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.
Best use case
social-media-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using social-media-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/social-media-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How social-media-analyzer Compares
| Feature / Agent | social-media-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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. Calculate engagement metrics per post
3. Aggregate campaign-level metrics
4. Calculate ROI if ad spend provided
5. Compare against platform benchmarks
6. Identify top and bottom performers
7. Generate recommendations
8. **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:
1. Sum total engagements across posts
2. Calculate cost per engagement (CPE)
3. Calculate cost per click (CPC) if clicks available
4. Estimate engagement value using benchmark rates
5. Calculate ROI percentage
6. **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
```bash
python scripts/calculate_metrics.py assets/sample_input.json
```
Calculates engagement rate, CTR, reach rate for each post and campaign totals.
### Analyze Performance
```bash
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`:
```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`:
```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.Related Skills
social-media-manager
When the user wants to develop social media strategy, plan content calendars, manage community engagement, or grow their social presence across platforms. Also use when the user mentions 'social media strategy,' 'social calendar,' 'community management,' 'social media plan,' 'grow followers,' 'engagement rate,' 'social media audit,' or 'which platforms should I use.' For writing individual social posts, see social-content. For analyzing social performance data, see social-media-analyzer.
social-content
When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms. Also use when the user mentions 'LinkedIn post,' 'Twitter thread,' 'social media,' 'content calendar,' 'social scheduling,' 'engagement,' or 'viral content.' This skill covers content creation, repurposing, and platform-specific strategies.
meeting-analyzer
Analyzes meeting transcripts and recordings to surface behavioral patterns, communication anti-patterns, and actionable coaching feedback. Use this skill whenever the user uploads or points to meeting transcripts (.txt, .md, .vtt, .srt, .docx), asks about their communication habits, wants feedback on how they run meetings, requests speaking ratio analysis, mentions filler words or conflict avoidance, or wants to compare their communication across time periods. Also trigger when users mention tools like Granola, Otter, Fireflies, or Zoom transcripts. Even if the user just says "look at my meetings" or "how do I come across in meetings" — use this skill.
wiki-query
Query the LLM Wiki — reads index.md first, drills into 3-10 relevant pages, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back as a new comparison or synthesis page. Usage /wiki-query "<question>"
wiki-log
Show recent entries from the LLM Wiki log (wiki/log.md). Uses the standardized
wiki-lint
Run a health check on the LLM Wiki vault — mechanical checks (orphans, broken links, stale pages, missing frontmatter, log gap, duplicates) plus semantic checks (contradictions, cross-reference gaps, concepts missing their own page). Outputs a markdown report with suggested actions. Usage /wiki-lint [--stale-days N] [--log-gap-days N]
wiki-init
Bootstrap a fresh LLM Wiki vault with the three-layer structure, schema files, and starter templates. Usage /wiki-init <path> --topic "<topic>" [--tool all|claude-code|codex|cursor|antigravity]
wiki-ingest
Ingest a source file from raw/ into the LLM Wiki — read, discuss, write summary page, update cross-references across 5-15 pages, regenerate index, append to log. Usage /wiki-ingest <path-to-source>
tc
Track technical changes with structured records, a state machine, and session handoff. Usage: /tc <init|create|update|status|resume|close|export|dashboard> [args]
tc-tracker
Use when the user asks to track technical changes, create change records, manage TC lifecycles, or hand off work between AI sessions. Covers init/create/update/status/resume/close/export workflows for structured code change documentation.
llm-wiki
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
karpathy-coder
Use when writing, reviewing, or committing code to enforce Karpathy's 4 coding principles — surface assumptions before coding, keep it simple, make surgical changes, define verifiable goals. Triggers on "review my diff", "check complexity", "am I overcomplicating this", "karpathy check", "before I commit", or any code quality concern where the LLM might be overcoding.