Scenario Recommendations (300 words)
- If you need X → Choose A
- If you need Y → Choose B
- If you need Z → Choose C

## Migration Cost Analysis (200 words)
- Cost of migrating from A to B

## Conclusion
- Winner + rationale
- Quick decision tree
```

## Trend Forecasting

Suitable for: annual trends, industry predictions, technology outlooks

```
# [Title: N Major Trends in X Domain for 202X / The Future Is Here]

## Hook (150 words)
- Review of the past year
- "The next inflection point is about to arrive"

## Trend 1: [Trend Name] (400-600 words)
- Current data
- Why it's happening now
- Scope of impact
- Actionable advice

## Trend 2: [Trend Name]
- ...

## Trend 3-N: [Trend Name]
- ...

## Cross-Trend Intersections (200 words)
- Superimposed effects between trends
- Where the biggest opportunities lie

## Risk Warnings (150 words)
- Predictions that may fail
- Underestimated black swans

## Conclusion
- One-sentence summary
- Recommended information sources to follow
```

## Problem Solving

Suitable for: bug fixes, troubleshooting, performance optimization

```
# [Title: The Ultimate Solution to XXX Problem / XXX Error]

## Problem Symptoms (100 words)
- Error message / screenshot
- Environment where it occurs

## Root Cause Analysis (200-300 words)
- Nature of the problem
- Why it happens (underlying principles)

## Solutions (from simplest to most complex)

### Solution 1: Quick Fix (100 words)
- One-line command / config change
- Applicable conditions

### Solution 2: Standard Solution (200 words)
- Complete steps
- Cautions and notes

### Solution 3: Permanent Fix (300 words)
- Architectural-level solution
- Pros and cons analysis

## Verification and Testing (100 words)
- How to confirm the problem is resolved
- Regression testing recommendations

## Preventive Measures (100 words)
- How to avoid encountering this again

## Conclusion
```

## Opinion / Insight

Suitable for: industry opinions, counter-consensus views, deep thinking

```
# [Title: The Counter-Intuitive Truth / Why Everyone Is Wrong / The Hidden Logic of X]

## Hook (150 words)
- Challenge the mainstream view
- "Most people think... but in reality..."

## Mainstream Narrative (200 words)
- The accepted viewpoint
- Supporting data

## Counterargument (500-800 words)
### Argument 1: Data Perspective
- Overlooked data

### Argument 2: Historical Patterns
- Review of similar cases

### Argument 3: First Principles
- Derived from foundational logic

## Preemptive Rebuttal (200 words)
- "You might say..."
- Respond point by point

## A New Thinking Framework (300 words)
- A better way to understand
- An actionable new framework

## Conclusion
- Core viewpoint
- "The most dangerous thing is not..."
```

path: references/platform-strategies.md
content: # Platform Search Strategy Reference

Search techniques and data collection key points for each platform, for reference during Step 2 data collection.

## Table of Contents

1. [Search Engine Tips](#search-engine-tips)
2. [Google Trends](#google-trends)
3. [Reddit](#reddit)
4. [YouTube](#youtube)
5. [Medium / Substack](#medium--substack)
6. [Twitter / X](#twitter--x)
7. [Zhihu](#zhihu)
8. [Weibo](#weibo)
9. [Baidu Index](#baidu-index)
10. [Product Hunt](#product-hunt)
11. [GitHub Trending](#github-trending)
12. [Stack Overflow](#stack-overflow)

---

## Search Engine Tips

### High-Value Search Patterns

```
# Discover problems / pain points
"{domain}" + "why" OR "how to fix" OR "error" OR "bug"
"{domain}" + "alternative" OR "vs" OR "which is better"

# Discover trends
"{domain}" + "2024" OR "2025" OR "trend" OR "future"
"{domain}" + "new" OR "release" OR "update"

# Discover content gaps
"{domain}" + "tutorial" OR "guide" OR "beginner"
"{domain}" + "advanced" OR "pro" OR "deep dive"
```

### Site-Specific Search

