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experiments

An experiment is one run of a prompt or pipeline over every example in a dataset.

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phoenix
11k7 skills7 agents
Install
$ npx -y skills add arize-ai/phoenix --agent claude-code

How it fires

How this agent gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.

Context preview

The summary Claude sees to decide when to auto-load this agent.

An experiment is one run of a prompt or pipeline over every example in a dataset.

Agent definition

experiments.md

Experiment, ExperimentRun

An experiment is one run of a prompt or pipeline over every example in a dataset.

Reaching an experiment

There is **no `getExperimentById`** — reach an `Experiment` via `node(id:)`, `Dataset.experiments`, or `compareExperiments`.

Experiment fields

  • `name`, `description`, `sequenceNumber`, `repetitions`, `isEphemeral`
  • `dataset`, `datasetVersion`, `project`
  • `runs(first, after, sort: ExperimentRunSort)` — **forward-only** (no `last`/`before`)
  • `runCount`, `expectedRunCount`
  • `errorRate`, `averageRunLatencyMs`, `costSummary`, `costDetailSummaryEntries`
  • `annotationSummaries { annotationName meanScore minScore maxScore count errorCount }`

Comparison

For candidate comparison prefer `compareExperiments(baseExperimentId: GlobalID!, compareExperimentIds: [GlobalID!]!, first, after, filterCondition)` over fetching each experiment's runs separately. Related: `experimentRunMetricComparisons(baseExperimentId, compareExperimentIds)` and `validateExperimentRunFilterCondition(condition, experimentIds)`.

Example

query ExperimentMetrics($id: ID!) {
  node(id: $id) {
    ... on Experiment {
      name
      sequenceNumber
      runCount
      errorRate
      averageRunLatencyMs
      annotationSummaries { annotationName meanScore count errorCount }
    }
  }
}
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