domino-genai-tracing
SkillTrace and evaluate GenAI applications including LLM calls, agents, RAG pipelines, and multi-step AI systems in Domino. Uses the Domino SDK (@add_tracing decorator, DominoRun context) with MLflow 3.2.0. Captures token usage, latency, cost, tool calls, and errors. Supports LLM-as-judge evaluators and
Install
git clone https://github.com/dominodatalab/domino-claude-plugin.git ~/.claude/skills/domino-genai-tracingWhat is domino-genai-tracing?
Trace and evaluate GenAI applications including LLM calls, agents, RAG pipelines, and multi-step AI systems in Domino. Uses the Domino SDK (@add_tracing decorator, DominoRun context) with MLflow 3.2.0. Captures token usage, latency, cost, tool calls, and errors. Supports LLM-as-judge evaluators and custom metrics. Use when building agents, debugging LLM applications, or needing audit trails for GenAI systems.
What this can do
Capabilities declared in this component's own frontmatter — not inferred.
Inherit all session tools
Declares no tool restrictions — inherits every session tool
~103 tokens of context used while enabled, before you invoke anything
Documentation
README · ~3 min readDomino GenAI Tracing Skill
This skill provides comprehensive knowledge for tracing and evaluating GenAI applications in Domino Data Lab, including LLM calls, agents, RAG pipelines, and multi-step AI systems.
Two Deployment Modes
GenAI tracing works differently depending on where your code runs:
| Mode | Where traces appear | When to use |
|---|---|---|
| Deployed App (Production) | App Performance tab | FastAPI/Flask apps deployed as Domino Apps |
| Development / Evaluation | Experiments UI | Batch scripts, Domino Jobs, Workspaces |
Critical difference: In a deployed Domino App, Domino auto-creates an experiment named agent_experiment_{app_id} and the Performance tab reads from it. If you call mlflow.set_experiment() or wrap calls in DominoRun(), traces go to your custom experiment instead — and the Performance tab won't see them.
Key Concepts
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