Social Analytics

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

majiayu000 266bc40 2 files · 6.4 KB Updated Sep 9, 2026 567 repo stars

Marketing & Growth

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License: MIT
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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

---name: social-analyticsdescription: Analyze social media profiles and engagement. Use when: auditing competitor social presence; calculating engagement rates; identifying top-performing content; tracking profile growth; benchmarking social metricslicense: MIT--- # 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 ```bash pip install click pandas requests beautifulsoup4 # For authenticated API access: pip install tweepy instaloader ``` ## Commands ### Analyze Profile ```bash python scripts/main.py analyze @competitor --platform twitter python scripts/main.py analyze @brand --platform instagram ``` ### Calculate Engagement ```bash python scripts/main.py engagement @profile --platform twitter --days 30 python scripts/main.py engagement @profile --platform linkedin --posts 50 ``` ### Find Top Posts ```bash python scripts/main.py top-posts @profile --platform twitter --count 10 python scripts/main.py top-posts @profile --metric likes ``` ### Export Data ```bash python scripts/main.py export @profile --platform twitter --format csv python scripts/main.py export @profile --platform instagram --output report.json ``` ### Compare Profiles ```bash python scripts/main.py compare @brand1 @brand2 @brand3 --platform twitter ``` ## Examples ### Example 1: Competitor Social Audit ```bash # 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 ```bash # 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 ```bash # 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](../../automation/content-repurposer/) - Repurpose top-performing content - [hashtag-analyzer](../hashtag-analyzer/) - Deep dive into hashtag performance ## Skill Metadata - **Mode**: centaur ```yaml category: social subcategory: analytics dependencies: [pandas, requests, beautifulsoup4] difficulty: intermediate time_saved: 4+ hours/week ```

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