hot-topics▌
vikiboss/60s-skills · updated Apr 8, 2026
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Real-time trending topics and hot searches from six major Chinese social media platforms.
- ›Supports six platforms: Weibo, Zhihu, Baidu, Douyin, Toutiao, and Bilibili via simple GET endpoints
- ›Returns ranked trending topics with metadata including heat scores, URLs, and update timestamps
- ›Enables cross-platform trend comparison, keyword tracking, and daily trending summaries
- ›Ideal for monitoring viral content, understanding current discussions, and identifying common topics across pla
Hot Topics & Trending Content Skill
This skill helps AI agents fetch trending topics and hot searches from major Chinese social media and content platforms.
When to Use This Skill
Use this skill when users:
- Want to know what's trending on social media
- Ask about hot topics or viral content
- Need to understand current popular discussions
- Want to track trending topics across platforms
- Research social media trends
Supported Platforms
- Weibo (微博) - Chinese Twitter equivalent
- Zhihu (知乎) - Chinese Quora equivalent
- Baidu (百度) - China's largest search engine
- Douyin (抖音) - TikTok China
- Toutiao (今日头条) - ByteDance news aggregator
- Bilibili (B站) - Chinese YouTube equivalent
API Endpoints
| Platform | Endpoint | Description |
|---|---|---|
/v2/weibo |
Weibo hot search topics | |
| Zhihu | /v2/zhihu |
Zhihu trending questions |
| Baidu | /v2/baidu/hot |
Baidu hot searches |
| Douyin | /v2/douyin |
Douyin trending videos |
| Toutiao | /v2/toutiao |
Toutiao hot news |
| Bilibili | /v2/bili |
Bilibili trending videos |
All endpoints use GET method and base URL: https://60s.viki.moe/v2
How to Use
Get Weibo Hot Searches
import requests
def get_weibo_hot():
response = requests.get('https://60s.viki.moe/v2/weibo')
return response.json()
hot_topics = get_weibo_hot()
print("🔥 微博热搜:")
for i, topic in enumerate(hot_topics['data'][:10], 1):
print(f"{i}. {topic['title']} - 热度: {topic['热度']}")
Get Zhihu Hot Topics
def get_zhihu_hot():
response = requests.get('https://60s.viki.moe/v2/zhihu')
return response.json()
topics = get_zhihu_hot()
print("💡 知乎热榜:")
for topic in topics['data'][:10]:
print(f"· {topic['title']}")
Get Multiple Platform Trends
def get_all_hot_topics():
platforms = {
'weibo': 'https://60s.viki.moe/v2/weibo',
'zhihu': 'https://60s.viki.moe/v2/zhihu',
'baidu': 'https://60s.viki.moe/v2/baidu/hot',
'douyin': 'https://60s.viki.moe/v2/douyin',
'bili': 'https://60s.viki.moe/v2/bili'
}
results = {}
for name, url in platforms.items():
try:
response = requests.get(url)
results[name] = response.json()
except:
results[name] = None
return results
# Usage
all_topics = get_all_hot_topics()
Simple bash examples
# Weibo hot search
curl "https://60s.viki.moe/v2/weibo"
# Zhihu trending
curl "https://60s.viki.moe/v2/zhihu"
# Baidu hot search
curl "https://60s.viki.moe/v2/baidu/hot"
# Douyin trending
curl "https://60s.viki.moe/v2/douyin"
# Bilibili trending
curl "https://60s.viki.moe/v2/bili"
Response Format
Responses typically include:
{
"data": [
{
"title": "话题标题",
"url": "https://...",
"热度": "1234567",
"rank": 1
},
...
],
"update_time": "2024-01-15 14:00:00"
}
Example Interactions
User: "现在微博上什么最火?"
hot = get_weibo_hot()
top_5 = hot['data'][:5]
response = "🔥 微博热搜 TOP 5:\n\n"
for i, topic in enumerate(top_5, 1):
response += f"{i}. {topic['title']}\n"
response += f" 热度:{topic.get('热度', 'N/A')}\n\n"
User: "知乎上大家在讨论什么?"
zhihu = get_zhihu_hot()
response = "💡 知乎当前热门话题:\n\n"
for topic in zhihu['data'][:8]:
response += f"· {topic['title']}\n"
User: "对比各平台热点"
def compare_platform_trends():
all_topics = get_all_hot_topics()
summary = "📊 各平台热点概览\n\n"
platforms = {
'weibo': '微博',
'zhihu': '知乎',
'baidu': '百度',
'douyin': '抖音',
'bili': 'B站'
}
for key, name in platforms.items():
if all_topics.get(key):
top_topic = all_topics[key]['data'][0]
summary += f"{name}:{top_topic['title']}\n"
return summary
Best Practices
- Rate Limiting: Don't call APIs too frequently, data updates every few minutes
- Error Handling: Always handle network errors and invalid responses
- Caching: Cache results for 5-10 minutes to reduce API calls
- Top N: Usually showing top 5-10 items is sufficient
- Context: Provide platform context when showing trending topics
Common Use Cases
1. Daily Trending Summary
def get_daily_trending_summary():
weibo = get_weibo_hot()
zhihu = get_zhihu_hot()
summary = "📱 今日热点速览\n\n"
summary += "【微博热搜】\n"
summary += "\n".join([f"{i}. {t['title']}"
for i, t in enumerate(weibo['data'][:3], 1)<How to use hot-topics on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add hot-topics
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches hot-topics from GitHub repository vikiboss/60s-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate hot-topics. Access the skill through slash commands (e.g., /hot-topics) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★67 reviews- ★★★★★Pratham Ware· Dec 28, 2024
hot-topics reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mateo Ndlovu· Dec 28, 2024
Registry listing for hot-topics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ama Tandon· Dec 28, 2024
Useful defaults in hot-topics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Valentina Lopez· Dec 20, 2024
Registry listing for hot-topics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Maya Bhatia· Dec 16, 2024
Solid pick for teams standardizing on skills: hot-topics is focused, and the summary matches what you get after install.
- ★★★★★Hana Ndlovu· Dec 12, 2024
hot-topics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kwame Garcia· Dec 8, 2024
hot-topics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakura Taylor· Nov 19, 2024
hot-topics has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Benjamin Harris· Nov 7, 2024
hot-topics reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Isabella Jain· Nov 7, 2024
I recommend hot-topics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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