Adoption

Agent Skills are supported by leading AI development tools.

VS Code Gemini CLI GitHub Goose Amp Cursor Claude Code Letta OpenCode Claude OpenAI Codex Factory VS Code Gemini CLI GitHub Goose Amp Cursor Claude Code Letta OpenCode Claude OpenAI Codex Factory

balaraj74/skills/data-analysis

Name: skills/data-analysis
Author: balaraj74

skills/data-analysis/SKILL.md

npx skillsauth add balaraj74/f.r.i.d.a.y skills/data-analysis

Clean

TrivyContainer and dependency vulnerability scanner

Clean

SemgrepStatic code analysis for vulnerabilities

Clean

mcp-scan (Snyk)Model Context Protocol security validation

Skipped

Snyk (dep)Open source security scanning

Skipped

Socket.devSupply chain security analysis

Skipped

VirusTotalMulti-engine malware detection

Skipped

CrowdStrikeAdvanced threat intelligence

Skipped

OSV-ScannerOpen Source Vulnerability database check

Skipped

OWASP Dep-Check

Data Analysis

Analyze data, automate reports, and create visualizations.

Python Data Analysis

Pandas Basics

import pandas as pd
import numpy as np

# Load data
df = pd.read_csv("data.csv")
df = pd.read_excel("data.xlsx")
df = pd.read_json("data.json")

# Explore data
df.head()           # First 5 rows
df.info()           # Column types and nulls
df.describe()       # Statistics
df.shape            # (rows, cols)
df.columns          # Column names

# Filter and select
df[df['column'] > 100]
df[['col1', 'col2']]
df.query("age > 25 and city == 'NYC'")

# Group and aggregate
df.groupby('category')['value'].sum()
df.groupby(['cat1', 'cat2']).agg({'value': 'mean', 'count': 'sum'})

# Pivot tables
pd.pivot_table(df, values='sales', index='region', columns='product', aggfunc='sum')

# Save results
df.to_csv("output.csv", index=False)
df.to_excel("output.xlsx", index=False)

Quick Analysis Script

python3 << 'EOF'
import pandas as pd
import sys

df = pd.read_csv(sys.argv[1] if len(sys.argv) > 1 else "data.csv")
print("=== Data Summary ===")
print(f"Rows: {len(df)}, Columns: {len(df.columns)}")
print(f"\nColumns: {list(df.columns)}")
print(f"\n{df.describe()}")
print(f"\nMissing values:\n{df.isnull().sum()}")
EOF

Data Visualization

Matplotlib/Seaborn

import matplotlib.pyplot as plt
import seaborn as sns

# Line chart
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['value'])
plt.title('Trend Over Time')
plt.savefig('chart.png')

# Bar chart
df.groupby('category')['value'].sum().plot(kind='bar')
plt.savefig('bar_chart.png')

# Histogram
df['column'].hist(bins=20)
plt.savefig('histogram.png')

# Heatmap (correlation)
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.savefig('correlation.png')

# Scatter plot
sns.scatterplot(data=df, x='col1', y='col2', hue='category')
plt.savefig('scatter.png')

Quick Chart Script

python3 << 'EOF'
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("data.csv")
df.plot(kind='line', x='date', y='value', figsize=(12, 6))
plt.title('Data Visualization')
plt.tight_layout()
plt.savefig('chart.png')
print("Chart saved to chart.png")
EOF

Automated Reports

Generate HTML Report

import pandas as pd
from datetime import datetime

df = pd.read_csv("data.csv")

html = f"""
<!DOCTYPE html>
<html>
<head><title>Report - {datetime.now().strftime('%Y-%m-%d')}</title></head>
<body>
<h1>Data Report</h1>
<h2>Summary Statistics</h2>
{df.describe().to_html()}
<h2>Sample Data</h2>
{df.head(10).to_html()}
</body>
</html>
"""

with open("report.html", "w") as f:
    f.write(html)

PDF Report with ReportLab

from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("report.pdf", pagesize=letter)
c.drawString(100, 750, "Data Analysis Report")
c.drawString(100, 700, f"Total Records: {len(df)}")
c.save()

