Communication Analytics for THE_SYNAPSE
Comprehensive analytics and insights into Team Brain communication patterns. Track message volume, response times, most active agents, trends over time, and export detailed reports.
- Summary Statistics - Total messages, reply rates, activity levels
- Agent Analytics - Per-agent stats (sent, received, response rates)
- Timeline Analysis - Message trends over days/weeks
- Priority Tracking - Distribution by priority level
- Communication Matrix - Who messages whom
- Response Time Analysis - Average, fastest, slowest responses
- Export Reports - CSV and JSON export
- Zero Dependencies - Pure Python standard library
- Cross-Platform - Works on Windows, Linux, macOS
# Clone or download
cd AutoProjects/SynapseStats
# Use immediately (no installation required!)
python synapsestats.py summary# Get overall summary
python synapsestats.py summary
# Analyze specific agent
python synapsestats.py agent --agent ATLAS
# View 7-day timeline
python synapsestats.py timeline --days 7
# Export full report
python synapsestats.py export --output report.json# Summary statistics
python synapsestats.py summary
# Agent-specific stats
python synapsestats.py agent --agent FORGE
# Timeline (last N days)
python synapsestats.py timeline --days 14
# Export to CSV
python synapsestats.py export --output stats.csv --format csv
# Export to JSON
python synapsestats.py export --output stats.json --format jsonfrom synapsestats import SynapseStats
# Initialize
stats = SynapseStats()
# Get summary
summary = stats.get_summary()
print(f"Total messages: {summary['total_messages']}")
print(f"Reply rate: {summary['reply_rate']}")
print(f"Most active: {summary['most_active_agent']}")
# Agent stats
atlas_stats = stats.get_agent_stats("ATLAS")
print(f"ATLAS sent: {atlas_stats['messages_sent']}")
print(f"ATLAS response rate: {atlas_stats['response_rate']}")
# Timeline (last 7 days)
timeline = stats.get_timeline(days=7)
for date, count in timeline.items():
print(f"{date}: {count} messages")
# Priority trends
trends = stats.get_priority_trends()
print(f"HIGH priority: {trends['HIGH']:.1f}%")
# Communication matrix
matrix = stats.get_communication_matrix()
print(f"FORGE -> ATLAS: {matrix['FORGE']['ATLAS']} messages")
# Response times
response_times = stats.get_response_times()
print(f"Average response: {response_times['average_minutes']} minutes")
# Export
stats.export_json("full_report.json")
stats.export_csv("summary.csv")Total messages: 221
Reply rate: 45.2%
Most active: FORGE (85 messages)
By sender:
FORGE: 85
ATLAS: 67
CLIO: 42
NEXUS: 27
By priority:
NORMAL: 156
HIGH: 48
CRITICAL: 17
=== STATS FOR ATLAS ===
Sent: 67
Received: 89
Replied to: 42
Response rate: 47.2%
=== MESSAGE TIMELINE (Last 7 days) ===
2026-01-12: 18 messages
2026-01-13: 24 messages
2026-01-14: 31 messages
2026-01-15: 28 messages
2026-01-16: 35 messages
2026-01-17: 42 messages
2026-01-18: 43 messages
{
"FORGE": {
"ATLAS": 25,
"ALL_AGENTS": 15,
"CLIO": 12
},
"ATLAS": {
"FORGE": 18,
"ALL_AGENTS": 8
}
}{
"messages_analyzed": 85,
"average_minutes": "45.3",
"fastest_minutes": "2.1",
"slowest_minutes": "1440.5"
}For Team Leaders:
- Monitor team communication health
- Identify bottlenecks (slow responders)
- Track message volume trends
- Measure collaboration effectiveness
For Individual Agents:
- Track your own activity levels
- See who you communicate with most
- Monitor your response rate
- Compare your activity to team
For System Analysis:
- Identify communication patterns
- Spot unusual activity
- Track priority distribution
- Measure system health
# Get daily message counts for last 30 days
timeline = stats.get_timeline(days=30)
# Identify trends
daily_counts = list(timeline.values())
avg = sum(daily_counts) / len(daily_counts)
print(f"Average daily messages: {avg:.1f}")trends = stats.get_priority_trends()
print("Priority Distribution:")
for priority, percent in sorted(trends.items(), key=lambda x: x[1], reverse=True):
print(f" {priority}: {percent:.1f}%")# Export to JSON for data science tools
stats.export_json("synapse_data.json")
# Export to CSV for Excel/Sheets
stats.export_csv("synapse_report.csv")from synapsestats import SynapseStats
from tokentracker import TokenTracker
stats = SynapseStats()
tracker = TokenTracker()
# Correlate communication with cost
summary = stats.get_summary()
usage = tracker.get_usage_summary("month")
print(f"Messages: {summary['total_messages']}")
print(f"Cost: ${usage['total_cost']:.2f}")
print(f"Cost per message: ${usage['total_cost'] / summary['total_messages']:.4f}")from synapsewatcher import SynapseWatcher
from synapsestats import SynapseStats
# Generate daily report when new message arrives
def generate_daily_report(message):
if "daily report" in message.subject.lower():
stats = SynapseStats()
stats.export_json(f"daily_report_{datetime.now().strftime('%Y%m%d')}.json")
watcher = SynapseWatcher()
watcher.register_callback(generate_daily_report)
watcher.start()- EXAMPLES.md - 10+ working examples
- CHEAT_SHEET.txt - Quick reference
- API Documentation - Full API reference above
Cause: Synapse path incorrect
Fix: Check DEFAULT_SYNAPSE_PATH in code or pass custom path:
stats = SynapseStats(synapse_path=Path("/custom/path"))Cause: Some Synapse messages have invalid JSON
Fix: Tool automatically skips malformed files (logged but not fatal)
Cause: Messages don't have valid timestamps
Fix: Ensure messages follow standard Synapse schema with ISO timestamps
Built by: Atlas (Team Brain)
Requested by: Forge (Q-Mode Tool Requests)
For: Logan Smith / Metaphy LLC
Part of: Beacon HQ / Team Brain Ecosystem
Date: January 18, 2026
Methodology: Test-Break-Optimize (20/20 tests passed)
Built with ❤️ as part of the Team Brain ecosystem - where AI agents collaborate to solve real problems.
MIT License - see LICENSE for details
- GitHub: https://github.com/DonkRonk17/SynapseStats
- Issues: https://github.com/DonkRonk17/SynapseStats/issues
- Team Brain: Part of the AutoProjects collection
SynapseStats - Because communication patterns reveal team health! 📊