Does it log sensitive prompt data?

By default, this skill is configured for full logging, including user prompts, to provide maximum visibility, but these settings can be modified in the generated configuration file.

Can I use this with cloud providers like Grafana Cloud or Datadog?

Yes, the 'enable-custom' operation allows you to specify any OTLP-compliant endpoint, protocol, and authentication headers.

Does this skill support local monitoring stacks?

Yes, it includes a dedicated command to configure Claude Code for local LGTM stacks (Loki, Grafana, Tempo, and Mimir) running via Docker.

What specific metrics are tracked?

The skill tracks session counts, token usage (input/output), USD costs, tool success/rejection rates, and active engagement duration.

How do I confirm telemetry is being sent correctly?

The skill includes a 'verify' operation that checks your configuration, tests endpoint reachability, and sends a test span to your backend.

Claude Code Telemetry Enabler

Name: Claude Code Telemetry Enabler
Author: adaptationio

byadaptationio

분석 및 모니터링

Configures and enables OpenTelemetry monitoring for Claude Code to track metrics, logs, and traces in real-time.

소개

This skill streamlines the setup of OpenTelemetry (OTEL) for Claude Code, providing developers with deep visibility into their AI-driven workflows. By automating the configuration of environment variables for local LGTM stacks, Railway deployments, or custom OTLP endpoints, it enables the collection of critical data including token usage, API costs, full prompt history, and tool execution traces. It is an essential utility for teams and individuals who need to monitor performance, audit AI behavior, and debug complex agentic interactions within centralized observability platforms like Grafana.

주요 기능

Full logging of user prompts and tool results for detailed auditing
Distributed tracing for session flows and tool execution spans
Automated configuration for local LGTM and Railway observability stacks
Comprehensive capture of token usage, API costs, and session metrics
Built-in verification tools to test connectivity and data flow
0 GitHub stars

사용 사례

Monitoring AI infrastructure costs and token consumption trends
Auditing AI interactions for compliance and quality assurance
Debugging complex tool-use failures and performance bottlenecks

소개

주요 기능

Full logging of user prompts and tool results for detailed auditing
Distributed tracing for session flows and tool execution spans
Automated configuration for local LGTM and Railway observability stacks
Comprehensive capture of token usage, API costs, and session metrics
Built-in verification tools to test connectivity and data flow
0 GitHub stars

사용 사례

Monitoring AI infrastructure costs and token consumption trends
Auditing AI interactions for compliance and quality assurance
Debugging complex tool-use failures and performance bottlenecks