Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dominodatalab/domino-claude-plugin --skill genai-tracinggit clone --depth 1 https://github.com/dominodatalab/domino-claude-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/genai-tracing)<a href="https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/genai-tracing"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/genai-tracing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/genai-tracing"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/genai-tracing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00102 | $0.01301 |
| Opus 5 | $0.00051 | $0.00651 |
| Sonnet 5 | $0.00020 | $0.00260 |
| Haiku 4.5 | $0.00010 | $0.00130 |
Grade A, and why
domino-genai-tracing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domino 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
What GenAI Tracing Captures
The Domino SDK automatically captures:
- Token usage - Input and output tokens per call
- Latency - Time for each operation
- Cost - Estimated cost per call
- Tool calls - Function/tool invocations
- Errors - Exceptions and failure modes
- Model parameters - Temperature, max_tokens, etc.
Core Components
@add_tracingdecorator - Wraps agent functions to capture traces (works standalone — noDominoRunrequired)mlflow.start_span()- Creates child spans for LLM calls and tool executions inside the agent loopDominoRuncontext manager - Groups traces into runs for development/evaluation (Experiments UI only)- Evaluators - Custom functions to score outputs
- MLflow integration - View traces in Experiment Manager or App Performance tab
Related Documentation
- TRACING-SETUP.md - Environment & SDK setup
- ADD-TRACING-DECORATOR.md - @add_tracing usage, span_type, autolog_frameworks
- DOMINO-RUN.md - DominoRun context manager (development/evaluation only)
- EVALUATORS.md - LLM-as-judge, custom evaluators
- MULTI-AGENT-EXAMPLE.md - Complete multi-agent example
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 147 lines · 102 tokens per session scan A 3ad50c4f417e
domino-genai-tracing is a skill published in the GitHub repository dominodatalab/domino-claude-plugin (6 stars, last pushed 2mo ago), licensed MIT. It adds 102 tokens to every session and 1,301 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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