Financial Hot Topic Selection Tool

This Skill is used to filter high-quality topics suitable for guiding investment and financial management from financial hot spots.

Core Workflow

Phase 0: Data Crawling (Python)
   → Obtain 133 news headlines
Phase 1: Agent Intelligent Screening (Markdown Prompt)
   → Filter out 5 topics
Phase 2: Content Crawling (Python)
   → Crawl detailed content of 5 news articles
Phase 3: Agent Intelligent Parsing (Markdown Prompt)
   → Generate 5 complete topic plans

Separation of Responsibilities:

Python Scripts: Data Crawling (Phase 0, 2)
Agent: Intelligent Screening and Parsing (Phase 1, 3)

Phase 0: Data Crawling

Execution Method:

bash

cd scripts
python3 fetch_hot_topics.py

Output:

/tmp/hot_topics.json

Included Content:

133 news headlines (from 5 platforms)
Weibo, Baidu, Toutiao, Douyin, Cailianshe

Data Format:

json

{
  "fetch_time": "2026-02-13T19:36:24+08:00",
  "total_items": 133,
  "data": {
    "weibo": { "items": [...], "count": 30 },
    "baidu": { "items": [...], "count": 30 }
  }
}

Phase 1: Agent Intelligent Screening

Agent Execution Prompt:

You are a financial content topic selection expert. Please filter out 5 topics most suitable for guiding securities account opening/fund investment based on the following news headlines.

Headline List

{133 headlines read from /tmp/hot_topics.json}

Screening Criteria (Investment Relevance as Core)

Highest Priority (90-100 points)

Can directly link to investment products:

Money-making stories → AI funds/tech stocks/brokerage products
Investment cases → Gold ETF/asset allocation
Financial management topics → Fund fixed-investment/securities account opening

Examples:

"AI earns 2 million yuan per month" → Can recommend AI funds (95 points)
"Gold earns 1.96 million yuan" → Can recommend Gold ETF (92 points)

High Priority (80-89 points)

Can link to financial planning:

Salary topics → Wage income vs investment income
Savings topics → Saving money vs investment
Consumption topics → Financial planning

Example:

"Year-end bonus of 180 million yuan" → Can recommend fund fixed-investment (85 points)

Medium Priority (60-79 points)

Barely can link:

Education topics → Education funds
Pension topics → Pension investment

Example:

"Hengshui High School has changed" → Can link to education funds (70 points)

Not Selected (<60 points)

Cannot link to investment:

Pure entertainment gossip
Pure social news
Topics unrelated to finance

Output Format (JSON)

json

{
  "selected_indices": [1, 3, 5, 7, 9],
  "reasons": {
    "1": "✅ Can directly link: AI money-making → AI fund investment. Investment relevance 95 points, high public attention",
    "3": "✅ Can directly link: Gold case → Gold ETF. Investment relevance 92 points, strong data impact"
  },
  "investment_angles": {
    "1": "Can recommend: AI-themed funds, tech stocks, brokerage AI products",
    "3": "Can recommend: Gold ETF, brokerage gold products, asset allocation services"
  },
  "investment_relevance_score": {
    "1": 95,
    "3": 92
  }
}

Strict Requirements:

Fixed selection of 5 headlines (no more, no less)
Each headline has an investment relevance score ≥80 points
Specify the recommended investment product directions
Prioritize topics that can directly link to products (≥90 points)

Phase 2: Content Crawling

Execution Method:

bash

cd scripts
python3 fetch_news_content.py \
  --input /tmp/hot_topics.json \
  --indices 1,3,5,7,9 \
  --output /tmp/news_content.json

Input: 5 topic indices output from Phase 1

Output:

/tmp/news_content.json

Included Content:

Complete content of 5 news articles
Headline, URL, platform, body text, keywords

Data Format:

json

{
  "fetch_time": "2026-02-13T21:00:00+08:00",
  "total_articles": 5,
  "articles": [
    {
      "index": 1,
      "title": "Hangzhou guy runs a 1-person company and earns 2 million yuan per month with AI",
      "url": "https://...",
      "platform": "Weibo",
      "content": "Complete news content...",
      "keywords": ["AI", "entrepreneurship", "earn 2 million per month"]
    }
  ]
}

Phase 3: Agent Intelligent Parsing

Agent Execution Prompt:

You are a financial content planning expert. Please generate a complete topic plan for each news article based on the following 5 news contents.

