social-analytics

name	social-analytics
description	Analyze social media profiles and engagement. Use when: auditing competitor social presence; calculating engagement rates; identifying top-performing content; tracking profile growth; benchmarking social metrics
license	MIT
metadata	{"author":"ClawFu","version":"1.0.0","mcp-server":"@clawfu/mcp-skills"}

Social Analytics

Analyze social media profiles and calculate engagement metrics - understand what content works for competitors and your own accounts.

When to Use This Skill

• Competitor analysis - Audit competitor social presence
• Engagement benchmarking - Calculate and compare engagement rates
• Content analysis - Identify top-performing post types
• Profile audit - Assess social media health
• Reporting - Generate social performance reports

What Claude Does vs What You Decide

Claude Does	You Decide
Structures analysis frameworks	Metric definitions
Identifies patterns in data	Business interpretation
Creates visualization templates	Dashboard design
Suggests optimization areas	Action priorities
Calculates statistical measures	Decision thresholds

Dependencies

pip install click pandas requests beautifulsoup4
# For authenticated API access:
pip install tweepy instaloader

Commands

Analyze Profile

python scripts/main.py analyze @competitor --platform twitter
python scripts/main.py analyze @brand --platform instagram

Calculate Engagement

python scripts/main.py engagement @profile --platform twitter --days 30
python scripts/main.py engagement @profile --platform linkedin --posts 50

Find Top Posts

python scripts/main.py top-posts @profile --platform twitter --count 10
python scripts/main.py top-posts @profile --metric likes

Export Data

python scripts/main.py export @profile --platform twitter --format csv
python scripts/main.py export @profile --platform instagram --output report.json

Compare Profiles

python scripts/main.py compare @brand1 @brand2 @brand3 --platform twitter

Examples

Example 1: Competitor Social Audit

# Analyze competitor profile
python scripts/main.py analyze @competitor_brand --platform twitter

# Output:
# Profile Analysis: @competitor_brand
# ─────────────────────────────────────
# Followers:      45,230
# Following:      1,234
# Total Posts:    2,456
# Avg Likes:      234
# Avg Retweets:   45
# Engagement:     2.3%
# Post Frequency: 3.2/day
# Top Hashtags:   #marketing, #growth, #startup

Example 2: Benchmark Engagement Rates

# Compare engagement across competitors
python scripts/main.py compare @brand1 @brand2 @brand3 --platform twitter

# Output:
# Engagement Comparison
# ─────────────────────
# Profile         Followers   Eng.Rate   Posts/Day
# @brand1         45,230      2.3%       3.2
# @brand2         32,100      3.1%       2.1
# @brand3         89,500      1.8%       4.5

# Winner: @brand2 (highest engagement despite fewer followers)

Example 3: Find Winning Content

# Identify top performing posts
python scripts/main.py top-posts @marketing_pro --platform twitter --count 10

# Output:
# Top 10 Posts by Engagement
# ──────────────────────────
# 1. "Here's what nobody tells you about..."
#    Likes: 2,345  RTs: 456  Eng: 6.2%
#    Type: Thread  Time: Tuesday 9am

# 2. "The biggest mistake I see founders make..."
#    Likes: 1,890  RTs: 312  Eng: 4.8%
#    Type: Single  Time: Wednesday 8am

Engagement Rate Benchmarks

Twitter/X

Account Size	Good	Great	Excellent
<10K	1-3%	3-6%	>6%
10K-100K	0.5-1%	1-3%	>3%
100K+	0.2-0.5%	0.5-1%	>1%

Instagram

Account Size	Good	Great	Excellent
<10K	3-6%	6-10%	>10%
10K-100K	1-3%	3-6%	>6%
100K+	0.5-1%	1-3%	>3%

LinkedIn

Account Size	Good	Great	Excellent
Personal	2-4%	4-8%	>8%
Company	0.5-1%	1-2%	>2%

Metrics Explained

Metric	Formula	What It Measures
Engagement Rate	(likes + comments + shares) / followers	Overall content resonance
Amplification	shares / followers	Content virality
Conversation	comments / followers	Community engagement
Applause	likes / followers	Content appreciation

Output Formats

Format	Best For
text	Quick terminal review
csv	Spreadsheet analysis
json	Programmatic use
md	Reports and docs

Skill Boundaries

What This Skill Does Well

• Structuring data analysis
• Identifying patterns and trends
• Creating visualization frameworks
• Calculating statistical measures

What This Skill Cannot Do

• Access your actual data
• Replace statistical expertise
• Make business decisions
• Guarantee prediction accuracy

Related Skills

• content-repurposer - Repurpose top-performing content
• hashtag-analyzer - Deep dive into hashtag performance

Skill Metadata

• Mode: centaur

category: social
subcategory: analytics
dependencies: [pandas, requests, beautifulsoup4]
difficulty: intermediate
time_saved: 4+ hours/week