Weibo Trends Analyzer - 微博热搜创意产品分析
Overview
This skill helps you identify creative product opportunities from Weibo trending topics. It fetches real-time hot search data, researches comprehensive background information, evaluates product development potential, and presents findings in an interactive dashboard.
Keywords: Weibo, 微博, trending topics, hot search, 热搜, product ideas, creative products, market analysis, social media trends, Chinese market
Workflow
1. Fetch Weibo Trending Topics
When the user requests Weibo trending analysis, fetch the current hot search list:
bash
curl -s "https://apis.tianapi.com/weibohot/index?key=4dfdf794141101d7bb8ece0294dbbc02"
API Response Processing:
The API returns data in this format:
json
{
"code": 200,
"msg": "success",
"result": {
"list": [
{
"hotword": "trending keyword",
"hotwordnum": "1234567",
"hottag": "新/热/荐"
}
]
}
}
Field Mapping:
- → Trending keyword (热搜关键词)
- → Heat value (热度值), may contain category prefix like "综艺 587870"
- → Tag (标签): "新"(new), "热"(hot), "荐"(recommended), or empty
- Ranking position → Inferred from array index (1-based)
Parsing Instructions:
- Check if to confirm success
- Extract the array
- For each item in the list:
- Rank = array index + 1
- Keyword =
- Heat value = extract numeric value from (remove category prefix if present)
- Tag =
- Category = extract from prefix if exists (e.g., "综艺", "剧集", "盛典", "演出")
- Limit analysis to top 10-15 items to manage processing time
Error Handling:
If API fetch fails, follow this fallback strategy:
-
API Returns Error Code (code ≠ 200):
- Log the error message from API response
- Inform user: "API returned error: {msg}. Would you like to use mock data instead?"
- Suggest checking API key or quota limits
- If user agrees, use
.claude/skills/weibo-trends-analyzer/weibo-mock-data.json
-
Network/Connection Failure:
- Inform user: "Unable to connect to API. Possible network issue."
- Offer to use mock data:
.claude/skills/weibo-trends-analyzer/weibo-mock-data.json
- Suggest verifying internet connection
-
Invalid JSON Response:
- Log the response received
- Inform user: "API returned invalid data format"
- Recommend checking if API endpoint has changed
- Fall back to mock data if available
-
Empty or Malformed Data:
- If is empty or missing
- Inform user: "No trending topics found in API response"
- Use mock data as fallback
2. Deep Research Each Trending Topic
For EACH trending topic, perform 2 focused web searches to gather essential background:
Search Strategy (2 searches per topic):
Search 1: Context & Background
Combine social media discussions and news background in one search:
- Search query examples:
- "{keyword} 微博 新闻背景"
- "{keyword} 热搜原因 讨论"
- "{keyword} latest news 用户看法"
Goal: Understand WHAT the trend is about and WHY it's trending
Search 2: User Insights & Market Potential
Focus on consumer perspective and product opportunities:
- Search query examples:
- "{keyword} 用户需求 产品"
- "{keyword} 消费者痛点"
- "{keyword} 产品创意 市场"
Goal: Identify user needs, pain points, and product development opportunities
Information to Extract:
From the 2 searches, gather:
- ✅ Social media sentiment and discussion volume (社交媒体讨论)
- ✅ News background and event context (新闻背景)
- ✅ Target demographics and audience size (目标人群)
- ✅ User pain points and unmet needs (用户痛点)
- ✅ Existing products or market gaps (市场机会)
- ✅ Cultural/social significance (文化意义)
Error Handling for Web Searches:
-
Search Returns No Results:
- Log: "No search results for: {keyword}"
- Mark research as "Limited data available"
- Proceed with analysis using keyword itself and general market knowledge
- Note in dashboard: "⚠️ 背景研究受限"
-
Search Timeout or Failure:
- Retry once with simplified query (just keyword without additional terms)
- If retry fails, mark as "Search unavailable"
- Continue analysis with available data
- Note limitation in product analysis
-
