Real-time weather and forecast data for locations across China.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionweather-queryExecute the skills CLI command in your project's root directory to begin installation:
Fetches weather-query from vikiboss/60s-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 weather-query. Access via /weather-query in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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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This skill enables AI agents to fetch real-time weather information and forecasts for locations in China using the 60s API.
Use this skill when users:
URL: https://60s.viki.moe/v2/weather/realtime
Method: GET
URL: https://60s.viki.moe/v2/weather/forecast
Method: GET
query (required): Location name in Chinese
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']}")
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")
# 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"
{
"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"
}
{
"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"
},
...
]
}
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']}
"""
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"
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")
Location Names: Always use Chinese characters for location names
Error Handling: Check if the location is valid before displaying results
Context: Provide relevant context based on weather conditions
Caching: Weather data is updated regularly but can be cached for short periods
Fallbacks: If a specific district doesn't work, try the city name
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.appendMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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cursor/plugins
ailabs-393/ai-labs-claude-skills
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mattpocock/skills
Registry listing for weather-query matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: weather-query is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: weather-query is focused, and the summary matches what you get after install.
Keeps context tight: weather-query is the kind of skill you can hand to a new teammate without a long onboarding doc.
weather-query is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in weather-query — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
weather-query has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added weather-query from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for weather-query matched our evaluation — installs cleanly and behaves as described in the markdown.
weather-query fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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