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SKILL verified Apache-2.0 Self-run

Social Media Analytics

skill-donvito-skillsbento-social-media-analyzer · by donvito

Analyzes social media performance data (Twitter/X, Instagram, LinkedIn, TikTok, etc.) to extract insights, identify patterns, and generate actionable recommendations. Use when the user uploads CSV/Excel files containing social metrics (impressions, engagements, followers, likes, shares, etc.) and asks for analysis, trends, performance review, content strategy advice, or data visualization.

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Install

$ agentstack add skill-donvito-skillsbento-social-media-analyzer

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
5mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

Social Media Analytics

Analyze social media data to extract insights and generate strategic recommendations.

Analysis Framework

1. Data Ingestion & Validation

Read the uploaded file and identify available metrics. Common columns:

| Category | Metrics | |----------|---------| | Reach | Impressions, Reach, Views, Profile visits | | Engagement | Likes, Comments, Replies, Shares, Reposts, Bookmarks, Saves | | Growth | New followers, Unfollows, Net followers | | Content | Posts created, Video views, Media views |

Validate data completeness. Note any missing or zero-value columns.

2. Calculate Key Performance Indicators

Engagement Rate = (Total Engagements / Total Impressions) × 100
Follow Conversion = (New Followers / Profile Visits) × 100
Net Growth = New Followers - Unfollows
Likes per Post = Total Likes / Posts Created
Impressions per Post = Total Impressions / Posts Created

3. Temporal Analysis

Identify patterns across time periods:

  • Best performing days: Highest impressions, engagement rate, follower growth
  • Worst performing days: Lowest metrics, potential issues
  • Posting frequency correlation: Compare posts/day vs engagement/post
  • Viral content detection: Days with 2x+ average performance

4. Engagement Composition Analysis

Break down total engagements by type:

  • Likes (passive appreciation)
  • Bookmarks/Saves (high-intent, reference value)
  • Replies/Comments (active conversation)
  • Shares/Reposts (amplification)

High bookmark rates suggest educational/reference content resonates. High reply rates indicate conversation-driving content.

5. Quality vs Quantity Assessment

Analyze the relationship between posting volume and performance:

for each day:
    likes_per_post = likes / posts
    impressions_per_post = impressions / posts

# Compare high-volume vs low-volume days
# Often: fewer high-quality posts > many low-quality posts

6. Growth Funnel Analysis

Track the conversion funnel:

Impressions → Engagements → Profile Visits → New Followers

Calculate conversion rates at each stage. Identify bottlenecks.

Visualization Guidelines

Create an interactive React dashboard with tabs:

  1. Overview: Key metrics cards, impressions/engagement trend, engagement breakdown pie chart
  2. Engagement: Daily engagement rate bar chart, likes per post analysis
  3. Growth: Follower gains/losses, profile visit to follower conversion
  4. Insights: Key findings cards with actionable recommendations

Use Recharts for charts:

  • ComposedChart for dual-axis (impressions + engagements)
  • BarChart for daily comparisons
  • PieChart for engagement breakdown

Recommendation Categories

Posting Schedule Optimization

  • Identify best/worst performing days
  • Suggest optimal posting frequency based on quality vs quantity analysis
  • Recommend content scheduling strategy

Content Strategy

  • Analyze which content types drive bookmarks (save-worthy)
  • Identify viral content patterns
  • Suggest content pillars based on engagement composition

Growth Tactics

  • Profile optimization suggestions based on follow conversion rate
  • Engagement strategy (when to be active in replies)
  • Audience retention insights from unfollow patterns

Output Structure

  1. Summary metrics: Total impressions, engagements, engagement rate, net followers
  2. Key findings: 3-5 bullet points of critical insights
  3. Interactive dashboard: React component with tabbed views
  4. Strategic recommendations: Prioritized action items with rationale

Example Insights

  • "Monday drove 24% of weekly impressions—analyze what made it viral"
  • "Tuesday underperformed despite 13 posts—consider reducing output"
  • "30 posts yielded 99 likes/post vs 7 posts with 309 likes/post—quality over quantity"
  • "5,312 bookmarks (6.8% of engagements) suggests save-worthy content resonates"
  • "10.3% of profile visitors convert to followers—optimize bio for higher conversion"

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.