Social Media Analyzer
Social media campaign analysis and performance tracking. Calculates engagement rates, ROI, and benchmarks across platforms. Use for analyzing social media performance, calculating engagement rate, measuring campaign ROI, comparing platform metrics, or benchmarking against industry standards.
$ npx promptcreek add social-media-analyzerAuto-detects your installed agents and installs the skill to each one.
What This Skill Does
This skill analyzes social media campaign performance by calculating engagement metrics, ROI, and comparing results against platform benchmarks. It's designed for social media managers and marketers who need to understand campaign effectiveness and optimize their strategies based on data-driven insights.
When to Use
- Analyze engagement rate of social media posts.
- Calculate ROI of social media campaigns.
- Compare campaign performance across platforms.
- Identify top and bottom performing content.
- Validate social media data completeness.
- Generate recommendations for campaign improvement.
Key Features
Installation
$ npx promptcreek add social-media-analyzerAuto-detects your installed agents (Claude Code, Cursor, Codex, etc.) and installs the skill to each one.
View Full Skill Content
Social Media Analyzer
Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.
Table of Contents
Analysis Workflow
Analyze social media campaign performance:
- Validate input data completeness (reach > 0, dates valid)
- Calculate engagement metrics per post
- Aggregate campaign-level metrics
- Calculate ROI if ad spend provided
- Compare against platform benchmarks
- Identify top and bottom performers
- Generate recommendations
- Validation: Engagement rate < 100%, ROI matches spend data
Input Requirements
| 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) |
Data Validation Checks
Before analysis, verify:
- [ ] Reach > 0 for all posts (avoid division by zero)
- [ ] Engagement counts are non-negative
- [ ] Date range is valid (start < end)
- [ ] Platform is recognized
- [ ] Spend > 0 if ROI requested
Engagement Metrics
Engagement Rate Calculation
Engagement Rate = (Likes + Comments + Shares + Saves) / Reach × 100
Metric Definitions
| 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 |
Performance Categories
| 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 |
ROI Calculation
Calculate return on ad spend:
- Sum total engagements across posts
- Calculate cost per engagement (CPE)
- Calculate cost per click (CPC) if clicks available
- Estimate engagement value using benchmark rates
- Calculate ROI percentage
- Validation: ROI = (Value - Spend) / Spend × 100
ROI Formulas
| 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 |
Engagement Value Estimates
| 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 Interpretation
| 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 Benchmarks
Engagement Rate by Platform
| 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% |
CTR by Platform
| 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% |
CPC by Platform
| 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.
Tools
Calculate Metrics
python scripts/calculate_metrics.py assets/sample_input.json
Calculates engagement rate, CTR, reach rate for each post and campaign totals.
Analyze Performance
python scripts/analyze_performance.py assets/sample_input.json
Generates full performance analysis with ROI, benchmarks, and recommendations.
Output includes:
- Campaign-level metrics
- Post-by-post breakdown
- Benchmark comparisons
- Top performers ranked
- Actionable recommendations
Examples
Sample Input
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
}
]
}
Sample Output
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%"
}
}
}
Interpretation
The sample campaign shows:
- Engagement rate 8.36% vs 1.22% benchmark = Excellent (6.8x above average)
- CTR 1.55% vs 0.22% benchmark = Excellent (7x above average)
- ROI 660% = Outstanding return on $500 spend
- Recommendation: Scale budget, replicate successful elements
Reference Documentation
Platform Benchmarks
references/platform-benchmarks.md contains:
- Engagement rate benchmarks by platform and industry
- CTR benchmarks for organic and paid content
- Cost benchmarks (CPC, CPM, CPE)
- Content type performance by platform
- Optimal posting times and frequency
- ROI calculation formulas
Proactive Triggers
- Engagement rate below platform average → Content isn't resonating. Analyze top performers for patterns.
- Follower growth stalled → Content distribution or frequency issue. Audit posting patterns.
- High impressions, low engagement → Reach without resonance. Content quality issue.
- Competitor outperforming significantly → Content gap. Analyze their successful posts.
Output Artifacts
| 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 |
Communication
All output passes quality verification:
- Self-verify: source attribution, assumption audit, confidence scoring
- Output format: Bottom Line → What (with confidence) → Why → How to Act
- Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.
Related Skills
- social-content: For creating social posts. Use this skill for analyzing performance.
- campaign-analytics: For cross-channel analytics including social.
- content-strategy: For planning social content themes.
- marketing-context: Provides audience context for better analysis.
Supported Agents
Attribution
Details
- License
- MIT
- Source
- seeded
- Published
- 3/17/2026
Tags
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