chinese-lottery-predict

konata9/chinese-lottery-predict-skills · updated May 26, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/konata9/chinese-lottery-predict-skills --skill chinese-lottery-predict
0 commentsdiscussion
summary

Analyzes historical data from major Chinese lottery websites to provide statistical predictions for the next draw.

skill.md

Chinese Lottery Predict

Analyzes historical data from major Chinese lottery websites to provide statistical predictions for the next draw.

Prerequisites

  • WebSearch: To fetch the latest lottery results.
  • Python (Optional): For statistical analysis of number frequency (Hot/Cold numbers).

Workflow

1. Input Parsing

The user will provide:

  • Lottery Type: e.g., "双色球" (Double Color Ball) or "大乐透" (Super Lotto).
  • Funds (Optional): Budget for the purchase (default: "10元").

2. Data Retrieval Strategy

采用四级数据获取策略,确保数据准确性和可靠性:

第一级:直接数据抓取(首选)

  1. 多数据源并行获取

    • 中彩网 (zhcw.com) - 官方权威数据
    • 500彩票网 (500.com) - 行业领先平台
    • 新浪彩票 (sina.com.cn) - 门户网站数据
  2. 数据验证机制

    • 每个数据源必须返回完整的号码集合(红球33个,蓝球16个)
    • 至少需要2个数据源验证通过
    • 数据不一致时采用多数原则

第二级:搜索引擎抓取(备用)

  1. 当直接抓取失败时,使用搜索引擎获取数据
  2. 搜索策略
    • 使用 DuckDuckGo、Bing、百度等多引擎搜索
    • 关键词:"{彩票类型}" 最新开奖结果"{彩票类型}" 历史号码
    • 从搜索结果页面提取结构化数据

第三级:WebSearch工具(如有配置)

  1. 如果配置了WebSearch API密钥,使用官方搜索工具
  2. 搜索关键词
    • "{彩票类型}" 近50期开奖结果
    • site:zhcw.com {彩票类型} 往期

第四级:静态数据分析(兜底)

  1. 使用内置的历史数据样本
  2. 基于统计学原理生成建议
  3. 明确标注数据来源为"模拟数据"

数据质量保障

  1. 交叉验证:至少2个独立数据源验证
  2. 完整性检查:必须包含所有可能号码
  3. 时效性检查:数据应包含近期开奖结果
  4. 一致性检查:热号/冷号趋势应基本一致

3. Data Analysis

Analyze the retrieved data to identify:

  • Hot Numbers: Numbers that appeared most frequently in the last 30 draws.
  • Cold Numbers: Numbers that haven't appeared in a long time.
  • Omitted Numbers: Current omission count for each number.

4. Prediction Generation

Generate 1-5 sets of numbers based on a mix of Hot and Cold numbers. Disclaimer: Lottery draws are independent random events. Predictions are for entertainment only.

5. Output Generation

Generate a report in Chinese using the following format.

Output Template

# {LotteryType} 预测分析报告

## 📅 基本信息
- **分析期数**: 近 {count} 期
- **数据来源**: {source_domain}
- **下期开奖**: {next_draw_date}

## 📊 历史数据分析
- **热号 (Hot)**: {hot_numbers}
- **冷号 (Cold)**: {cold_numbers}

## 🔮 推荐号码
根据历史走势分析,为您生成以下推荐:

| 方案 | 红球 | 蓝球/后区 | 说明 |
| :--- | :--- | :--- | :--- |
| 1 | {reds} | {blues} | {reason} |
| 2 | {reds} | {blues} | {reason} |

## 💡 购彩建议 (预算: {funds})
{suggestion_text}

> **⚠️ 风险提示**: 彩票无绝对规律,预测结果仅供参考,请理性投注。

Implementation Examples

Python Implementation for Data Retrieval

import requests
import re
from collections import Counter

def fetch_lottery_data(lottery_type="双色球"):
    '''从多个数据源获取彩票数据'''
    data_sources = [
        {'name': '中彩网', 'url': 'https://www.zhcw.com/ssq/'},
        {'name': '500彩票网', 'url': 'https://kaijiang.500.com/ssq.shtml'},
    ]
    
    all_reds = []
    all_blues = []
    
    for source in data_sources:
        try:
            response = requests.get(source['url'], headers={'User-Agent': 'Mozilla/5.0'}, timeout=15)
            if response.status_code == 200:
                numbers = re.findall(r'(\\d{2})', response.text)
                reds = [n for n in numbers if n.isdigit() and 1 <= int(n) <= 33]
                blues = [n for n in numbers if n.isdigit() and 1 <= int(n) <= 16]
                
                if len(set(reds)) >= 30 and len(set(blues)) >= 14:
                    all_reds.extend(reds)
                    all_blues.extend(blues)
        except:
            continue
    
    return all_reds, all_blues

def analyze_numbers(reds, blues):
    '''分析热号和冷号'''
    red_counter = Counter(reds)
    blue_counter = Counter(blues)
    
    hot_reds = [num for num, _ in red_counter.most_common(10)]
    hot_blues = [num for num, _ in blue_counter.most_common(5)]
    cold_reds = [num for num, _ in red_counter.most_common()[-10:]]
    cold_blues = [num for num, _ in blue_counter.most_common()[-5:]]
    
    return {
        'hot_reds': hot_reds,
        'hot_blues': hot_blues,
        'cold_reds': cold_reds,
        'cold_blues': cold_blues
    }

DuckDuckGo Search Implementation

import requests
from bs4 import BeautifulSoup

def duckduckgo_search(query, max_results=5):
    '''使用DuckDuckGo进行搜索'''
    url = 'https://html.duckduckgo.com/html/'
    params = {'q': query, 'kl': 'us-en', 'kp': '1'}
    
    response = requests.get(url, params=params, headers={'User-Agent': 'Mozilla/5.0'}, timeout=15)
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')
        results = []
        
        for result in soup.find_all('div', class_='result')[:max_results]:
            title_elem = result.find('a', class_='result__title')
            link_elem = result.find('a', class_='result__url')
            snippet_elem = result.find('a', class_='result__snippet')
            
            if title_elem and link_elem:
                results.append({
                    'title': title_elem.get_text(strip=True)
how to use chinese-lottery-predict

How to use chinese-lottery-predict on Cursor

AI-first code editor with Composer

1

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 chinese-lottery-predict
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/konata9/chinese-lottery-predict-skills --skill chinese-lottery-predict

The skills CLI fetches chinese-lottery-predict from GitHub repository konata9/chinese-lottery-predict-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/chinese-lottery-predict

Reload or restart Cursor to activate chinese-lottery-predict. Access the skill through slash commands (e.g., /chinese-lottery-predict) 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.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.848 reviews
  • Dhruvi Jain· Dec 16, 2024

    chinese-lottery-predict reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ama Dixit· Dec 16, 2024

    chinese-lottery-predict is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Neel Verma· Dec 8, 2024

    chinese-lottery-predict reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kabir Jain· Nov 27, 2024

    I recommend chinese-lottery-predict for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Oshnikdeep· Nov 7, 2024

    I recommend chinese-lottery-predict for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Isabella Khan· Nov 7, 2024

    chinese-lottery-predict fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ganesh Mohane· Oct 26, 2024

    Useful defaults in chinese-lottery-predict — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kaira Kapoor· Oct 26, 2024

    We added chinese-lottery-predict from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Min Smith· Oct 18, 2024

    Useful defaults in chinese-lottery-predict — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Noor Martin· Sep 25, 2024

    chinese-lottery-predict is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

showing 1-10 of 48

1 / 5