Command Line Analysis

CSV Analysis with csvkit

# Install csvkit
pip install csvkit

# View CSV structure
csvstat data.csv

# Query CSV with SQL
csvsql --query "SELECT category, SUM(value) FROM data GROUP BY category" data.csv

# Convert formats
in2csv data.xlsx > data.csv
csvjson data.csv > data.json

# Filter rows
csvgrep -c category -m "Electronics" data.csv

Quick Stats with awk

# Sum column 3
awk -F',' '{sum+=$3} END {print "Sum:", sum}' data.csv

# Average
awk -F',' '{sum+=$3; count++} END {print "Avg:", sum/count}' data.csv

# Count unique values
awk -F',' '{a[$2]++} END {for(k in a) print k, a[k]}' data.csv

Database Integration

SQLite Analysis

# Query SQLite database
sqlite3 database.db "SELECT * FROM table LIMIT 10"

# Export to CSV
sqlite3 -header -csv database.db "SELECT * FROM table" > export.csv

# Run SQL file
sqlite3 database.db < queries.sql

Python SQLite

import sqlite3
import pandas as pd

conn = sqlite3.connect('database.db')
df = pd.read_sql_query("SELECT * FROM table WHERE condition", conn)

Tools Required

python3 with pandas, numpy, matplotlib, seaborn
csvkit - CSV utilities
sqlite3 - SQLite database
jq - JSON processing

Installation

pip install pandas numpy matplotlib seaborn openpyxl xlrd csvkit reportlab
sudo apt install -y sqlite3 jq

balaraj74/skills/data-analysis

skills/data-analysis/SKILL.md

# Data Analysis Analyze data, automate reports, and create visualizations. ## Python Data Analysis ### Pandas Basics ```python import pandas as pd import numpy as np # Load data df = pd.read_csv("data.csv") df = pd.read_excel("data.xlsx") df = pd.read_json("data.json") # Explore data df.head() # First 5 rows df.info() # Column types and nulls df.describe() # Statistics df.shape # (rows, cols) df.columns # Column names # Filter and select df[df[

development

Updated Apr 3, 2026

$ install --global

skillsauth

npx skillsauth add balaraj74/f.r.i.d.a.y skills/data-analysis

Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.

Security Scan Results

3 of 9 scanners reported clean

Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.

Scanners Passed

Scanners in report

Clean

TrivyContainer and dependency vulnerability scanner

95%

Clean

SemgrepStatic code analysis for vulnerabilities

95%

Clean

mcp-scan (Snyk)Model Context Protocol security validation

95%

Skipped

Snyk (dep)Open source security scanning

50%

Skipped

Socket.devSupply chain security analysis

50%

Skipped

VirusTotalMulti-engine malware detection

50%

Skipped

CrowdStrikeAdvanced threat intelligence

50%

Skipped

OSV-ScannerOpen Source Vulnerability database check

50%

Skipped

OWASP Dep-Check

50%

Last scanned: Apr 3, 2026, 9:06 PM5.6s1 file scanned

SKILL.md

Data Analysis

Analyze data, automate reports, and create visualizations.

Python Data Analysis

Pandas Basics

import pandas as pd
import numpy as np

# Load data
df = pd.read_csv("data.csv")
df = pd.read_excel("data.xlsx")
df = pd.read_json("data.json")

# Explore data
df.head()           # First 5 rows
df.info()           # Column types and nulls
df.describe()       # Statistics
df.shape            # (rows, cols)
df.columns          # Column names

# Filter and select
df[df['column'] > 100]
df[['col1', 'col2']]
df.query("age > 25 and city == 'NYC'")

# Group and aggregate
df.groupby('category')['value'].sum()
df.groupby(['cat1', 'cat2']).agg({'value': 'mean', 'count': 'sum'})

# Pivot tables
pd.pivot_table(df, values='sales', index='region', columns='product', aggfunc='sum')

# Save results
df.to_csv("output.csv", index=False)
df.to_excel("output.xlsx", index=False)

Quick Analysis Script

python3 << 'EOF'
import pandas as pd
import sys

df = pd.read_csv(sys.argv[1] if len(sys.argv) > 1 else "data.csv")
print("=== Data Summary ===")
print(f"Rows: {len(df)}, Columns: {len(df.columns)}")
print(f"\nColumns: {list(df.columns)}")
print(f"\n{df.describe()}")
print(f"\nMissing values:\n{df.isnull().sum()}")
EOF

Data Visualization

Matplotlib/Seaborn

import matplotlib.pyplot as plt
import seaborn as sns

# Line chart
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['value'])
plt.title('Trend Over Time')
plt.savefig('chart.png')