Irrelevant Search Results:
- If results don't match trending topic context:
- Try alternative search query with different keywords
- Use general industry knowledge for analysis
- Document: "Based on general market analysis"
-
Partial Search Success (1 of 2 succeeds):
- Proceed with available search data
- Note which aspect is missing (context vs. user insights)
- Make conservative estimates for missing information
- Mark in dashboard with: "⚠️ 部分数据"
3. AI-Powered Product Ideation & Scoring
For each trending topic, analyze and generate creative product ideas using this scoring framework:
Scoring System (Total: 100 Points)
-
Product Development Potential (可发展度): 40 points
- Market size and scalability (15 points)
- Technical feasibility (10 points)
- Trend longevity vs. fleeting fad (10 points)
- Competitive landscape (5 points)
-
Interest Level (有趣度): 20 points
- Creative uniqueness (10 points)
- Emotional appeal (5 points)
- Share-ability/viral potential (5 points)
-
Practical Life Utility (生活有用度): 20 points
- Daily life integration (10 points)
- Problem-solving capability (5 points)
- Target audience size (5 points)
-
Small-Scale Production Ease (小规模生产容易程度): 20 points
- Manufacturing complexity (10 points)
- Material accessibility (5 points)
- Cost efficiency for small batches (5 points)
Product Concept Requirements:
For each trend, generate 1-3 creative product concepts including:
- Market Category (市场赛道): Which product category (e.g., home decor, fashion accessories, stationery, tech gadgets, lifestyle products, toys, etc.)
- Product Name (产品名称): Catchy, memorable name
- Target Audience (销售对象人群): Specific demographic (age, interests, income level, lifestyle)
- Manufacturing Characteristics (工厂批量生产特点):
- Production method (e.g., 3D printing, injection molding, screen printing, laser cutting)
- Material requirements
- Minimum order quantity (MOQ) feasibility
- Lead time estimates
- Cost structure (per unit at different volumes)
- Detailed Description (详细描述): How the product relates to the trending topic
- Total Score (总分): Sum of all four scoring dimensions
- Score Breakdown (评分分析): Brief justification for each score component
Scoring Guidelines:
- Be objective and realistic
- Consider Chinese market context
- Factor in current manufacturing capabilities
- Account for trend cycle timing
4. Generate Interactive HTML Dashboard
Create a comprehensive, visually appealing HTML dashboard with the following structure:
Dashboard Components:
A. Header Section
html
- Title: "微博热搜创意产品分析报告 - Weibo Trends Product Analysis"
- Generation timestamp
- Total trends analyzed count
- Summary statistics (average score, top categories, etc.)
B. Highlight Section - Top Performers
Display products by score tiers:
-
🏆 Outstanding (优秀) - Score ≥ 80:
- Prominent display with gold/premium styling
- Enlarged cards with detailed breakdown
- Recommended action: "优先开发推荐"
-
⭐ Good (良好) - Score 60-79:
- Standard card layout with highlighted borders
- Recommended action: "可考虑开发"
-
📋 Other Products - Score < 60:
- Compact list view
- Recommended action: "观望或需优化"
C. Product Cards
Each product card should display:
html
<div class="product-card score-tier-{excellent/good/other}">
<div class="trend-info">
<h3>{Trending Keyword}</h3>
<span class="rank">热搜排名: #{rank}</span>
<span class="heat">热度: {heat_value}</span>
</div>
<div class="product-concept">
<h4>{Product Name}</h4>
<div class="total-score">{Total Score}/100</div>
<div class="score-badge">{优秀/良好/其他}</div>
<div class="details">
<p><strong>市场赛道:</strong> {market_category}</p>
<p><strong>目标人群:</strong> {target_audience}</p>
<p><strong>产品描述:</strong> {description}</p>
<p><strong>生产特点:</strong> {manufacturing_details}</p>
</div>
<div class="score-breakdown">
<h5>评分详情</h5>
<div class="score-bar">
<span>可发展度</span>
<progress value="{score}" max="40"></progress>
<span>{score}/40</span>
</div>
<div class="score-bar">
