Arize Instrumentation Health Skill

Use this skill for an on-demand instrumentation health audit over a project's existing traces — the aggregate counterpart to arize-instrumentation (which verifies a single new trace) and arize-trace (which exports and inspects spans). It answers questions like:

• "Why do my traces look empty or flat?"
• "Check whether my Arize instrumentation is healthy."
• "Find instrumentation issues in this project."
• "Why are my evals / token / cost dashboards showing n/a or zero?"

Workflow

1. Resolve scope — get the project (and space, if needed). If ambiguous, ask; do not guess.
2. Export a bounded span sample using the arize-trace skill — do not hand-roll ax flags here. Follow its export guidance: start with a small sample scoped by --start-time to a recent window, into --output-dir .arize-tmp-traces. Pull ~20 traces' worth of spans for a full audit (see minimum-data rules below).
3. Group spans by trace (context.trace_id); within each trace identify the root (parent_id/parent_span_id is null).
4. Run the deterministic checks in references/checks.md against the sample.
5. Report findings ranked by severity then confidence, using the Output format in references/checks.md.

This skill is read-only by default. Inspect exported spans and source files only when they help attribute the cause. Do not edit application code, tests, configuration, dependencies, or generated artifacts during a health audit unless the user explicitly asks this skill to make fixes in the same turn. When fixes are needed and the user has not asked for them in this turn, report the next action as a handoff to arize-instrumentation or the relevant framework-specific instrumentation path.