AGENT SKILLBY ALIREZAREZVANI
Social media analyzer
AT A GLANCE
Social media analyzer is a social media skill for AI agents. Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks. The alirezarezvani/claude-skills repository on GitHub, maintained by alirezarezvani, has 19.6K stars and the MIT license. To add it, run npx skills add alirezarezvani/claude-skills --skill social-media-analyzer
- SOURCE REPOSITORY
- alirezarezvani/claude-skills (opens GitHub in a new tab)
- REPO STARS
- SOURCE REPOSITORY
- LICENSE
- MIT
- CATEGORIES
- SKILL.MD SIZE
- 7.7 KB294 LINES
QUICK ANSWER
- WHAT IT IS01
- Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks. The value is a focused slice of social content judgment, useful when several similar skills cover the same ground.
- INPUTS02
- Context for social content: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result for social content: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add alirezarezvani/claude-skills --skill social-media-analyzerREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 29
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 × 100Metric 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 |
|---|---|---|---|
| 1.22% | 3-6% | >6% | |
| 0.07% | 0.5-1% | >1% | |
| Twitter/X | 0.05% | 0.1-0.5% | >0.5% |
| 2.0% | 3-5% | >5% | |
| TikTok | 5.96% | 8-15% | >15% |
CTR by Platform#
| Platform | Average | Good | Excellent |
|---|---|---|---|
| 0.22% | 0.5-1% | >1% | |
| 0.90% | 1.5-2.5% | >2.5% | |
| 0.44% | 1-2% | >2% | |
| TikTok | 0.30% | 0.5-1% | >1% |
CPC by Platform#
| Platform | Average | Good |
|---|---|---|
| $0.97 | <$0.50 | |
| $1.20 | <$0.70 | |
| $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.jsonCalculates engagement rate, CTR, reach rate for each post and campaign totals.
Analyze Performance#
python scripts/analyze_performance.py assets/sample_input.jsonGenerates 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.
FAQ
07 QUESTIONS
01What is the Social media analyzer agent skill?
Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks. It is listed under social media in the 100xLab agent skills directory.
02How do I install Social media analyzer in Cursor, Claude Code, or Codex?
Run npx skills add alirezarezvani/claude-skills --skill social-media-analyzer in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only Social media analyzer, not every skill in the repository.
03What does the Social media analyzer SKILL.md cover?
The SKILL.md includes sections on Table of Contents, Analysis Workflow, Input Requirements, Data Validation Checks, Engagement Metrics and Engagement Rate Calculation. Read the full text on this page or in plain markdown at /resources/agent-skills/alirezarezvani-claude-skills--social-media-analyzer--1odtkhf/raw.
04Which agents can use Social media analyzer?
Social media analyzer installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is Social media analyzer under?
The alirezarezvani/claude-skills repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is Social media analyzer free to use?
Reading, copying and installing Social media analyzer costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of Social media analyzer?
Social media analyzer comes from https://github.com/alirezarezvani/claude-skills, which has 19.6K GitHub stars. The SKILL.md is at https://github.com/alirezarezvani/claude-skills/blob/HEAD/marketing-skill/skills/social-media-analyzer/SKILL.md. The author is alirezarezvani.
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ATTRIBUTION
Social media analyzer was written by its original authors and is published in alirezarezvani/claude-skills (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.