trends

name	trends
description	Analyze temporal patterns, content growth, genre shifts, and seasonal trends in the content library
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

Trends — Content Trend Analysis

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

What This Skill Does

Analyzes how the content library has evolved over time — growth patterns, genre shifts, format changes, and seasonal patterns.

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/trend-analyst.md for detailed analysis instructions

Step 3: Run Trend Analysis

Create a Python script that generates the following analyses and charts:

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

# Setup
sns.set_style("whitegrid")
PALETTE = sns.color_palette("muted")
plt.rcParams.update({
    'figure.dpi': 120,
    'axes.titlesize': 14, 'axes.titleweight': 'bold',
    'axes.labelsize': 12, 'figure.facecolor': 'white',
    'axes.facecolor': 'white', 'axes.edgecolor': '#cccccc',
    'grid.color': '#eeeeee'
})

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

Charts to Generate (minimum 6):

1. Yearly Content Additions — Stacked bar (Movies vs TV Shows per year)
2. Cumulative Content Growth — Line chart showing library size over time
3. Monthly Release Seasonality — Which months get the most content?
4. Top 10 Genres Over Time — How genre popularity has shifted
5. Average Movie Duration by Year — Is content getting shorter?
6. Rating Distribution Over Time — Content maturity shift

Step 4: Generate Insights

After each chart, write a 2-3 sentence insight explaining what the data reveals.

Step 5: Save Outputs

• Save all charts to output/{argument}/trends/chart_*.png
• Write output/{argument}/trends/trend_analysis.md with all insights and embedded chart references

Output

• output/{argument}/trends/trend_analysis.md
• output/{argument}/trends/chart_01_yearly_additions.png
• output/{argument}/trends/chart_02_cumulative_growth.png
• output/{argument}/trends/chart_03_monthly_seasonality.png
• output/{argument}/trends/chart_04_genre_trends.png
• output/{argument}/trends/chart_05_duration_trends.png
• output/{argument}/trends/chart_06_rating_shift.png