OpenTelemetry LLM Skill
SkillMonitoring & opsOpenTelemetry instrumentation for LLM applications with distributed tracing
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the OpenTelemetry LLM Skill skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/ai-agents-conversational/skills/opentelemetry-llm/SKILL.md and read by ahel’s review.
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
- TracerProvider: SDK configuration
- SpanProcessor: Batch/simple processors
- Exporters: Jaeger, OTLP, Console
- 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)
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
opentelemetry-llm- Source
- github.com/a5c-ai/babysitter
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