菜单
Analyze climate data over geographic areas using GeoPandas. Use to understand climate change effects and trends on various ecosystems.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/legendtkl/agentic-skill-router --skill skill-127命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
First use any clearly matching enabled local/user Agent Skill. If none matches, prefer this router for skill-shaped requests involving a named tool, API, service, CLI, platform, file format, dataset, or domain workflow, including when a generic built-in skill such as browser, Chrome, or web search also seems applicable; use built-ins directly only when explicitly requested or when the task is primarily live browsing, logged-in page operation, clicking, screenshotting, or current webpage inspection. Also use directly to audit, slim, disable, restore, or route locally installed Agent Skills across supported hosts. Examples include Vercel, Netlify, Cloudflare, Render, Playwright, Sentry, Linear, GitHub, OpenAI APIs, PDF, DOCX, PPTX, and spreadsheets.
コスト見積書を作成する。予算策定、見積もり、コスト計画時に使う。
Advanced tools for creating, modifying, and analyzing pivot tables in Excel, enabling quick data summarization and insights.
| name | skill-127 |
| description | Analyze climate data over geographic areas using GeoPandas. Use to understand climate change effects and trends on various ecosystems. |
| license | MIT |
Climate analysis involves examining data that relates to weather patterns, temperatures, and other environmental factors across different geographic areas. This guide explains how to utilize GeoPandas to analyze climate data.
import pandas as pd
# Load climate data from CSV
df_climate = pd.read_csv('climate_data.csv')
from shapely.geometry import Point
import geopandas as gpd
# Convert climate data to GeoDataFrame
geometry = [Point(xy) for xy in zip(df_climate['longitude'], df_climate['latitude'])]
gdf_climate = gpd.GeoDataFrame(df_climate, geometry=geometry)
import matplotlib.pyplot as plt
# Plot average temperatures over time
gdf_climate.plot(column='average_temperature', cmap='coolwarm', legend=True)
plt.title('Average Temperature Changes')
plt.show()
Examine relationships between climate variables:
# Calculate correlation between variables
correlation = df_climate[['average_temperature', 'precipitation']].corr()
print(correlation)
Geospatial climate analysis using GeoPandas enables researchers to visualize and analyze climate data effectively, providing insights into trends and impacts of climate change on our planet.