opentelemetry-llm
OpenTelemetry instrumentation for LLM applications with distributed tracing
npx skills add a5c-ai/babysitter --skill opentelemetry-llm --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# OpenTelemetry LLM Skill ## Capabilities - Configure OpenTelemetry SDK for LLM apps - Implement LLM-specific instrumentation - Set up trace exporters (Jaeger, OTLP) - Design semantic conventions for LLM - Configure span attributes for AI workloads - Implement context propagation ## Target Processes - llm-observability-monitoring - agent-deployment-pipeline ## Implementation Details ### Core Components 1. **TracerProvider**: SDK configuration 2. **SpanProcessor**: Batch/simple processors 3. **Exporters**: Jaeger, OTLP, Console 4. **Instrumentation**: Auto and manual ### LLM Semantic Conventions - gen_ai.system (OpenAI, Anthropic) - gen_ai.request.model - gen_ai.request.max_tokens - gen_ai.response.finish_reason - gen_ai.usage.prompt_tokens ### Configuration Options - Exporter selection - Sampling strategies - Resource attributes - Span limits - Context propagation ### Best Practices - Consistent attribute naming - Appropriate sampling - Error handling traces - Propagate context across services ### Dependencies - opentelemetry-sdk - opentelemetry-exporter-* - openinference (optional)
- Capabilities
- Target Processes
- Implementation Details
- Core Components
- LLM Semantic Conventions
- Configuration Options
- Best Practices
- Dependencies
What does the opentelemetry-llm skill do?
OpenTelemetry instrumentation for LLM applications with distributed tracing
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill opentelemetry-llm --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.