Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
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.
skill openclawclawhub Free
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.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- HOW_TO_USE.md, SKILL.md, assets/expected_output.json, assets/sample_input.json, references/platform-benchmarks.md, scripts/analyze_performance.py
Validation
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- Review SKILL.md after the package is downloaded.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.1.1
Provenance
- Publisher
- alirezarezvani
- Source page
- View original listing
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Documentation
Social Media Analyzer
Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.
Table of Contents
Analysis Workflow Engagement Metrics ROI Calculation Platform Benchmarks Tools Examples
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
FieldRequiredDescriptionplatformYesinstagram, facebook, twitter, linkedin, tiktokposts[]YesArray of post dataposts[].likesYesLike/reaction countposts[].commentsYesComment countposts[].reachYesUnique users reachedposts[].impressionsNoTotal viewsposts[].sharesNoShare/retweet countposts[].savesNoSave/bookmark countposts[].clicksNoLink clickstotal_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 Rate Calculation
Engagement Rate = (Likes + Comments + Shares + Saves) / Reach × 100
Metric Definitions
MetricFormulaInterpretationEngagement RateEngagements / Reach × 100Audience interaction levelCTRClicks / Impressions × 100Content click appealReach RateReach / Followers × 100Content distributionVirality RateShares / Impressions × 100Share-worthinessSave RateSaves / Reach × 100Content value
Performance Categories
RatingEngagement RateActionExcellent> 6%Scale and replicateGood3-6%Optimize and expandAverage1-3%Test improvementsPoor< 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
MetricFormulaCost Per Engagement (CPE)Total Spend / Total EngagementsCost Per Click (CPC)Total Spend / Total ClicksCost Per Thousand (CPM)(Spend / Impressions) × 1000Return on Ad Spend (ROAS)Revenue / Ad Spend
Engagement Value Estimates
ActionValueRationaleLike$0.50Brand awarenessComment$2.00Active engagementShare$5.00AmplificationSave$3.00Intent signalClick$1.50Traffic value
ROI Interpretation
ROI %RatingRecommendation> 500%ExcellentScale budget significantly200-500%GoodIncrease budget moderately100-200%AcceptableOptimize before scaling0-100%Break-evenReview targeting and creative< 0%NegativePause and restructure
Engagement Rate by Platform
PlatformAverageGoodExcellentInstagram1.22%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
PlatformAverageGoodExcellentInstagram0.22%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
PlatformAverageGoodFacebook$0.97<$0.50Instagram$1.20<$0.70LinkedIn$5.26<$3.00TikTok$1.00<$0.50 See references/platform-benchmarks.md for complete benchmark data.
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
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
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.
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs2 Config1 Scripts
- SKILL.md
- HOW_TO_USE.md
- references/platform-benchmarks.md
- scripts/analyze_performance.py
- assets/expected_output.json
- assets/sample_input.json