geo

name	geo
description	Analyze geographic content distribution — which countries and regions produce the most content
argument-hint	<dataset-folder-name>
risk	safe
user-invocable	true
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep"]
model	claude-sonnet-4-6
context	fork
agent	general-purpose

Geo — Geographic Content Analysis

You are executing the :geo skill for the 10x-content-intel plugin.

What This Skill Does

Analyzes the geographic distribution of content — which countries produce the most, regional patterns, and co-production networks.

Instructions

Step 1: Load Cleaned Data

• Check if output/{argument}/{argument}_cleaned.csv exists
• If NOT, first run the Content Profiler (read agents/content-profiler.md) to clean the data
• Load the cleaned CSV into a pandas DataFrame

Step 2: Read Agent Instructions

• Read agents/geo-analyst.md for detailed analysis instructions

Step 3: Handle Multi-Country Data

The country column often contains multiple countries separated by commas. Split and explode:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path

dataset = "{argument}"
df = pd.read_csv(f"output/{dataset}/{dataset}_cleaned.csv")
out_dir = Path(f"output/{dataset}/geo")
out_dir.mkdir(parents=True, exist_ok=True)

# Split multi-country entries
if 'country' in df.columns:
    df_countries = df.dropna(subset=['country']).copy()
    df_countries['country'] = df_countries['country'].str.split(',')
    df_exploded = df_countries.explode('country')
    df_exploded['country'] = df_exploded['country'].str.strip()

Step 4: Generate Charts (minimum 5):

1. Top 15 Content-Producing Countries — Horizontal bar chart with value labels
2. Regional Content Share — Donut chart (group countries into continents/regions)
3. Content Type by Top 10 Countries — Grouped bar (Movies vs TV Shows)
4. Top 5 Countries Growth Over Time — Multi-line chart
5. Genre Preferences by Top Regions — Heatmap showing genre × region

Step 5: Region Mapping

Map countries to regions:

region_map = {
    'United States': 'North America', 'Canada': 'North America', 'Mexico': 'Latin America',
    'United Kingdom': 'Europe', 'France': 'Europe', 'Germany': 'Europe', 'Spain': 'Europe',
    'India': 'Asia', 'Japan': 'Asia', 'South Korea': 'Asia', 'China': 'Asia',
    'Nigeria': 'Africa', 'South Africa': 'Africa', 'Egypt': 'Africa',
    'Australia': 'Oceania', 'Brazil': 'Latin America', 'Argentina': 'Latin America',
    # ... extend as needed, default to 'Other'
}

Step 6: Save Outputs

• Save all charts to output/{argument}/geo/chart_*.png
• Write output/{argument}/geo/geo_analysis.md with insights

Output

• output/{argument}/geo/geo_analysis.md
• output/{argument}/geo/chart_01_top_countries.png
• output/{argument}/geo/chart_02_regional_share.png
• output/{argument}/geo/chart_03_type_by_country.png
• output/{argument}/geo/chart_04_country_growth.png
• output/{argument}/geo/chart_05_genre_by_region.png