```
site:reddit.com "{domain}" discussion
site:youtube.com "{domain}" tutorial
site:medium.com "{domain}" guide
site:zhihu.com "{domain}" how to
```

---

## Google Trends

**URL**: https://trends.google.com/trends/explore

**Collection Key Points:**
- Search the "Interest over time" chart (select 12 months or 5 years)
- Check "Related queries" under "Rising" (Breakout > 5000%)
- Compare trend differences across multiple related terms
- View geographic distribution by region

**Analysis Dimensions:**
- Sustained rise vs. seasonal fluctuation vs. decline
- Semantic clustering of related queries
- Regional differences suggesting market opportunities

---

## Reddit

**Key Subreddits by Domain:**
- Tech: r/technology, r/programming, r/webdev
- AI/ML: r/MachineLearning, r/artificial, r/ChatGPT
- Startups: r/startups, r/entrepreneur
- Product: r/SaaS, r/productmanagement
- Finance: r/investing, r/personalfinance

**Collection Key Points:**
- Sort by "Top" + time range (week/month/year)
- Focus on authentic pain point descriptions in highly upvoted comments
- Directional signals from "What are you working on" type posts
- In-depth posts tagged with [Discussion]

**Analysis Dimensions:**
- Sentiment differences between comments section and post title
- Recurring question patterns
- Specific issues with concentrated negative sentiment

---

## YouTube

**Search Strategy:**
- Sort by upload date to find the latest content
- Sort by "View count" to find validated topics
- Check the comments section for follow-up questions and supplementary queries

**Collection Key Points:**
- Keyword patterns in video titles (what attracts clicks)
- Trending topic directions in the last 30 days
- High-frequency questions in comments = potential topics
- Sentiment trends in bullet comments / comments

---

## Medium / Substack

**Search Strategy:**
```
site:medium.com "{domain}" tag:{tag}
site:substack.com "{domain}"
```

**Collection Key Points:**
- Medium: Focus on article structure and title phrasing of highly-read articles
- Substack: Focus on topic selection of rapidly growing newsletters
- Analyze engagement ratio (clap/like vs. views)

---

## Twitter / X

**Search Strategy:**
```
site:x.com "{domain}" min_faves:100
"{domain}" thread
```

**Collection Key Points:**
- Topics and expressions in highly favorited posts
- Structural patterns of Threads (long-form tweets)
- Arguments from both sides on controversial topics
- Intersection points of KOL discussions

---

## Zhihu

**URL**: https://www.zhihu.com

**Collection Key Points:**
- Related topics on the hot list
- Structure and angles of highly upvoted answers
- Authentic needs behind "What is the experience of XXX" questions
- Topic direction of column articles
- Number of topic followers = potential reader scale

---

## Weibo

**URL**: https://weibo.com

**Collection Key Points:**
- Related topics on the trending search list
- Discussion volume and read volume of hashtags
- Engagement data on KOL Weibo posts
- High-frequency appeals in the comments section

---

## Baidu Index

**URL**: https://index.baidu.com

**Collection Key Points:**
- Search index trends (daily/weekly/monthly)
- Demand graph (clustering of related search terms)
- Audience profile (age, gender, region)
- Information index (news attention)
- Fastest-rising search terms

---

## Product Hunt

**URL**: https://www.producthunt.com

**Collection Key Points:**
- Daily/weekly/monthly trending products
- Keyword trends in product descriptions
- Need expressions in user reviews
- Browse by category: AI, Developer Tools, Productivity

---

## GitHub Trending

**URL**: https://github.com/trending

**Collection Key Points:**
- Daily/weekly/monthly trending repositories
- Positioning descriptions in README files
- Star growth rate
- Changes in programming languages and tech stacks used

---

## Stack Overflow

**Search Strategy:**
```
site:stackoverflow.com "{domain}" "how to"
site:stackoverflow.com "{technology}" tags
```

**Collection Key Points:**
- Highly voted questions = high-frequency pain points
- Unsolved / low-vote questions = content gaps
- Tag trends (emerging tags)
- Questions with high bounty amounts

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