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.
npx skills add majiayu000/claude-skill-registry --skill phoenix-cli-finimo-solutions-research --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 ## Invocation ```bash px <command> # if installed globally npx @arizeai/phoenix-cli <command> # no install required ``` ## Setup ```bash 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 ```bash 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_deta
- Invocation
- Setup
- Traces
- Trace JSON shape
- Datasets / Experiments / Prompts
- GraphQL
px <command> # if installed globally npx @arizeai/phoenix-cli <command> # no install required export PHOENIX_HOST=http://localhost:6006 export PHOENIX_PROJECT=my-project export PHOENIX_API_KEY=your-api-key # if auth is enabled 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")'
What does the phoenix-cli skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill phoenix-cli-finimo-solutions-research --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.