2664 Event Types 6e22a836

Analytics Event Types Reference

Complete documentation of all event types emitted by the Claude Code Analytics Daemon.

Event Structure

All events share a common base structure:

interface BaseEvent {
  type: string;           // Event type identifier
  timestamp: number;      // Unix timestamp (milliseconds)
  conversationId: string; // Unique conversation identifier
}

Event Types

1. Plugin Activation

Type: plugin.activation

Description: Emitted when a plugin is activated or installed in a conversation.

Fields:

• pluginName (string): Plugin identifier (e.g., "terraform-specialist")
• pluginVersion (string, optional): Semantic version (e.g., "1.2.0")
• marketplace (string, optional): Marketplace slug (e.g., "claude-code-plugins-plus")

Example:

{
  "type": "plugin.activation",
  "timestamp": 1703347200000,
  "conversationId": "abc123",
  "pluginName": "terraform-specialist",
  "pluginVersion": "1.0.0",
  "marketplace": "claude-code-plugins-plus"
}

Use Cases:

• Track most popular plugins
• Measure plugin adoption rates
• Identify plugin combinations
• Monitor marketplace distribution

2. Skill Trigger

Type: skill.trigger

Description: Emitted when an Agent Skill is activated based on conversation context.

Fields:

• skillName (string): Skill identifier (e.g., "terraform-plan-analyzer")
• pluginName (string): Parent plugin name
• triggerPhrase (string, optional): User phrase that triggered the skill

Example:

{
  "type": "skill.trigger",
  "timestamp": 1703347200000,
  "conversationId": "abc123",
  "skillName": "terraform-plan-analyzer",
  "pluginName": "terraform-specialist",
  "triggerPhrase": "analyze this terraform plan"
}

Use Cases:

• Measure skill effectiveness
• Identify popular skills
• Optimize skill trigger phrases
• Track skill usage patterns

3. LLM Call

Type: llm.call

Description: Emitted when Claude API is invoked during conversation.

Fields:

• model (string): Model identifier (e.g., "claude-sonnet-4-5")
• inputTokens (number, optional): Input token count
• outputTokens (number, optional): Output token count
• totalTokens (number, optional): Total token count

Example:

{
  "type": "llm.call",
  "timestamp": 1703347200000,
  "conversationId": "abc123",
  "model": "claude-sonnet-4-5",
  "inputTokens": 1500,
  "outputTokens": 800,
  "totalTokens": 2300
}

Use Cases:

• Monitor API usage
• Calculate cost estimates
• Track token consumption trends
• Optimize prompt efficiency

4. Cost Update

Type: cost.update

Description: Emitted when API costs are calculated for a conversation.

Fields:

• model (string): Model identifier
• inputCost (number): Cost for input tokens
• outputCost (number): Cost for output tokens
• totalCost (number): Total cost for the call
• currency (string): Currency code (e.g., "USD")

Example:

{
  "type": "cost.update",
  "timestamp": 1703347200000,
  "conversationId": "abc123",
  "model": "claude-sonnet-4-5",
  "inputCost": 0.0045,
  "outputCost": 0.012,
  "totalCost": 0.0165,
  "currency": "USD"
}

Use Cases:

• Track spending per conversation
• Calculate ROI for automation
• Budget forecasting
• Cost optimization insights

5. Rate Limit Warning

Type: rate_limit.warning

Description: Emitted when approaching or hitting API rate limits.

Fields:

• service (string): Service identifier (e.g., "anthropic-api")
• limit (number): Rate limit ceiling
• current (number): Current usage count
• resetAt (number, optional): Unix timestamp when limit resets

Example:

{
  "type": "rate_limit.warning",
  "timestamp": 1703347200000,
  "conversationId": "abc123",
  "service": "anthropic-api",
  "limit": 1000,
  "current": 950,
  "resetAt": 1703350800000
}

Use Cases:

• Prevent rate limit errors
• Throttle requests proactively
• Plan API capacity
• Alert on high usage

6. Conversation Created

Type: conversation.created

Description: Emitted when a new conversation file is detected.

Fields:

• title (string, optional): Conversation title

Example:

{
  "type": "conversation.created",
  "timestamp": 1703347200000,
  "conversationId": "abc123",
  "title": "Deploy infrastructure with Terraform"
}

Use Cases:

• Track conversation volume
• Measure user engagement
• Identify peak usage times
• Analyze conversation topics

7. Conversation Updated

Type: conversation.updated

Description: Emitted when a conversation file is modified (new messages added).

Fields:

• messageCount (number): Total number of messages in conversation

Example:

{
  "type": "conversation.updated",
  "timestamp": 1703347200000,
  "conversationId": "abc123",
  "messageCount": 12
}

Use Cases:

• Monitor conversation length
• Detect long-running sessions
• Measure conversation depth
• Track engagement patterns

Event Flow Example

Typical event sequence for a plugin-powered conversation:

1. conversation.created
   ↓
2. plugin.activation (terraform-specialist)
   ↓
3. llm.call (initial message)
   ↓
4. cost.update ($0.0025)
   ↓
5. conversation.updated (messageCount: 2)
   ↓
6. skill.trigger (terraform-plan-analyzer)
   ↓
7. llm.call (skill response)
   ↓
8. cost.update ($0.0045)
   ↓
9. conversation.updated (messageCount: 4)

WebSocket Connection

Endpoint: ws://localhost:3456 (default)

Configuration:

• CCP_ANALYTICS_PORT - Custom port (default: 3456)
• CCP_ANALYTICS_HOST - Custom host (default: localhost)

Connection Example:

const ws = new WebSocket('ws://localhost:3456');

ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  console.log(`[${data.type}]`, data);
};

TypeScript Definitions

See src/types.ts for complete TypeScript definitions.

Dashboard Integration

Events are designed for real-time dashboard consumption:

1. Usage Analytics: plugin.activation, skill.trigger
2. Cost Tracking: cost.update, llm.call
3. Rate Limiting: rate_limit.warning
4. Engagement Metrics: conversation.created, conversation.updated

Privacy & Security

• All events are local only - no data leaves your machine
• Conversation content is never transmitted (only metadata)
• WebSocket server is unauthenticated (localhost binding only)
• Production deployments should add authentication layer