community-health-monitoring
SKILL.md
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
- Profile baseline -- Call
x_get_profileto get follower count, following count, and calculate follower-to-following ratio. - Audit follower quality -- Call
x_get_followerswithlimit: 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+
- Check engagement authenticity -- Call
x_get_tweetswithlimit: 30. For each tweet, compare engagement volume to follower count. Flag anomalies: likes/follower ratio > 10% (potential engagement pods) or < 0.1% (ghost followers). - Analyze unfollower patterns -- Call
x_detect_unfollowers. Note churn rate and whether unfollowers correlate with specific content types or posting gaps. - Assess reciprocity -- Call
x_get_non_followers. Calculate reciprocity rate:mutual_follows / total_following * 100. Identify high-value accounts not following back. - 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)
- 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
- Run full MCP workflow above for baseline report
- Compare against previous month's scores
- Action items: block flagged bots, unfollow non-reciprocals above threshold
- Use
src/accountHealthMonitor.jsfor 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
- Block suspect bot followers with
src/blockBots.js - Unfollow non-reciprocals with
src/unfollowback.js - Increase posting consistency to reduce churn
- 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
Weekly Installs
4
Repository
nirholas/xactionsGitHub Stars
108
First Seen
Feb 28, 2026
Security Audits
Installed on
openclaw4
gemini-cli4
github-copilot4
codex4
kimi-cli4
cursor4