skills$openclaw/social-media-analyzer
alirezarezvani8.9k

by alirezarezvani

social-media-analyzer – OpenClaw Skill

social-media-analyzer is an OpenClaw Skills integration for data analytics workflows. 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.

8.9k stars4.5k forksSecurity L1
Updated Feb 7, 2026Created Feb 7, 2026data analytics

Skill Snapshot

namesocial-media-analyzer
descriptionSocial 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. OpenClaw Skills integration.
owneralirezarezvani
repositoryalirezarezvani/social-media-analyzer
languageMarkdown
licenseMIT
topics
securityL1
installopenclaw add @alirezarezvani/social-media-analyzer
last updatedFeb 7, 2026

Maintainer

alirezarezvani

alirezarezvani

Maintains social-media-analyzer in the OpenClaw Skills directory.

View GitHub profile
File Explorer
11 files
.
assets
expected_output.json
1.5 KB
sample_input.json
867 B
references
platform-benchmarks.md
5.9 KB
scripts
analyze_performance.py
7.2 KB
calculate_metrics.py
5.4 KB
_meta.json
302 B
HOW_TO_USE.md
1.8 KB
SKILL.md
7.0 KB
SKILL.md

name: social-media-analyzer description: 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. triggers:

  • analyze social media
  • calculate engagement rate
  • social media ROI
  • campaign performance
  • compare platforms
  • benchmark engagement
  • Instagram analytics
  • Facebook metrics
  • TikTok performance
  • LinkedIn engagement

Social Media Analyzer

Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.


Table of Contents


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

FieldRequiredDescription
platformYesinstagram, facebook, twitter, linkedin, tiktok
posts[]YesArray of post data
posts[].likesYesLike/reaction count
posts[].commentsYesComment count
posts[].reachYesUnique users reached
posts[].impressionsNoTotal views
posts[].sharesNoShare/retweet count
posts[].savesNoSave/bookmark count
posts[].clicksNoLink clicks
total_spendNoAd 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

MetricFormulaInterpretation
Engagement RateEngagements / Reach × 100Audience interaction level
CTRClicks / Impressions × 100Content click appeal
Reach RateReach / Followers × 100Content distribution
Virality RateShares / Impressions × 100Share-worthiness
Save RateSaves / Reach × 100Content value

Performance Categories

RatingEngagement RateAction
Excellent> 6%Scale and replicate
Good3-6%Optimize and expand
Average1-3%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

MetricFormula
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

ActionValueRationale
Like$0.50Brand awareness
Comment$2.00Active engagement
Share$5.00Amplification
Save$3.00Intent signal
Click$1.50Traffic value

ROI Interpretation

ROI %RatingRecommendation
> 500%ExcellentScale budget significantly
200-500%GoodIncrease budget moderately
100-200%AcceptableOptimize before scaling
0-100%Break-evenReview targeting and creative
< 0%NegativePause and restructure

Platform Benchmarks

Engagement Rate by Platform

PlatformAverageGoodExcellent
Instagram1.22%3-6%>6%
Facebook0.07%0.5-1%>1%
Twitter/X0.05%0.1-0.5%>0.5%
LinkedIn2.0%3-5%>5%
TikTok5.96%8-15%>15%

CTR by Platform

PlatformAverageGoodExcellent
Instagram0.22%0.5-1%>1%
Facebook0.90%1.5-2.5%>2.5%
LinkedIn0.44%1-2%>2%
TikTok0.30%0.5-1%>1%

CPC by Platform

PlatformAverageGood
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
README.md

No README available.

Permissions & Security

Security level L1: Low-risk skills with minimal permissions. Review inputs and outputs before running in production.

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) |

FAQ

How do I install social-media-analyzer?

Run openclaw add @alirezarezvani/social-media-analyzer in your terminal. This installs social-media-analyzer into your OpenClaw Skills catalog.

Does this skill run locally or in the cloud?

OpenClaw Skills execute locally by default. Review the SKILL.md and permissions before running any skill.

Where can I verify the source code?

The source repository is available at https://github.com/openclaw/skills/tree/main/skills/alirezarezvani/social-media-analyzer. Review commits and README documentation before installing.