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vscode/.github/instructions/telemetry.instructions.md
2025-08-27 21:32:05 +00:00

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---
description: Use when asked to work on telemetry events
---
Patterns for GDPR-compliant telemetry in VS Code with proper type safety and privacy protection.
## Implementation Pattern
### 1. Define Types
```typescript
type MyFeatureEvent = {
action: string;
duration: number;
success: boolean;
errorCode?: string;
};
type MyFeatureClassification = {
action: { classification: 'SystemMetaData'; purpose: 'FeatureInsight'; comment: 'The action performed.' };
duration: { classification: 'SystemMetaData'; purpose: 'PerformanceAndHealth'; isMeasurement: true; comment: 'Time in milliseconds.' };
success: { classification: 'SystemMetaData'; purpose: 'FeatureInsight'; isMeasurement: true; comment: 'Whether action succeeded.' };
errorCode: { classification: 'SystemMetaData'; purpose: 'PerformanceAndHealth'; comment: 'Error code if action failed.' };
owner: 'yourGitHubUsername';
comment: 'Tracks MyFeature usage and performance.';
};
```
### 2.1. Send Event
```typescript
this.telemetryService.publicLog2<MyFeatureEvent, MyFeatureClassification>('myFeatureAction', {
action: 'buttonClick',
duration: 150,
success: true
});
```
### 2.2. Error Events
For error-specific telemetry with stack traces or error messages:
```typescript
type MyErrorEvent = {
operation: string;
errorMessage: string;
duration?: number;
};
type MyErrorClassification = {
operation: { classification: 'SystemMetaData'; purpose: 'PerformanceAndHealth'; comment: 'The operation that failed.' };
errorMessage: { classification: 'CallstackOrException'; purpose: 'PerformanceAndHealth'; comment: 'The error message.' };
duration: { classification: 'SystemMetaData'; purpose: 'PerformanceAndHealth'; isMeasurement: true; comment: 'Time until failure.' };
owner: 'yourGitHubUsername';
comment: 'Tracks MyFeature errors for reliability.';
};
this.telemetryService.publicLogError2<MyErrorEvent, MyErrorClassification>('myFeatureError', {
operation: 'fileRead',
errorMessage: error.message,
duration: 1200
});
```
### 3. Service Injection
```typescript
constructor(
@ITelemetryService private readonly telemetryService: ITelemetryService,
) { super(); }
```
## GDPR Classifications & Purposes
**Classifications (choose the most restrictive):**
- `SystemMetaData` - **Most common.** Non-personal system info, user preferences, feature usage, identifiers (extension IDs, language types, counts, durations, success flags)
- `CallstackOrException` - Error messages, stack traces, exception details. **Only for actual error information.**
- `PublicNonPersonalData` - Data already publicly available (rare)
**Purposes (combine with different classifications):**
- `FeatureInsight` - **Default.** Understanding how features are used, user behavior patterns, feature adoption
- `PerformanceAndHealth` - **For errors & performance.** Metrics, error rates, performance measurements, diagnostics
**Required Properties:**
- `comment` - Clear explanation of what the field contains and why it's collected
- `owner` - GitHub username (infer from branch or ask)
- `isMeasurement: true` - **Required** for all numeric values flags used in calculations
## Error Events
Use `publicLogError2` for errors with `CallstackOrException` classification:
```typescript
this.telemetryService.publicLogError2<ErrorEvent, ErrorClassification>('myFeatureError', {
errorMessage: error.message,
errorCode: 'MYFEATURE_001',
context: 'initialization'
});
```
## Naming & Privacy Rules
**Naming Conventions:**
- Event names: `camelCase` with context (`extensionActivationError`, `chatMessageSent`)
- Property names: specific and descriptive (`agentId` not `id`, `durationMs` not `duration`)
- Common patterns: `success/hasError/isEnabled`, `sessionId/extensionId`, `type/kind/source`
**Critical Don'ts:**
- ❌ No PII (usernames, emails, file paths, content)
- ❌ Missing `owner` field in classification (infer from branch name or ask user)
- ❌ Vague comments ("user data" → "selected language identifier")
- ❌ Wrong classification
- ❌ Missing `isMeasurement` on numeric metrics
**Privacy Requirements:**
- Minimize data collection to essential insights only
- Use hashes/categories instead of raw values when possible
- Document clear purpose for each data point