phoenix-cli

arize-ai/phoenix · updated Apr 8, 2026

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$npx skills add https://github.com/arize-ai/phoenix --skill phoenix-cli
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summary

The CLI uses singular resource commands with subcommands like list and get:

skill.md

Phoenix CLI

Invocation

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

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.

Auth

px auth status                                # check connection and authentication
px auth status --endpoint http://other:6006   # check a specific endpoint

Projects

px project list                                            # list all projects (table view)
px project list --format raw --no-progress | jq '.[].name' # project names as JSON

Traces

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 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"|"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

Spans

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 JSON shape

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 }

Sessions

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'

Session JSON shape

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

Datasets / Experiments / Prompts

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

Annotation Configs

px annotation-config list                                           # list all configs (table view)
px annotation-config list --format raw --no-progress | jq '.[].name' # config names as JSON

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.

Docs

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).

how to use phoenix-cli

How to use phoenix-cli on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add phoenix-cli
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/arize-ai/phoenix --skill phoenix-cli

The skills CLI fetches phoenix-cli from GitHub repository arize-ai/phoenix and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/phoenix-cli

Reload or restart Cursor to activate phoenix-cli. Access the skill through slash commands (e.g., /phoenix-cli) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.728 reviews
  • Kwame Huang· Dec 20, 2024

    Useful defaults in phoenix-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Shikha Mishra· Dec 12, 2024

    Registry listing for phoenix-cli matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Henry Perez· Nov 11, 2024

    I recommend phoenix-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • William Wang· Oct 2, 2024

    phoenix-cli reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aarav Jackson· Sep 25, 2024

    Solid pick for teams standardizing on skills: phoenix-cli is focused, and the summary matches what you get after install.

  • Soo Choi· Sep 9, 2024

    We added phoenix-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Rahul Santra· Sep 5, 2024

    phoenix-cli reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Charlotte Garcia· Aug 28, 2024

    Solid pick for teams standardizing on skills: phoenix-cli is focused, and the summary matches what you get after install.

  • Pratham Ware· Aug 24, 2024

    I recommend phoenix-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mia Chen· Aug 16, 2024

    We added phoenix-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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