Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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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
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Analyze social media profiles and calculate engagement metrics - understand what content works for competitors and your own accounts.
| 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 |
pip install click pandas requests beautifulsoup4
# For authenticated API access:
pip install tweepy instaloader
python scripts/main.py analyze @competitor --platform twitter
python scripts/main.py analyze @brand --platform instagram
python scripts/main.py engagement @profile --platform twitter --days 30
python scripts/main.py engagement @profile --platform linkedin --posts 50
python scripts/main.py top-posts @profile --platform twitter --count 10
python scripts/main.py top-posts @profile --metric likes
python scripts/main.py export @profile --platform twitter --format csv
python scripts/main.py export @profile --platform instagram --output report.json
python scripts/main.py compare @brand1 @brand2 @brand3 --platform twitter
# 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
# 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)
# 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
| 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% |
| 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 |
| Format | Best For |
|---|---|
text | Quick terminal review |
csv | Spreadsheet analysis |
json | Programmatic use |
md | Reports and docs |
category: social
subcategory: analytics
dependencies: [pandas, requests, beautifulsoup4]
difficulty: intermediate
time_saved: 4+ hours/week
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
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Assess customer account health using product usage, support sentiment, payment status, and relationship signals
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Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
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Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
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Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
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