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Get Started Free →Social media campaign analysis and performance tracking. Calculates engagement rates, ROI, and benchmarks across platforms. Use when analyzing social media performance, calculating engagement rate, measuring campaign ROI, comparing platform metrics, or benchmarking against industry standards. Also use when the user mentions "social media audit," "engagement rate," or "which platform performs best."
.claude/skills/alirezarezvani-social-media-analyzer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 71% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 81% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 55% | 0% |
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.
bashpython scripts/calculate_metrics.py assets/sample_input.json
Calculates engagement rate, CTR, reach rate for each post and campaign totals.
bashpython scripts/analyze_performance.py assets/sample_input.json
Generates full performance analysis with ROI, benchmarks, and recommendations.
Output includes:
See assets/sample_input.json:
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:
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:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 16,297 | 15,103 | -7% | 1 | 1 | 0% | 3,863 | 5,915 | +53% | 0 | 0 | — |
case-02 | fail→pass | 17,343 | 17,050 | -2% | 1 | 1 | 0% | 3,839 | 6,548 | +71% | 0 | 0 | — |
case-03 | fail→pass | 15,398 | 15,627 | +1% | 1 | 1 | 0% | 3,654 | 6,118 | +67% | 0 | 0 | — |
case-04 | fail→fail | 9,736 | 8,685 | -11% | 1 | 1 | 0% | 2,032 | 4,042 | +99% | 0 | 0 | — |
case-05 | fail→pass | 12,248 | 9,589 | -22% | 1 | 1 | 0% | 2,394 | 4,326 | +81% | 0 | 0 | — |
case-06 | fail→pass | 15,035 | 9,115 | -39% | 1 | 1 | 0% | 2,699 | 4,178 | +55% | 0 | 0 | — |
case-07 | fail→fail | 14,556 | 10,003 | -31% | 1 | 1 | 0% | 2,893 | 4,404 | +52% | 0 | 0 | — |
case-08 | pass→pass | 4,004 | 6,323 | +58% | 1 | 1 | 0% | 775 | 3,446 | +345% | 0 | 0 | — |
case-09 | fail→pass | 5,728 | 7,255 | +27% | 1 | 1 | 0% | 1,256 | 3,828 | +205% | 0 | 0 | — |
case-10 | pass→pass | 3,454 | 3,795 | +10% | 1 | 1 | 0% | 704 | 2,961 | +321% | 0 | 0 | — |
case-11 | fail→pass | 14,446 | 11,626 | -20% | 1 | 1 | 0% | 2,554 | 4,494 | +76% | 0 | 0 | — |
case-12 | pass→pass | 2,904 | 4,608 | +59% | 1 | 1 | 0% | 545 | 3,218 | +490% | 0 | 0 | — |
case-13 | pass→pass | 12,310 | 8,606 | -30% | 1 | 1 | 0% | 2,080 | 3,800 | +83% | 0 | 0 | — |
case-14 | pass→pass | 14,296 | 8,883 | -38% | 1 | 1 | 0% | 2,359 | 3,816 | +62% | 0 | 0 | — |
case-15 | pass→pass | 7,424 | 6,515 | -12% | 1 | 1 | 0% | 1,294 | 3,468 | +168% | 0 | 0 | — |
case-16 | pass→pass | 11,374 | 4,950 | -56% | 1 | 1 | 0% | 1,995 | 3,213 | +61% | 0 | 0 | — |
case-17 | pass→pass | 4,354 | 5,116 | +18% | 1 | 1 | 0% | 1,048 | 3,334 | +218% | 0 | 0 | — |
case-18 | fail→pass | 4,786 | 5,075 | +6% | 1 | 1 | 0% | 905 | 3,115 | +244% | 0 | 0 | — |
case-19 | pass→pass | 13,073 | 10,422 | -20% | 1 | 1 | 0% | 1,933 | 3,856 | +99% | 0 | 0 | — |
case-20 | fail→fail | 9,895 | 10,357 | +5% | 1 | 1 | 0% | 1,534 | 3,826 | +149% | 0 | 0 | — |
case-21 | fail→fail | 26,305 | 31,693 | +20% | 1 | 1 | 0% | 5,113 | 7,067 | +38% | 0 | 0 | — |
case-22 | fail→fail | 14,875 | 12,006 | -19% | 1 | 1 | 0% | 2,616 | 4,582 | +75% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.