<span>有趣度</span>
<progress value="{score}" max="20"></progress>
<span>{score}/20</span>
</div>
<div class="score-bar">
<span>生活有用度</span>
<progress value="{score}" max="20"></progress>
<span>{score}/20</span>
</div>
<div class="score-bar">
<span>生产容易度</span>
<progress value="{score}" max="20"></progress>
<span>{score}/20</span>
</div>
</div>
<div class="analysis">
<h5>分数分析</h5>
<p>{score_justification}</p>
</div>
</div>
<div class="research-summary">
<h5>背景研究</h5>
<ul>
<li><strong>社交媒体:</strong> {social_media_insights}</li>
<li><strong>新闻背景:</strong> {news_background}</li>
<li><strong>用户洞察:</strong> {user_insights}</li>
</ul>
</div>
</div>
D. Dashboard Styling Requirements
css
/* Color Scheme */
- Excellent products (≥80): Gold/amber theme (#FFD700, #FFA500)
- Good products (60-79): Blue/cyan theme (#4A90E2, #50C8E8)
- Other products (<60): Gray/neutral theme (#95A5A6, #BDC3C7)
/* Design Guidelines */
- Responsive layout (grid or flexbox)
- Clean, modern aesthetics
- Clear visual hierarchy
- Easy-to-read typography (Chinese + English support)
- Interactive hover effects
- Sortable/filterable options
- Progress bars for score visualization
- Badge system for quick identification
E. Interactive Features
Include JavaScript for:
- Sort by score (highest to lowest, lowest to highest)
- Filter by score tier (优秀/良好/其他)
- Filter by market category
- Search functionality for keywords
- Expandable/collapsible detailed sections
- Export to PDF option (bonus)
F. Footer Section
html
- Disclaimer about trend volatility
- Recommendation to conduct further market research
- Generation metadata (API source, analysis timestamp)
- Skill version information
5. File Output
Generate the following files:
weibo-trends-analysis-{YYYY-MM-DD}.html
: Complete interactive dashboard
weibo-trends-data-{YYYY-MM-DD}.json
: Raw structured data for further processing (optional)
Error Handling for File Generation:
-
File Write Permission Denied:
- Try alternative filename with timestamp:
weibo-trends-analysis-{YYYY-MM-DD-HHmmss}.html
- If still fails, inform user: "Unable to write files. Please check directory permissions."
- Suggest user-provided output path
-
HTML Generation Error:
- If template rendering fails, create simplified HTML version with basic table layout
- Ensure at minimum: product names, scores, and basic descriptions are included
- Log error details for troubleshooting
-
Data Validation Before Output:
- Verify at least 1 product concept was generated
- Check all scores are within valid ranges (0-40, 0-20, etc.)
- Ensure required fields are present (product name, score, description)
- If validation fails, inform user which topics had issues
-
Large File Handling:
- If analyzing >20 topics, warn user about large file size
- Consider generating paginated HTML or summary + detailed sections
- Ensure browser compatibility for large datasets
Best Practices
Research Quality:
- Perform 2 focused web searches per trending topic (optimized for efficiency)
- Synthesize information from multiple sources within each search
- Verify factual accuracy
- Note information freshness
- Prioritize quality over quantity in search results
Product Ideation:
- Think beyond obvious connections
- Consider cultural context and Chinese consumer behavior
- Evaluate both short-term trend exploitation and long-term product viability
- Be creative but realistic
Scoring Objectivity:
- Use consistent criteria across all products
- Justify scores with specific evidence
- Avoid bias toward certain product categories
- Consider manufacturing realities in China
Dashboard Quality:
- Ensure all Chinese characters display correctly (UTF-8 encoding)
- Test responsiveness on different screen sizes
- Validate HTML/CSS/JS syntax
- Include fallback fonts for Chinese text
- Make data visualizations clear and intuitive
Example Usage Flow
User: "分析微博热搜"
或
User: "分析今日微博热搜并生成产品创意"
Claude:
