phoenix-cli
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, and inspect datasets. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
npx skills add majiayu000/claude-skill-registry --skill phoenix-cli --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Phoenix CLI Debug and analyze LLM applications using the Phoenix CLI (`px`). ## Quick Start ### Installation ```bash npm install -g @arizeai/phoenix-cli # Or run directly with npx npx @arizeai/phoenix-cli ``` ### Configuration Set environment variables before running commands: ```bash export PHOENIX_HOST=http://localhost:6006 export PHOENIX_PROJECT=my-project export PHOENIX_API_KEY=your-api-key # if authentication is enabled ``` CLI flags override environment variables when specified. ## Debugging Workflows ### Debug a failing LLM application 1. Fetch recent traces to see what's happening: ```bash px traces --limit 10 ``` 2. Find failed traces: ```bash px traces --limit 50 --format raw --no-progress | jq '.[] | select(.status == "ERROR")' ``` 3. Get details on a specific trace: ```bash px trace <trace-id> ``` 4. Look for errors in spans: ```bash px trace <trace-id> --format raw | jq '.spans[] | select(.status_code != "OK")' ``` ### Find performance issues 1. Get the slowest traces: ```bash px traces --limit 20 --format raw --no-progress | jq 'sort_by(-.duration) | .[0:5]' ``` 2. Analyze span durations within a trace: ```bash px trace <trace-id> --format raw | jq '.spans | sort_by
- Quick Start
- Installation
- Configuration
- Debugging Workflows
- Debug a failing LLM application
- Find performance issues
- Analyze LLM usage
- Review experiment results
- Command Reference
- px traces
- px trace
- px datasets
- px dataset
- px experiments
npm install -g @arizeai/phoenix-cli Or run directly with npx npx @arizeai/phoenix-cli export PHOENIX_HOST=http://localhost:6006 export PHOENIX_PROJECT=my-project export PHOENIX_API_KEY=your-api-key # if authentication is enabled px traces --limit 10 px traces --limit 50 --format raw --no-progress | jq '.[] | select(.status == "ERROR")' px trace <trace-id> px trace <trace-id> --format raw | jq '.spans[] | select(.status_code != "OK")'
What does the phoenix-cli skill do?
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, and inspect datasets. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill phoenix-cli --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.