weather-query

vikiboss/60s-skills · updated Apr 8, 2026

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

$npx skills add https://github.com/vikiboss/60s-skills --skill weather-query
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summary

Real-time weather and forecast data for locations across China.

  • Provides two core endpoints: real-time weather (temperature, humidity, wind, air quality) and multi-day forecasts with daily highs and lows
  • Accepts Chinese location names (cities or districts) as query parameters; supports major cities and most districts nationwide
  • Includes practical helper patterns for weather-based recommendations, multi-city comparisons, and travel suitability checks
  • Returns structured JSON respons
skill.md

Weather Query Skill

This skill enables AI agents to fetch real-time weather information and forecasts for locations in China using the 60s API.

When to Use This Skill

Use this skill when users:

  • Ask about current weather conditions
  • Want weather forecasts
  • Need temperature, humidity, wind information
  • Request air quality data
  • Plan outdoor activities and need weather info

API Endpoints

1. Real-time Weather

URL: https://60s.viki.moe/v2/weather/realtime
Method: GET

2. Weather Forecast

URL: https://60s.viki.moe/v2/weather/forecast
Method: GET

Parameters

  • query (required): Location name in Chinese
    • Can be city name: "北京", "上海", "广州"
    • Can be district name: "海淀区", "浦东新区"

How to Use

Get Real-time Weather

import requests

def get_realtime_weather(query):
    url = 'https://60s.viki.moe/v2/weather/realtime'
    response = requests.get(url, params={'query': query})
    return response.json()

# Example
weather = get_realtime_weather('北京')
print(f"☁️ {weather['location']}天气")
print(f"🌡️ 温度:{weather['temperature']}°C")
print(f"💨 风速:{weather['wind']}")
print(f"💧 湿度:{weather['humidity']}")

Get Weather Forecast

def get_weather_forecast(query):
    url = 'https://60s.viki.moe/v2/weather/forecast'
    response = requests.get(url, params={'query': query})
    return response.json()

# Example
forecast = get_weather_forecast('上海')
for day in forecast['forecast']:
    print(f"{day['date']}: {day['weather']} {day['temp_low']}°C ~ {day['temp_high']}°C")

Simple bash example

# Real-time weather
curl "https://60s.viki.moe/v2/weather/realtime?query=北京"

# Weather forecast
curl "https://60s.viki.moe/v2/weather/forecast?query=上海"

Response Format

Real-time Weather Response

{
  "location": "北京",
  "weather": "晴",
  "temperature": "15",
  "humidity": "45%",
  "wind": "东北风3级",
  "air_quality": "良",
  "updated": "2024-01-15 14:00:00"
}

Forecast Response

{
  "location": "上海",
  "forecast": [
    {
      "date": "2024-01-15",
      "day_of_week": "星期一",
      "weather": "多云",
      "temp_low": "10",
      "temp_high": "18",
      "wind": "东风3-4级"
    },
    ...
  ]
}

Example Interactions

User: "北京今天天气怎么样?"

Agent Response:

weather = get_realtime_weather('北京')
response = f"""
☁️ 北京今日天气

天气状况:{weather['weather']}
🌡️ 温度:{weather['temperature']}°C
💧 湿度:{weather['humidity']}
💨 风力:{weather['wind']}
🌫️ 空气质量:{weather['air_quality']}
"""

User: "上海未来三天天气"

forecast = get_weather_forecast('上海')
response = "📅 上海未来天气预报\n\n"
for day in forecast['forecast'][:3]:
    response += f"{day['date']} {day['day_of_week']}\n"
    response += f"  {day['weather']} {day['temp_low']}°C ~ {day['temp_high']}°C\n"
    response += f"  {day['wind']}\n\n"

User: "深圳会下雨吗?"

weather = get_realtime_weather('深圳')
if '雨' in weather['weather']:
    print("☔ 是的,深圳现在正在下雨")
    print("建议带伞出门!")
else:
    forecast = get_weather_forecast('深圳')
    rain_days = [d for d in forecast['forecast'] if '雨' in d['weather']]
    if rain_days:
        print(f"未来{rain_days[0]['date']}可能会下雨")
    else:
        print("近期没有降雨预报")

Best Practices

  1. Location Names: Always use Chinese characters for location names

  2. Error Handling: Check if the location is valid before displaying results

  3. Context: Provide relevant context based on weather conditions

    • Rain: Suggest bringing umbrella
    • Hot: Recommend staying hydrated
    • Cold: Advise wearing warm clothes
    • Poor AQI: Suggest wearing mask
  4. Caching: Weather data is updated regularly but can be cached for short periods

  5. Fallbacks: If a specific district doesn't work, try the city name

Common Use Cases

1. Weather-based Recommendations

def give_weather_advice(location):
    weather = get_realtime_weather(location)
    advice = []
    
    temp = int(weather['temperature'])
    if temp > 30:
        advice.append("🔥 天气炎热,注意防暑降温,多喝水")
    elif temp < 5:
        advice.append
how to use weather-query

How to use weather-query 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 weather-query
2

Execute installation command

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

$npx skills add https://github.com/vikiboss/60s-skills --skill weather-query

The skills CLI fetches weather-query from GitHub repository vikiboss/60s-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/weather-query

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

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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

4.825 reviews
  • Zara Rao· Nov 27, 2024

    Registry listing for weather-query matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Zara Ramirez· Oct 18, 2024

    Keeps context tight: weather-query is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Yash Thakker· Sep 25, 2024

    Solid pick for teams standardizing on skills: weather-query is focused, and the summary matches what you get after install.

  • Rahul Santra· Sep 21, 2024

    Keeps context tight: weather-query is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Tariq Chawla· Sep 21, 2024

    weather-query is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Omar Zhang· Sep 5, 2024

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

  • Isabella Taylor· Aug 24, 2024

    weather-query has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Dhruvi Jain· Aug 16, 2024

    We added weather-query from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Pratham Ware· Aug 12, 2024

    Registry listing for weather-query matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Tariq Malhotra· Aug 12, 2024

    weather-query fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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