mastodon-analyze

name	mastodon-analyze
description	Analyze Mastodon engagement patterns and content performance. Use when you need insights on posting effectiveness, content strategy, or audience engagement.

Mastodon Analyze Skill

Use this skill when you need to analyze engagement patterns, content performance, and posting effectiveness from the RaspiMIDIHub Mastodon account.

Usage

This skill fetches posts and provides analysis on:

• Engagement metrics (likes, reblogs, replies)
• Content type distribution
• Posting frequency patterns
• Top performing posts
• Content category breakdown
• Media impact analysis
• Time-based patterns

Command

python -m announce --fetch-mastodon N --analyze

Where N is the number of posts to analyze (recommended: 50-100 for meaningful patterns).

Analysis Categories

1. Engagement Metrics

• Total engagement: Sum of likes + reblogs + replies
• Average engagement per post
• Top 10 most engaging posts
• Engagement rate by content type

2. Content Type Distribution

Auto-categorized into:

• Features: Version announcements, changelog items
• Jokes: MIDI-themed humor
• Facts: "Did you know?" MIDI trivia
• Creative Uses: How-to guides and use cases
• History: MIDI history and background
• Quick Tips: Short practical advice
• Behind the Code: Developer stories

3. Posting Patterns

• Time of day analysis: When do posts perform best?
• Day of week analysis: Which days get more engagement?
• Frequency: Posts per day/hour

4. Content Performance

• Best performing source: Which content type gets most engagement?
• Worst performing source: Which content type needs improvement?
• Media impact: Do posts with images perform better?

5. Variety Analysis

• Content diversity: Number of different content categories
• Repetition detection: Identifies similar content that may cause audience fatigue
• Balance score: Assesses if content mix is well-distributed

Output Format

======================================================================
MASTODON POST ANALYSIS
======================================================================

Total Posts Analyzed: 20
Date Range: 2026-07-12 to 2026-07-12

ENGAGEMENT SUMMARY
----------------------------------------------------------------------
Total Engagement: 6
  - Likes: 5
  - Reblogs: 1
  - Replies: 0
Average per Post: 0.30

TOP 10 POSTS BY ENGAGEMENT
----------------------------------------------------------------------
1. [2026-07-12 09:13] 2 engagement - Build a MIDI-controlled 3D printer...
2. [2026-07-12 09:13] 1 engagement - Fix MIDI clock drift by designating...
...

CONTENT BREAKDOWN
----------------------------------------------------------------------
facts: 4 posts (20.0%) - Avg engagement: 0.00
jokes: 2 posts (10.0%) - Avg engagement: 0.00
features: 8 posts (40.0%) - Avg engagement: 0.25
creative_uses: 2 posts (10.0%) - Avg engagement: 1.00
quick_tips: 1 posts (5.0%) - Avg engagement: 1.00
other: 2 posts (10.0%) - Avg engagement: 0.50
history: 1 posts (5.0%) - Avg engagement: 0.00

MEDIA ANALYSIS
----------------------------------------------------------------------
Posts with media: 5 (25.0%)
  - Avg engagement: 0.00
Posts without media: 15 (75.0%)
  - Avg engagement: 0.40
Media posts perform 0.0x better

TIME ANALYSIS
----------------------------------------------------------------------
Best posting hour: 09:00 (avg 0.30 engagement)
Best posting day: Sunday (avg 0.30 engagement)

INSIGHTS & RECOMMENDATIONS
----------------------------------------------------------------------
• Best performing content: creative_uses (avg 1.00 engagement)
• Lowest performing content: facts (avg 0.00 engagement)
• ✓ Good content variety (7 categories)
• ⚠️  Low average engagement (0.30). Consider adjusting content strategy.

Implementation

The analysis functionality is implemented in:

• announce/mastodon_analyzer.py - analyze_posts() function
• announce/__main__.py - CLI argument --analyze

Key Features

# Auto-categorization
def _categorize_post(text: str) -> str:
    """Categorize a post by its content type."""
    # Detects: features, jokes, facts, creative_uses, history, quick_tips, behind_the_code

# Full analysis
def analyze_posts(statuses: list) -> None:
    """Analyze engagement patterns and content performance."""
    # - Categorize posts by source type
    # - Calculate engagement metrics
    # - Identify top performers
    # - Analyze media impact
    # - Time-based patterns
    # - Generate insights and recommendations

Recommendations

Based on analysis, the skill provides actionable recommendations:

1. Content Mix: Adjust posting frequency by content type based on engagement
2. Timing: Optimize posting schedule based on hour/day analysis
3. Media Strategy: Increase media attachments for high-performing categories
4. Engagement Boosters: Identify what resonates with your audience
5. Variety Check: Warns if content diversity is low (prevents audience fatigue)
6. Quality Alerts: Flags low engagement for strategy adjustment

Related

• Use mastodon-fetch skill to simply fetch and view posts
• Use python -m announce --help to see all CLI options