social-analytics
About
This skill analyzes social media profiles to calculate engagement rates, identify top-performing content, and track growth. It's used for competitor analysis, benchmarking metrics, and generating performance reports. Developers can integrate it via the MCP server to audit social presence and assess account health.
Quick Install
Claude Code
Recommendednpx skills add guia-matthieu/clawfu-skills -a claude-code/plugin add https://github.com/guia-matthieu/clawfu-skillsgit clone https://github.com/guia-matthieu/clawfu-skills.git ~/.claude/skills/social-analyticsCopy and paste this command in Claude Code to install this skill
Documentation
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% |
| 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% |
| 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
GitHub Repository
Related Skills
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
cost-optimization
OtherThis Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
quantizing-models-bitsandbytes
OtherThis skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.
dispatching-parallel-agents
OtherThis Claude Skill dispatches multiple agents to investigate and fix 3+ independent problems concurrently. It is designed for scenarios involving unrelated failures that can be resolved without shared state or dependencies. The core capability is parallel problem-solving, assigning one agent per independent problem domain to maximize efficiency.
