community-health-monitoring

name	community-health-monitoring
description	Audits follower quality, engagement authenticity, unfollower patterns, and network efficiency to produce a community health score. Use when monitoring account health or detecting bot/spam followers.
license	MIT
metadata	{"author":"nichxbt","version":"4.0"}

Community Health Monitoring

MCP-powered workflow for auditing follower quality, engagement health, and network efficiency. Produces a scored health report.

MCP Tools Used

Tool	Purpose
x_get_profile	Account-level stats
x_get_followers	Follower list for quality audit
x_get_following	Following list for reciprocity check
x_get_non_followers	Identify non-reciprocal follows
x_get_tweets	Engagement data for authenticity check
x_detect_unfollowers	Track recent unfollower patterns

Browser Scripts

Complement MCP analysis with browser-side tools:

Goal	Script
Audit follower quality	src/auditFollowers.js
Detect unfollowers	src/detectUnfollowers.js
Audience demographics	src/audienceDemographics.js
Follow ratio analysis	src/followRatioManager.js
Account health dashboard	src/accountHealthMonitor.js
Shadowban check	src/shadowbanChecker.js

Workflow

1. Profile baseline -- Call x_get_profile to get follower count, following count, and calculate follower-to-following ratio.
2. Audit follower quality -- Call x_get_followers with limit: 200. Classify each follower:
   • Active: Has bio, 50+ followers, posted in last 30 days
   • Low quality: No bio, <10 followers, or no recent activity
   • Suspect bot: Default avatar, username with many numbers, 0 tweets, follows 1000+
3. Check engagement authenticity -- Call x_get_tweets with limit: 30. For each tweet, compare engagement volume to follower count. Flag anomalies: likes/follower ratio > 10% (potential engagement pods) or < 0.1% (ghost followers).
4. Analyze unfollower patterns -- Call x_detect_unfollowers. Note churn rate and whether unfollowers correlate with specific content types or posting gaps.
5. Assess reciprocity -- Call x_get_non_followers. Calculate reciprocity rate: mutual_follows / total_following * 100. Identify high-value accounts not following back.
6. Calculate health score -- Weighted composite (0-100):
   • Follower quality: 30% (% active followers)
   • Engagement authenticity: 25% (normal engagement patterns)
   • Churn rate: 20% (low unfollower rate)
   • Reciprocity: 15% (healthy follower/following balance)
   • Growth trend: 10% (net positive follower change)
7. Generate report -- Compile into the template below with actionable recommendations.

Output Template

## Community Health Report: @{username}
Date: {date} | Health Score: {score}/100

### Score Breakdown
| Category | Score | Weight | Weighted |
|----------|-------|--------|----------|
| Follower Quality | {n}/100 | 30% | {n} |
| Engagement Authenticity | {n}/100 | 25% | {n} |
| Churn Rate | {n}/100 | 20% | {n} |
| Reciprocity | {n}/100 | 15% | {n} |
| Growth Trend | {n}/100 | 10% | {n} |

### Follower Audit
- Total: {count} | Active: {n}% | Low quality: {n}% | Suspect bots: {n}%

### Engagement Health
- Avg engagement rate: {rate}%
- Anomalous posts: {count} flagged

### Reciprocity
- Following: {count} | Follow back: {n}% | Non-followers: {count}

### Recommendations
1. {actionable recommendation}
2. {actionable recommendation}
3. {actionable recommendation}

Strategy Guide

Monthly health audit routine

1. Run full MCP workflow above for baseline report
2. Compare against previous month's scores
3. Action items: block flagged bots, unfollow non-reciprocals above threshold
4. Use src/accountHealthMonitor.js for quick between-audit checks

Score interpretation

Score	Grade	Action
80-100	Excellent	Maintain current strategy
60-79	Good	Minor adjustments needed
40-59	Fair	Review engagement strategy, clean follower list
20-39	Poor	Major cleanup needed, block bots, reassess content
0-19	Critical	Possible shadowban, mass bot followers, or inactive account

Improving a low health score

1. Block suspect bot followers with src/blockBots.js
2. Unfollow non-reciprocals with src/unfollowback.js
3. Increase posting consistency to reduce churn
4. Engage authentically to improve engagement rate

Notes

• Health score is a heuristic -- use as directional guidance, not exact measurement
• Bot detection uses profile signals, not ML -- some false positives expected
• Run quarterly for trend tracking, monthly for active management