telemetry

Local-first telemetry collects opt-in usage data on your machine using local JSONL logging. It records anonymized per-skill runs, tracking duration and success without exposing your underlying code or content.

Opt-in telemetry requires explicit user configuration before collecting or sending data. It preserves privacy by storing anonymized usage logs locally first, using a persistent opt-in mechanism to control whether aggregate data is sent to a remote analytics endpoint.

You log per-skill runs locally by enabling the telemetry configuration. The system automatically records execution duration and success status into a local JSONL log file on your machine for later observation.

Yes, you can send anonymized usage analytics to a remote stub endpoint. This optional remote analytics feature is disabled by default and requires configuring the persistent opt-in mechanism to transmit aggregate data.

You check for newer library releases using the cache-backed version-check workflow. This telemetry feature queries for available release updates and surfaces them to the user while maintaining local-first privacy constraints.

Local JSONL logging limitations include requiring manual configuration for opt-in data collection and remote analytics. The remote endpoint is currently a stub, meaning actual external data transmission functionality may be limited or require further implementation.