lead-qualification-meddic
Qualify enterprise opportunities using MEDDIC/MEDDPICC framework for complex B2B sales cycles
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
Full skill instructions
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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