Analyzes historical data from major Chinese lottery websites to provide statistical predictions for the next draw.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionchinese-lottery-predictExecute the skills CLI command in your project's root directory to begin installation:
Fetches chinese-lottery-predict from konata9/chinese-lottery-predict-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate chinese-lottery-predict. Access via /chinese-lottery-predict in your agent's command palette.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Analyzes historical data from major Chinese lottery websites to provide statistical predictions for the next draw.
The user will provide:
采用四级数据获取策略,确保数据准确性和可靠性:
多数据源并行获取:
数据验证机制:
"{彩票类型}" 最新开奖结果、"{彩票类型}" 历史号码"{彩票类型}" 近50期开奖结果site:zhcw.com {彩票类型} 往期Analyze the retrieved data to identify:
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.
Generate a report in Chinese using the following format.
# {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。
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
}
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)✓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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
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4.8★★★★★48 reviews- DDhruvi Jain★★★★★Dec 16, 2024
chinese-lottery-predict reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAma Dixit★★★★★Dec 16, 2024
chinese-lottery-predict is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- NNeel Verma★★★★★Dec 8, 2024
chinese-lottery-predict reduced setup friction for our internal harness; good balance of opinion and flexibility.
- KKabir Jain★★★★★Nov 27, 2024
I recommend chinese-lottery-predict for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- OOshnikdeep★★★★★Nov 7, 2024
I recommend chinese-lottery-predict for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- IIsabella Khan★★★★★Nov 7, 2024
chinese-lottery-predict fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- GGanesh 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.
- KKaira Kapoor★★★★★Oct 26, 2024
We added chinese-lottery-predict from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- MMin 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.
- NNoor Martin★★★★★Sep 25, 2024
chinese-lottery-predict is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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