News List

{5 news articles read from /tmp/news_content.json}

Parsing Requirements

1. News Content Summary

Extract core information (100-150 words)
Retain key data and cases
Highlight investment-related information

2. Topic Selection Reasons

Explain investment relevance (80-100 points)
Analyze public attention
Predict viral potential

3. Guidance Strategy

Entry Point: How to start from the news topic
Turning Point: How to naturally shift to investment
Product Recommendation: Specific investment products to recommend
Script Examples: 2-3 guiding scripts

Example Guidance Strategy:

Entry Point: AI entrepreneurship earns 2 million per month, triggering the thought "Can I do it too?"
Turning Point: Ordinary people don't have the skills, but can share the dividends through investment
Product Recommendation: AI-themed funds, tech ETFs, brokerage AI products
Script Examples:
  "Want to participate in AI dividends? No need to have technical skills, investing in AI funds allows you to get a share"
  "Open an account to buy AI funds, ordinary people can also enjoy the dividends of AI development"

4. Original Link

Retain the complete news URL
Facilitate subsequent review and citation

Output Format (JSON)

json

{
  "topic_plans": [
    {
      "index": 1,
      "title": "Hangzhou guy runs a 1-person company and earns 2 million yuan per month with AI",
      "summary": "A Hangzhou entrepreneur operates a 1-person company through AI tools, with a monthly income of 2 million yuan. The main business is...",
      "selection_reason": {
        "investment_relevance": 95,
        "mass_attention": "High (AI + money-making topic)",
        "viral_potential": "Extremely strong (data impact of 2 million yuan monthly income)"
      },
      "guidance_strategy": {
        "entry_point": "AI entrepreneurship earns 2 million per month, triggering the thought 'Can I do it too?'",
        "turning_point": "Ordinary people don't have the skills, but can share AI dividends through investment",
        "product_recommendation": "AI-themed funds, tech ETFs, brokerage AI products",
        "script_examples": [
          "Want to participate in AI dividends? Investing in AI funds allows you to get a share",
          "Open an account to buy AI funds, ordinary people can also enjoy the dividends of AI development"
        ]
      },
      "source_url": "https://..."
    }
  ]
}

Strict Requirements:

Each topic plan must have all 4 sections
Guidance strategy must be specific and operable
Product recommendations must be related to brokerages/funds
Scripts must be natural and not forced

Usage Example

Complete Workflow

bash

# Step 1: Data Crawling
python3 scripts/fetch_hot_topics.py

# Step 2: Agent Intelligent Screening
# Agent reads the Phase 1 prompt in this file and executes it

# Step 3: Content Crawling
python3 scripts/fetch_news_content.py \
  --input /tmp/hot_topics.json \
  --indices 1,3,5,7,9

# Step 4: Agent Intelligent Parsing
# Agent reads the Phase 3 prompt in this file and executes it

Total Time Consumption: Approximately 15 minutes

Script Explanation

scripts/fetch_hot_topics.py

Function: Calls the TrendRadar API to crawl hot news headlines from 5 platforms

Output: 133 news headlines (JSON)

scripts/fetch_news_content.py

Function: Crawls detailed news content based on topic indices

Input: Topic indices (e.g., 1,3,5,7,9)

Output: Complete content of 5 news articles (JSON)

Reference Materials

references/Topic Selection Methodology.md

Includes topic selection methodologies from top financial new media influencers, including:

5 core methodologies (pain point-driven, emotional resonance, data impact, story expression, practical orientation)
Topic formulas (number + conflict + result, pain point + solution, etc.)
Evaluation criteria (virality 40%, relevance 30%, practicality 20%, compliance 10%)

Agent can refer to this file to optimize topic selection strategies as needed.

Notes

Investment relevance is core: Each topic must be able to naturally shift to securities account opening or fund investment
Fixed output of 5 topics: No more, no less, ensure quality
Investment relevance ≥80 points: Topics with scores below 80 will be excluded
Python is responsible for data: Agent does not need to perform data crawling
Agent is responsible for intelligence: Screening and parsing are completed by Agent

hot_topics_selector

NPX Install

Tags

SKILL.md Content (Chinese)

Financial Hot Topic Selection Tool

Core Workflow

Phase 0: Data Crawling

Phase 1: Agent Intelligent Screening

Headline List

Screening Criteria (Investment Relevance as Core)

Highest Priority (90-100 points)

High Priority (80-89 points)

Medium Priority (60-79 points)

Not Selected (<60 points)

Output Format (JSON)

Phase 2: Content Crawling

Phase 3: Agent Intelligent Parsing

News List

Parsing Requirements

1. News Content Summary

2. Topic Selection Reasons

3. Guidance Strategy

4. Original Link

Output Format (JSON)

Usage Example

Complete Workflow

Script Explanation

scripts/fetch_hot_topics.py

scripts/fetch_news_content.py

Reference Materials

references/Topic Selection Methodology.md

Notes