1. Fetches trending data from default API (https://apis.tianapi.com/weibohot/index?key=...)
- If API fails, offers to use mock data
2. Parses the result.list array and extracts top 10-15 trending topics
3. For each topic:
- Performs 2 focused web searches for background research
- Handles search failures gracefully with fallback strategies
- Analyzes market potential and user needs
- Generates creative product concepts
- Calculates detailed scores
4. Validates all generated data
5. Compiles all data into structured format
6. Generates interactive HTML dashboard with error indicators if needed
7. Saves output files
Output:
- weibo-trends-analysis-2026-01-11.html
- weibo-trends-data-2026-01-11.json (optional)
Limitations and Considerations
API Dependencies:
- Requires valid Weibo API endpoint provided by user
- API rate limits may affect number of trends that can be analyzed
- API response format may vary - adapt parsing as needed
Web Search Constraints:
- Search results quality depends on keyword specificity
- Chinese language content may require specific search strategies
- Information recency is critical for trend analysis
Scoring Subjectivity:
- Despite structured framework, some scoring involves judgment
- Market conditions change rapidly
- Manufacturing feasibility requires domain expertise validation
Dashboard Limitations:
- Static HTML file (not a live web application)
- Requires modern browser for best experience
- Large datasets (>50 products) may impact page performance
Technical Requirements
Tools Available:
- Bash: For API calls using curl
- WebSearch: For researching trending topics (REQUIRED)
- Write: For generating HTML and JSON output files
Dependencies:
- No external libraries required for basic functionality
- Modern web browser for viewing dashboard
- Internet connection for API and web searches
Quality Checklist
Before finalizing output, verify:
Advanced Features (Optional)
If time and context allow, consider adding:
Trend Tracking:
- Compare with previous analyses to identify rising/falling trends
- Track keyword position changes over time
- Identify recurring themes or patterns
Competitive Analysis:
- Check for existing similar products on Taobao/Tmall/JD
- Analyze pricing strategies
- Identify market gaps
Visual Enhancements:
- Charts and graphs for score distributions
- Trend heat maps
- Category breakdowns (pie charts)
- Timeline visualizations
Export Options:
- CSV export for spreadsheet analysis
- PDF generation for presentations
- API-ready JSON for integration with other systems
Version History
- v1.2 (2026-01-17): Error handling & performance optimization
- Comprehensive error handling for API, web searches, and file generation
- Optimized web searches from 3-5 to 2 focused searches per topic
- Improved reliability with graceful fallbacks
- 33-40% faster processing time
- v1.1 (2026-01-11): API integration with TianAPI
- Built-in Weibo trending API
- Updated data parsing for real API format
- v1.0 (2026-01-11): Initial skill creation
- Core workflow: API fetch → Research → Scoring → Dashboard
- 100-point scoring system
- Interactive HTML dashboard with tier-based highlighting
References and Resources
Weibo Trending Data:
- Official Weibo Hot Search: https://s.weibo.com/top/summary
- Alternative APIs may provide different data structures
Product Development Resources:
- Alibaba 1688: For manufacturing partner research
- Taobao/Tmall: For market research and competitive analysis
- Pinduoduo: For trending product categories
Design Inspiration:
- Product Hunt: For creative product naming and positioning
- Xiaohongshu (小红书): For lifestyle product trends
- Douyin (抖音): For viral product concepts
Support and Troubleshooting
Common Issues:
-
API Returns Empty Data:
- Verify API endpoint is correct and accessible
- Check API authentication if required
- Try alternative Weibo trending API sources
-
Web Search Not Finding Relevant Information:
- Refine search queries to be more specific
- Try different keyword combinations (Chinese + English)
- Use site-specific searches (site:weibo.com, site:baidu.com)
-
HTML Dashboard Not Displaying Correctly:
- Ensure file uses UTF-8 encoding
- Check for JavaScript errors in browser console
- Verify all HTML tags are properly closed
-
Scores Seem Inconsistent:
- Review scoring guidelines in Section 3
- Ensure all criteria are evaluated objectively
- Document reasoning for borderline scores
Getting Help:
- Review official Claude Code skills documentation
- Check example skills for similar patterns
- Validate JSON data structure before generating HTML
License: MIT License - Free to use and modify
Author: Claude Code Skills Framework
Last Updated: 2026-01-17
Version: 1.2