Chat Vitals

Chat Vitals - Monitor chat conversation health with real-time insights. Tracks conversation quality metrics: first-try success rate, promise fulfillment, token efficiency, and detects inefficiencies like rework and plan inflation. Features: - Auto-collect: Zero-friction conversation tracking - Real-time dashboard: Live health monitoring with visual indicators - Health scoring: 4-tier system (🟢🟡🟠🔴) - Actionable reports: Optimization suggestions based on data Keywords: chat monitoring, LLM health, conversation quality, token efficiency, AI performance, real-time dashboard, vitals

📊 Chat Vitals v1.1.0

Monitor your AI conversation health like a doctor checks vital signs. Auto-collection + Real-time dashboard + Actionable insights.

✨ What's New in v1.1.0

🚀 Auto-Collection (Zero Friction)

Before: Manual tracking for every turn
Now: One command, automatic tracking

bash

vitals start claude-sonnet-4.6
# All conversations tracked automatically!

🖥️ Real-Time Dashboard

Live terminal UI with health monitoring:

bash

vitals dashboard

Shows:

• ⚡ Live health score (0-100) with color coding
• 📊 Real-time metrics refresh
• 🚨 Instant alerts for problems
• 📈 Token usage tracking

🚀 Quick Start

bash

# 1. Start monitoring (one command!)
vitals start claude-sonnet-4.6

# 2. View real-time dashboard (in another terminal)
vitals dashboard

# 3. Check status anytime
vitals status

# 4. Generate report when done
vitals report

📋 All Commands

Command	Description
vitals start [model]	Start auto-monitoring session
vitals dashboard	Launch real-time dashboard
vitals status	Show current session status
vitals summary	Quick session summary
vitals complete	Mark session as complete
vitals report	Generate detailed report

🎯 Core Metrics

Metric	Description	Healthy	Warning	Danger
First-Try Success	% tasks without rework	≥70%	50-70%	<50%
Rework Count	Corrections per task	0	1-2	>2
Promise Fulfillment	% promises delivered	≥80%	60-80%	<60%
Plan Inflation	Actual / Promised turns	≤1.3x	1.3-2.0x	>2.0x
Token Efficiency	Value per token	≥0.15	0.08-0.15	<0.08

🏥 Health Status

Score	Status	Emoji	Color
85-100	Excellent	🟢	Green
70-84	Good	🟡	Yellow
50-69	Warning	🟠	Orange
<50	Critical	🔴	Red

📊 Sample Output

text

📊 Chat Vitals Dashboard
──────────────────────────────────────────────────

Session: a1b2c3d4
Model:   claude-sonnet-4.6
Started: 2026-04-08T11:34:27

Health Score:
🟢 85/100 - Excellent
[██████████████████████████████] 85%

Key Metrics:
──────────────────────────────────────────────────
  ✅ First-Try Success: 85%
  ✅ Rework Count: 0
  ⚠️  Promise Fulfillment: 75%
  ✅ Plan Inflation: 1.1x
  ✅ Token Efficiency: 0.22

Session Stats:
  💬 Total Turns: 3
  🔢 Total Tokens: 1,030
  📊 Avg Tokens/Turn: 343

⚙️ Configuration

Edit ~/.openclaw/skills/chat-vitals/config.json:

json

{
  "monitor": {
    "token_thresholds": {
      "report_daily": 50000
    },
    "health_thresholds": {
      "first_try_success_rate": {
        "excellent": 85,
        "good": 70,
        "warning": 50
      }
    }
  },
  "patterns": {
    "correction_keywords": ["不对", "错了", "重新"],
    "promise_patterns": ["接下来我会", "首先让我"]
  }
}

📁 Project Structure

text

chat-vitals/
├── vitals                  # ⭐ Simple CLI entry point
├── SKILL.md
├── README.md
├── config.json
├── scripts/
│   ├── collector.py        # Core data collection
│   ├── analyzer.py         # Metric analysis
│   ├── reporter.py         # Report generation
│   ├── auto_collector.py   # ⭐ Auto-collection
│   └── dashboard.py        # ⭐ Real-time dashboard
└── tests/
    └── test_vitals.py      # Test suite

🧪 Testing

bash

cd ~/.openclaw/skills/chat-vitals
./tests/run_tests.sh

# Test new features
vitals start test-model
vitals status
vitals dashboard  # In a real terminal

🔌 Advanced Usage

Legacy Manual Mode

bash

python3 scripts/collector.py create ...
python3 scripts/collector.py record ...
python3 scripts/collector.py complete ...

Programmatic Access

python

from llmchat_vitals.scripts import auto_collector

# Start monitoring
auto_collector.auto_start_session("gpt-4")

# Record turn
auto_collector.auto_record_turn(user_input, model_output)

# Get live summary
summary = auto_collector.get_session_summary()
print(f"Health: {summary['health_score']}/100")

🛣️ Roadmap

• Auto-collection (v1.1.0)
• Real-time dashboard (v1.1.0)
• Simplified CLI (v1.1.0)
• Webhook integrations (Feishu/Slack)
• Intent drift detection
• Web dashboard
• Multi-model comparison
• Prompt optimization suggestions

🤝 Contributing

Issues and PRs welcome!

📄 License

MIT License