# Bar chart
df.groupby('category')['value'].sum().plot(kind='bar')
plt.savefig('bar_chart.png')

# Histogram
df['column'].hist(bins=20)
plt.savefig('histogram.png')

# Heatmap (correlation)
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.savefig('correlation.png')

# Scatter plot
sns.scatterplot(data=df, x='col1', y='col2', hue='category')
plt.savefig('scatter.png')

Quick Chart Script

python3 << 'EOF'
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("data.csv")
df.plot(kind='line', x='date', y='value', figsize=(12, 6))
plt.title('Data Visualization')
plt.tight_layout()
plt.savefig('chart.png')
print("Chart saved to chart.png")
EOF

Automated Reports

Generate HTML Report

import pandas as pd
from datetime import datetime

df = pd.read_csv("data.csv")

html = f"""
<!DOCTYPE html>
<html>
<head><title>Report - {datetime.now().strftime('%Y-%m-%d')}</title></head>
<body>
<h1>Data Report</h1>
<h2>Summary Statistics</h2>
{df.describe().to_html()}
<h2>Sample Data</h2>
{df.head(10).to_html()}
</body>
</html>
"""

with open("report.html", "w") as f:
    f.write(html)

PDF Report with ReportLab

from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("report.pdf", pagesize=letter)
c.drawString(100, 750, "Data Analysis Report")
c.drawString(100, 700, f"Total Records: {len(df)}")
c.save()

Command Line Analysis

CSV Analysis with csvkit

# Install csvkit
pip install csvkit

# View CSV structure
csvstat data.csv

# Query CSV with SQL
csvsql --query "SELECT category, SUM(value) FROM data GROUP BY category" data.csv

# Convert formats
in2csv data.xlsx > data.csv
csvjson data.csv > data.json

# Filter rows
csvgrep -c category -m "Electronics" data.csv

Quick Stats with awk

# Sum column 3
awk -F',' '{sum+=$3} END {print "Sum:", sum}' data.csv

# Average
awk -F',' '{sum+=$3; count++} END {print "Avg:", sum/count}' data.csv

# Count unique values
awk -F',' '{a[$2]++} END {for(k in a) print k, a[k]}' data.csv

Database Integration

SQLite Analysis

# Query SQLite database
sqlite3 database.db "SELECT * FROM table LIMIT 10"

# Export to CSV
sqlite3 -header -csv database.db "SELECT * FROM table" > export.csv

# Run SQL file
sqlite3 database.db < queries.sql

Python SQLite

import sqlite3
import pandas as pd

conn = sqlite3.connect('database.db')
df = pd.read_sql_query("SELECT * FROM table WHERE condition", conn)

Tools Required

python3 with pandas, numpy, matplotlib, seaborn
csvkit - CSV utilities
sqlite3 - SQLite database
jq - JSON processing

Installation

pip install pandas numpy matplotlib seaborn openpyxl xlrd csvkit reportlab
sudo apt install -y sqlite3 jq

Related Skills

balaraj74/weather

development

VerifiedTrustedCommunity

Get current weather and forecasts (no API key required).

SKILL.mdUpdated Apr 3, 2026

balaraj74/wacli

tools

VerifiedTrustedCommunity

Send WhatsApp messages to other people or search/sync WhatsApp history via the wacli CLI (not for normal user chats).

SKILL.mdUpdated Apr 3, 2026

balaraj74/vscode

tools

VerifiedTrustedCommunity

Control VS Code from the terminal. Open files, run commands, manage extensions, use the integrated terminal, interact with Copilot, and automate editor tasks. Use when a user asks FRIDAY to work with VS Code, edit files, or run VS Code commands.

SKILL.mdUpdated Apr 3, 2026

balaraj74/voice-call

tools

VerifiedTrustedCommunity

Start voice calls via the FRIDAY voice-call plugin.

SKILL.mdUpdated Apr 3, 2026

Download

For Claude Desktop. Download once, then upload the file in the app — no terminal needed.

Need help? View full Cowork setup guide →

Install manually

Choose your platform

# Clone the repo
git clone https://github.com/balaraj74/f.r.i.d.a.y.git

# Copy into Claude Code skills folder (global)
cp -r f.r.i.d.a.y/skills/data-analysis ~/.claude/skills/

Claude Code Skills — official skills path docs.

Repository

balaraj74/f.r.i.d.a.y

Compatible with

Claude Code

OpenAI Codex CLI

ChatGPT