artifacts/bundle/skills/marketing-skill/social-media-analyzer/SKILL.md
# Social Media Analyzer Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks. --- ## Table of Contents - [Analysis Workflow](#analysis-workflow) - [Engagement Metrics](#engagement-metrics) - [ROI Calculation](#roi-calculation) - [Platform Benchmarks](#platform-benchmarks) - [Tools](#tools) - [Examples](#examples) --- ## Analysis Workflow Analyze social media campaign performance: 1. Validate input data completeness (reach > 0, dates valid) 2. C
npx skillsauth add neekware/ehayeskills artifacts/bundle/skills/marketing-skill/social-media-analyzerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.
Analyze social media campaign performance:
| Field | Required | Description | | ------------------- | -------- | ---------------------------------------------- | | platform | Yes | instagram, facebook, twitter, linkedin, tiktok | | posts[] | Yes | Array of post data | | posts[].likes | Yes | Like/reaction count | | posts[].comments | Yes | Comment count | | posts[].reach | Yes | Unique users reached | | posts[].impressions | No | Total views | | posts[].shares | No | Share/retweet count | | posts[].saves | No | Save/bookmark count | | posts[].clicks | No | Link clicks | | total_spend | No | Ad spend (for ROI) |
Before analysis, verify:
Engagement Rate = (Likes + Comments + Shares + Saves) / Reach × 100
| Metric | Formula | Interpretation | | --------------- | -------------------------- | -------------------------- | | Engagement Rate | Engagements / Reach × 100 | Audience interaction level | | CTR | Clicks / Impressions × 100 | Content click appeal | | Reach Rate | Reach / Followers × 100 | Content distribution | | Virality Rate | Shares / Impressions × 100 | Share-worthiness | | Save Rate | Saves / Reach × 100 | Content value |
| Rating | Engagement Rate | Action | | --------- | --------------- | ------------------- | | Excellent | > 6% | Scale and replicate | | Good | 3-6% | Optimize and expand | | Average | 1-3% | Test improvements | | Poor | < 1% | Analyze and pivot |
Calculate return on ad spend:
| Metric | Formula | | ------------------------- | ------------------------------- | | Cost Per Engagement (CPE) | Total Spend / Total Engagements | | Cost Per Click (CPC) | Total Spend / Total Clicks | | Cost Per Thousand (CPM) | (Spend / Impressions) × 1000 | | Return on Ad Spend (ROAS) | Revenue / Ad Spend |
| Action | Value | Rationale | | ------- | ----- | ----------------- | | Like | $0.50 | Brand awareness | | Comment | $2.00 | Active engagement | | Share | $5.00 | Amplification | | Save | $3.00 | Intent signal | | Click | $1.50 | Traffic value |
| ROI % | Rating | Recommendation | | -------- | ---------- | ----------------------------- | | > 500% | Excellent | Scale budget significantly | | 200-500% | Good | Increase budget moderately | | 100-200% | Acceptable | Optimize before scaling | | 0-100% | Break-even | Review targeting and creative | | < 0% | Negative | Pause and restructure |
| Platform | Average | Good | Excellent | | --------- | ------- | -------- | --------- | | Instagram | 1.22% | 3-6% | >6% | | Facebook | 0.07% | 0.5-1% | >1% | | Twitter/X | 0.05% | 0.1-0.5% | >0.5% | | LinkedIn | 2.0% | 3-5% | >5% | | TikTok | 5.96% | 8-15% | >15% |
| Platform | Average | Good | Excellent | | --------- | ------- | -------- | --------- | | Instagram | 0.22% | 0.5-1% | >1% | | Facebook | 0.90% | 1.5-2.5% | >2.5% | | LinkedIn | 0.44% | 1-2% | >2% | | TikTok | 0.30% | 0.5-1% | >1% |
| Platform | Average | Good | | --------- | ------- | ------ | | Facebook | $0.97 | <$0.50 | | Instagram | $1.20 | <$0.70 | | LinkedIn | $5.26 | <$3.00 | | TikTok | $1.00 | <$0.50 |
See references/platform-benchmarks.md for complete benchmark data.
python scripts/calculate_metrics.py assets/sample_input.json
Calculates engagement rate, CTR, reach rate for each post and campaign totals.
python scripts/analyze_performance.py assets/sample_input.json
Generates full performance analysis with ROI, benchmarks, and recommendations.
Output includes:
See assets/sample_input.json:
{
"platform": "instagram",
"total_spend": 500,
"posts": [
{
"post_id": "post_001",
"content_type": "image",
"likes": 342,
"comments": 28,
"shares": 15,
"saves": 45,
"reach": 5200,
"impressions": 8500,
"clicks": 120
}
]
}
See assets/expected_output.json:
{
"campaign_metrics": {
"total_engagements": 1521,
"avg_engagement_rate": 8.36,
"ctr": 1.55
},
"roi_metrics": {
"total_spend": 500.0,
"cost_per_engagement": 0.33,
"roi_percentage": 660.5
},
"insights": {
"overall_health": "excellent",
"benchmark_comparison": {
"engagement_status": "excellent",
"engagement_benchmark": "1.22%",
"engagement_actual": "8.36%"
}
}
}
The sample campaign shows:
references/platform-benchmarks.md contains:
| When you ask for... | You get... | | ---------------------------- | ------------------------------------------------------ | | "Social media audit" | Performance analysis across platforms with benchmarks | | "What's performing?" | Top content analysis with patterns and recommendations | | "Competitor social analysis" | Competitive social media comparison with gaps |
All output passes quality verification:
Creator: Marketing Skill License: MIT Source Repo:
neekware/ehaye-skillsSource Bucket:marketing-skillOriginal Path:marketing-skill/social-media-analyzer
testing
/em -stress-test — Business Assumption Stress Testing
research
/em -postmortem — Honest Analysis of What Went Wrong
development
/em -hard-call — Framework for Decisions With No Good Options
research
/em -challenge — Pre-Mortem Plan Analysis