Agent skill
phoenix-cli
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, inspect datasets, and query the GraphQL API. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/phoenix-cli
Metadata
Additional technical details for this skill
- author
- arize-ai
- version
- 1.0
SKILL.md
Phoenix CLI
Invocation
px <command> # if installed globally
npx @arizeai/phoenix-cli <command> # no install required
Setup
export PHOENIX_HOST=http://localhost:6006
export PHOENIX_PROJECT=my-project
export PHOENIX_API_KEY=your-api-key # if auth is enabled
Always use --format raw --no-progress when piping to jq.
Traces
px traces --limit 20 --format raw --no-progress | jq .
px traces --last-n-minutes 60 --limit 20 --format raw --no-progress | jq '.[] | select(.status == "ERROR")'
px traces --format raw --no-progress | jq 'sort_by(-.duration) | .[0:5]'
px trace <trace-id> --format raw | jq .
px trace <trace-id> --format raw | jq '.spans[] | select(.status_code != "OK")'
Trace JSON shape
Trace
traceId, status ("OK"|"ERROR"), duration (ms), startTime, endTime
rootSpan — top-level span (parent_id: null)
spans[]
name, span_kind ("LLM"|"CHAIN"|"TOOL"|"RETRIEVER"|"EMBEDDING"|"AGENT")
status_code ("OK"|"ERROR"), parent_id, context.span_id
attributes
input.value, output.value — raw input/output
llm.model_name, llm.provider
llm.token_count.prompt/completion/total
llm.token_count.prompt_details.cache_read
llm.token_count.completion_details.reasoning
llm.input_messages.{N}.message.role/content
llm.output_messages.{N}.message.role/content
llm.invocation_parameters — JSON string (temperature, etc.)
exception.message — set if span errored
Datasets / Experiments / Prompts
px datasets --format raw --no-progress | jq '.[].name'
px dataset <name> --format raw | jq '.examples[] | {input, output: .expected_output}'
px experiments --dataset <name> --format raw --no-progress | jq '.[] | {id, name, failed_run_count}'
px experiment <id> --format raw --no-progress | jq '.[] | select(.error != null) | {input, error}'
px prompts --format raw --no-progress | jq '.[].name'
px prompt <name> --format text --no-progress # plain text, ideal for piping to AI
GraphQL
For ad-hoc queries not covered by the commands above. Output is {"data": {...}}.
px api graphql '{ projectCount datasetCount promptCount evaluatorCount }'
px api graphql '{ projects { edges { node { name traceCount tokenCountTotal } } } }' | jq '.data.projects.edges[].node'
px api graphql '{ datasets { edges { node { name exampleCount experimentCount } } } }' | jq '.data.datasets.edges[].node'
px api graphql '{ evaluators { edges { node { name kind } } } }' | jq '.data.evaluators.edges[].node'
# Introspect any type
px api graphql '{ __type(name: "Project") { fields { name type { name } } } }' | jq '.data.__type.fields[]'
Key root fields: projects, datasets, prompts, evaluators, projectCount, datasetCount, promptCount, evaluatorCount, viewer.
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