The CLI uses singular resource commands with subcommands like list and get:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionphoenix-cliExecute the skills CLI command in your project's root directory to begin installation:
Fetches phoenix-cli from arize-ai/phoenix and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate phoenix-cli. Access via /phoenix-cli in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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px <resource> <action> # if installed globally
npx @arizeai/phoenix-cli <resource> <action> # no install required
The CLI uses singular resource commands with subcommands like list and get:
px trace list
px trace get <trace-id>
px span list
px dataset list
px dataset get <name>
px project list
px annotation-config list
px auth status
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.
px auth status # check connection and authentication
px auth status --endpoint http://other:6006 # check a specific endpoint
px project list # list all projects (table view)
px project list --format raw --no-progress | jq '.[].name' # project names as JSON
px trace list --limit 20 --format raw --no-progress | jq .
px trace list --last-n-minutes 60 --limit 20 --format raw --no-progress | jq '.[] | select(.status == "ERROR")'
px trace list --since 2025-01-15T00:00:00Z --limit 50 --format raw --no-progress | jq .
px trace list --format raw --no-progress | jq 'sort_by(-.duration) | .[0:5]'
px trace get <trace-id> --format raw | jq .
px trace get <trace-id> --format raw | jq '.spans[] | select(.status_code != "OK")'
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"|"RERANKER"|"GUARDRAIL"|"EVALUATOR"|"UNKNOWN")
status_code ("OK"|"ERROR"|"UNSET"), 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
px span list --limit 20 # recent spans (table view)
px span list --last-n-minutes 60 --limit 50 # spans from last hour
px span list --since 2025-01-15T00:00:00Z --limit 50 # spans since a timestamp
px span list --span-kind LLM --limit 10 # only LLM spans
px span list --status-code ERROR --limit 20 # only errored spans
px span list --name chat_completion --limit 10 # filter by span name
px span list --trace-id <id> --format raw --no-progress | jq . # all spans for a trace
px span list --parent-id null --limit 10 # only root spans
px span list --parent-id <span-id> --limit 10 # only children of a span
px span list --include-annotations --limit 10 # include annotation scores
px span list output.json --limit 100 # save to JSON file
px span list --format raw --no-progress | jq '.[] | select(.status_code == "ERROR")'
Span
name, span_kind ("LLM"|"CHAIN"|"TOOL"|"RETRIEVER"|"EMBEDDING"|"AGENT"|"RERANKER"|"GUARDRAIL"|"EVALUATOR"|"UNKNOWN")
status_code ("OK"|"ERROR"|"UNSET"), status_message
context.span_id, context.trace_id, parent_id
start_time, end_time
attributes
input.value, output.value — raw input/output
llm.model_name, llm.provider
llm.token_count.prompt/completion/total
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
annotations[] (with --include-annotations)
name, result { score, label, explanation }
px session list --limit 10 --format raw --no-progress | jq .
px session list --order asc --format raw --no-progress | jq '.[].session_id'
px session get <session-id> --format raw | jq .
px session get <session-id> --include-annotations --format raw | jq '.annotations'
SessionData
id, session_id, project_id
start_time, end_time
traces[]
id, trace_id, start_time, end_time
SessionAnnotation (with --include-annotations)
id, name, annotator_kind ("LLM"|"CODE"|"HUMAN"), session_id
result { label, score, explanation }
metadata, identifier, source, created_at, updated_at
px dataset list --format raw --no-progress | jq '.[].name'
px dataset get <name> --format raw | jq '.examples[] | {input, output: .expected_output}'
px dataset get <name> --split train --format raw | jq . # filter by split
px dataset get <name> --version <version-id> --format raw | jq .
px experiment list --dataset <name> --format raw --no-progress | jq '.[] | {id, name, failed_run_count}'
px experiment get <id> --format raw --no-progress | jq '.[] | select(.error != null) | {input, error}'
px prompt list --format raw --no-progress | jq '.[].name'
px prompt get <name> --format text --no-progress # plain text, ideal for piping to AI
px annotation-config list # list all configs (table view)
px annotation-config list --format raw --no-progress | jq '.[].name' # config names as JSON
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.
Download Phoenix documentation markdown for local use by coding agents.
px docs fetch # fetch default workflow docs to .px/docs
px docs fetch --workflow tracing # fetch only tracing docs
px docs fetch --workflow tracing --workflow evaluation
px docs fetch --dry-run # preview what would be downloaded
px docs fetch --refresh # clear .px/docs and re-download
px docs fetch --output-dir ./my-docs # custom output directory
Key options: --workflow (repeatable, values: tracing, evaluation, datasets, prompts, integrations, sdk, self-hosting, all), --dry-run, --refresh, --output-dir (default .px/docs), --workers (default 10).
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Useful defaults in phoenix-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for phoenix-cli matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend phoenix-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
phoenix-cli reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: phoenix-cli is focused, and the summary matches what you get after install.
We added phoenix-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
phoenix-cli reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: phoenix-cli is focused, and the summary matches what you get after install.
I recommend phoenix-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added phoenix-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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